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The spatial organization of the cell depends upon intracellular trafficking of cargos hauled along microtubules and actin filaments by the molecular motor proteins kinesin , dynein , and myosin . Although much is known about how single motors function , there is significant evidence that cargos in vivo are carried by multiple motors . While some aspects of multiple motor function have received attention , how the cargo itself —and motor organization on the cargo—affects transport has not been considered . To address this , we have developed a three-dimensional Monte Carlo simulation of motors transporting a spherical cargo , subject to thermal fluctuations that produce both rotational and translational diffusion . We found that these fluctuations could exert a load on the motor ( s ) , significantly decreasing the mean travel distance and velocity of large cargos , especially at large viscosities . In addition , the presence of the cargo could dramatically help the motor to bind productively to the microtubule: the relatively slow translational and rotational diffusion of moderately sized cargos gave the motors ample opportunity to bind to a microtubule before the motor/cargo ensemble diffuses out of range of that microtubule . For rapidly diffusing cargos , the probability of their binding to a microtubule was high if there were nearby microtubules that they could easily reach by translational diffusion . Our simulations found that one reason why motors may be approximately 100 nm long is to improve their ‘on’ rates when attached to comparably sized cargos . Finally , our results suggested that to efficiently regulate the number of active motors , motors should be clustered together rather than spread randomly over the surface of the cargo . While our simulation uses the specific parameters for kinesin , these effects result from generic properties of the motors , cargos , and filaments , so they should apply to other motors as well .
Cells are highly organized , and much of this organization results from motors that move cargos along microtubules . The single-molecule properties of molecular motors are relatively well understood both experimentally and theoretically . With this as a starting point , we investigated how the presence of the cargo itself alters transport . Aside from exerting viscous drag , the cargo could in principle alter single-motor based transport both by changing the motors' diffusion and ability to contact the filament ( a free motor diffuses very differently from a cargo-bound one ) , and also by exposing the motor to the random forces resulting from thermal fluctuations of the cargo which depend on the size of the cargo and the viscosity of the environment . Whether such effects are significant are investigated here . Recent studies show that cargos in vivo are frequently moved by more than one microtubule-based motor [1] , [2] , [3] , [4] . This raises the question of how multiple motors function together , the subject of recent theoretical and experimental work [1] , [5] , [6] , [7] . In vitro , when more than one motor is actively hauling a cargo , the run length , i . e . , the distance that the cargo travels along the microtubule before detaching , increases with the number of active motors . However , the presence of the cargo itself may be important when there are multiple motors . In addition to possibly changing the single-molecule's function , the cargo's size may alter the relationship between the total number of motors present and the number of motors actively engaged in transporting the cargo ( assuming random motor organization on the cargo's surface ) . If motors are not randomly organized , details of this organization will also be important . How each of these factors contributes to overall transport is unknown . To approach these problems requires a new theoretical framework: past studies simplified the problem using essentially one-dimensional models [5] , [6] , [8] , [9] that had the motors attached to the cargo at a single point , with the cargo represented by a single point ( though potentially experiencing viscous drag proportional to a specific diameter ) . Here we have developed a bone-fide three dimensional Monte Carlo simulation that allows us to directly investigate how the presence of the cargo itself affects single-motor driven transport and motor-microtubule attachment , as well as how the relationship between cargo size and the arrangement of motors on the cargo affects ultimate cargo motion , all within the context of a cargo experiencing random Brownian translational and rotational motion . The attachment of motors to a cargo of finite size , rather than an idealized point mass , has a number of ramifications . First , the function of the motor ( s ) might be altered by the translational and rotational diffusion of the cargo; the larger the cargo , the more effect it has on the motors' diffusion , and thus , potentially , on the motors' ability to contact/interact with a microtubule . Second , when a motor is attached to both the microtubule and the cargo , it will feel instantaneous forces due to the cargo's thermal motion . These forces will depend on the cargo's size; and the random thermal ‘tugs’ from the cargo could slow the rate of travel of a motor and , in principle , induce the motor to detach from the filament . Third , there is a relationship between the cargo size , the total number of motors present , how they are arranged , and how many can be engaged . To illustrate this , imagine one cargo that is 50 nm in diameter , and another that is 500 nm in diameter . In the first case , even if the motors are randomly distributed on the cargo , because the length of an individual motor is more than 100 nm , all of those on the lower half of the cargo , and some on the upper half , will be able to reach a nearby microtubule ( Figure 1A ) . In contrast for the 500 nm cargo , most motors will be unable to reach if they are randomly distributed on the cargo ( Figure 1B ) . However , if all the motors were clumped at a single point , the size of the cargo essentially becomes irrelevant , because if one motor can reach , they all can ( Figure 1C ) . We thus set out to answer the following questions: We organized the presentation of our results according to these questions .
To address these questions , we developed three-dimensional Monte Carlo simulations . Generally speaking , Monte Carlo is an approach to computer simulations in which an event A occurs with a certain probability PA where 0≤PA≤1 . In practice , during each time step , a random number x is generated with uniform probability between 0 and 1 . If x≤PA , event A occurs; if x>PA , event A does not occur . Our simulations were carried out as follows . We started with a three dimensional spherical cargo , subject to rotational and translational diffusion according to the equations presented below and in the Text S1 . To this cargo , we attached kinesin motor ( s ) that are modeled as bungee cords , i . e . , they behave as springs with a spring constant of 0 . 32 pN/nm [5] , [10] when stretched beyond their relaxed length of 110 nm but produce no force when compressed . We started the simulation so that potentially one or more motors could bind to a cylindrical microtubule ( 25 nm diameter ) . The motors then moved the cargo along the microtubules , taking 8 nm steps . While technical details of the simulation are in the Text S1 , the general idea is that at each time step Δt , we consider all motors present , calculate all forces acting upon them , and then ask what each of them does . We start by describing how we simulate transport of a cargo with motors attached . Our basic algorithm is as follows . Consider one or more motors attached at random points to the cargo surface . The cargo is then suspended above the microtubule , with a well-defined separation distance between the bottom of the cargo and the top of the microtubule , and the motors are each given an opportunity to attach to the microtubule . If none do ( either because none can reach , or because although they can reach , they stochastically are not able to attach in the allotted time with the ‘on’ rate assumed to be ∼2/sec [11] , [12] , [13] ) , we use one of two initial conditions . If we want to find the time it takes for a cargo with a single motor to attach , then the cargo is allowed to rotate consistent with Brownian diffusion , and the procedure is repeated . Eventually , the motor binds . The time between when the simulation is started and when the motor attaches is the ‘on’ rate for the cargo; since only one motor is present , it reflects how the presence of the cargo affects the motors' on-rate . The other initial condition is used if there are multiple motors and we are more interested in transport along the microtubule after the motors attach to the filament . In this case , if none of the motors attaches after being given the opportunity to do so , the cargo is rotated so that at least one motor attaches to the microtubule . Once some subset of the motors is attached , the cargo travels along the microtubule . At each time step of the simulation , each motor on the cargo is given the opportunity to detach from the MT if it is attached , or attach if it is detached ( and geometrically can reach the MT ) . If a motor is attached to a MT , then there is some probability that it will bind and hydrolyze ATP , and subsequently take a step . Although kinesin is a two headed motor , we model each motor by a single kinesin head that hydrolyzes ATP in such a way that Michaelis-Menten kinetics is obeyed . The probabilities of a motor detaching from the MT , releasing ATP , and taking a step are all dependent on the load on the cargo because the cargo exerts force on the motors ( see Text S1 . This load has contributions from the externally applied force , the other motors which are pulling the cargo , and from thermal fluctuations . The thermal fluctuations randomly rotate and translate the cargo which , in turn , can stretch the motor linkage and exert a load on the motor . ( See below for further details on thermal fluctuations . ) Once all the motors have been given a chance to step , the cargo is translated and rotated according to the force and torque to which it is subjected . The cargo travels along the microtubule until all the motors detach from the microtubule , and the ‘run’ ends; this then determines the run length of the cargo . The velocity is calculated by dividing the distance the cargo moves by the travel time τ , where τ is typically 1 msec but may be as long as 10 msec . Averaging over these velocities gives the average velocity . To get good statistics , we simulate a specified number of runs with the same initial conditions to get a set of runs . We also simulate a number of sets with different initial conditions to obtain good statistics . In our simulations , the spherical cargo is subjected to thermal fluctuations which we can divide into translational and rotational components . The equation of the cargo's translational motion is given by the Langevin equation: ( 1 . 1 ) where m is the cargo's mass and is the cargo's velocity . The drag force on the cargo is proportional to its velocity with the drag coefficient , where R is the cargo's radius and is the coefficient of viscosity which is the kinematic viscosity multiplied by the specific gravity of the fluid . is the sum of the forces due to an external force of magnitude FL and the force of the engaged motors pulling on the cargo . We solve this equation in the Text S1 , and quote the solution here for the position of the cargo at time step t+Δt: ( 1 . 2 ) where is the standard deviation of a normal distribution and is a vector in Cartesian coordinates of the laboratory frame of reference that represents three independent random variates drawn on a normal distribution having zero mean and unit standard deviation . For the cargo's rotational motion , the corresponding Langevin equation is ( 1 . 3 ) where is the moment of inertia of a solid spherical cargo , and is the drag coefficient proportional to the angular velocity . is the torque on the cargo referenced from the center of mass due to the engaged motors . is the rapidly varying random torque due to the thermal fluctuations of the environment . We solve this equation in the Text S1 where we give the formulas for the change in orientation of the cargo at each time step . These formulas are analogous to Eq . ( 1 . 2 ) . As we shall see , rotational diffusion due to thermal fluctuations can play a significant role in limiting the distance that a cargo can travel . After considering motors randomly attached anywhere on the cargo , we consider cases which have a restricted region of the cargo surface area where motors can attach . For these cases , we start each simulation with N motors randomly attached to the cargo's surface within a region specified by the cone angle as shown in Figure 2 . The area available for attachment can be described by a cone with its apex at the center of the sphere . A line extends from the apex to the base of the cone . The cluster angle φ is the angle between this line and the side of the cone . The intersection of the cone with the surface of the cargo defines the allowed region of motor attachment . The cluster angle can vary between 0 and 180 degrees . A cluster angle of 90 degrees defines the lower hemisphere of the cargo . A cluster angle of 180 degrees corresponds to the entire spherical surface , and means that the motors can attach anywhere on the sphere .
Our study of the effects of the cargo on transport has a number of ‘take-home’ messages . The first is that , at both the single-motor and multiple-motor levels , the presence of the cargo can significantly alter the effective ‘on’ rate/probability of successful binding of the motor ( s ) to the filament , because the center of mass of the cargo diffuses away from the microtubule relatively slowly , and while this is occurring , its rotational diffusion frequently brings the motor close enough to the microtubule to allow attachment . Thus , the cargo ‘helps’ the motor to attach , though the degree of assistance depends on cargo size and viscosity of the medium surrounding the cargo . Rapidly diffusing cargos might not linger long in the vicinity of a microtubule , but in a cell where there are multiple filaments available , these cargos could quickly find and bind to a filament . Second , in order to for a motor to attach to the filament in a reasonable amount of time , the motor length needs to be longer or comparable to the radius of the cargo which may explain why motors are 60 to 110 nm in length . Third , if motors are randomly arranged on the cargo's surface , the relationship between the number of motors present and the number of actually engaged motors depends strongly on the cargo size , so that different simple models of regulating cargo motion by recruiting motors to the cargo surface ( either by a specified change in total number of motors , or by a specified change in local motor surface density ) will have different effects on overall cargo motion as a function of cargo size . Thus , in order to have regulation affect a set of cargos equally , independent in variations in cargo size , it is best to have motors clustered in a small region on the cargo . A further finding also supports the utility of motor clustering: for large cargos , if motors are randomly placed , achieving a reasonable number of engaged motors ( n = 3–6 ) would require a large number of motors ( 50–100 ) to be present on the cargo , which appears inconsistent with biochemical characterizations of cargo-bound microtubule motors [4] , though it is consistent with biochemical characterizations of cargo-bound myosin motors [18] which are likely randomly arranged on cargos [18] , [19] . Overall , our findings suggest that , in vivo , microtubule motors are likely organized into clusters when present on large cargos , but that such clustering is unnecessary for small cargos . In addition , a reasonable number of engaged motors would be required for long travel distances of several microns but not for short run lengths . Since microtubules can be tens of microns long compared to actin filaments which have a typical decay length of 1 . 6 microns [18] , we expect long travel distances along microtubules but relatively short run lengths along actin filaments . Thus we predict the microtubule motors kinesin and dynein to be clustered on cargos while we expect the actin motor myosin V to bind randomly to cargos . There is clear experimental evidence for the random arrangement of myosin on cargos in vivo , and weak experimental evidence for the clustering of kinesin and cytoplasmic dynein [19] . For the purposes of this paper , we have assumed that the points where motors are attached to the cargos are fixed on the cargo's surface . This is true in some cases , e . g . , when motors bind to dynactin which in turn binds to spectrin which is a filament that coats some vesicles [20] , [21] . However , in other cases , the attachment points can diffuse through the fluid membrane of the vesicle and cluster at one location . An example of this is an experiment showing that motors dynamically accumulate at the tip of membrane tubes growing out of a vesicle as a consequence of the fluidity of the membrane [13] , [22] . Clustering does not seem to affect the rate at which the first motor of a cargo attaches to a microtubule unless the cargo is large ( greater than 200 nm ) and the viscosity is high . Motor proteins are sufficiently long ( greater than 50 nm ) and rotational diffusion sufficiently rapid that the number of motors on a cargo does not significantly affect the rate at which the cargo binds to the microtubule .
|
The spatial organization of living cells depends upon a transportation system consisting of molecular motor proteins that act like porters carrying cargos along filaments that are analogous to roads . The breakdown of this transportation system has been associated with neurodegenerative diseases such as Alzheimer's and Huntington's disease . In living cells , cargos are typically carried by multiple motors . While some aspects of multiple motor function have received attention , how the cargo itself affects transport has not been considered . To address this , we developed a three-dimensional computer simulation of motors transporting a spherical cargo subject to fluctuations produced when small molecules in the intracellular environment buffet the cargo . These fluctuations can cause the cargo to pull on the motors , slowing them down and making them detach from the filament ( road ) . This effect increases as the cargo size and viscosity of the medium increase . We also found that the presence of the cargo helped the motors to bind to a filament before it drifted away . If other filaments were present , then the cargo could bind to one of them . Our results also indicated that it is better to group the motors on the cargo rather than spread them randomly over the surface .
|
[
"Abstract",
"Introduction",
"Methods",
"Discussion"
] |
[
"physics",
"biophysic",
"al",
"simulations",
"biophysics",
"theory",
"biology",
"computational",
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2011
|
How Molecular Motors Are Arranged on a Cargo Is Important for Vesicular Transport
|
Enterovirus 71 ( EV71 ) is the major causative pathogen of hand , foot , and mouth disease ( HFMD ) . Its pathogenicity is not fully understood , but innate immune evasion is likely a key factor . Strategies to circumvent the initiation and effector phases of anti-viral innate immunity are well known; less well known is whether EV71 evades the signal transduction phase regulated by a sophisticated interplay of cellular and viral proteins . Here , we show that EV71 inhibits anti-viral type I interferon ( IFN ) responses by targeting the mitochondrial anti-viral signaling ( MAVS ) protein—a unique adaptor molecule activated upon retinoic acid induced gene-I ( RIG-I ) and melanoma differentiation associated gene ( MDA-5 ) viral recognition receptor signaling—upstream of type I interferon production . MAVS was cleaved and released from mitochondria during EV71 infection . An in vitro cleavage assay demonstrated that the viral 2A protease ( 2Apro ) , but not the mutant 2Apro ( 2Apro-110 ) containing an inactivated catalytic site , cleaved MAVS . The Protease-Glo assay revealed that MAVS was cleaved at 3 residues between the proline-rich and transmembrane domains , and the resulting fragmentation effectively inactivated downstream signaling . In addition to MAVS cleavage , we found that EV71 infection also induced morphologic and functional changes to the mitochondria . The EV71 structural protein VP1 was detected on purified mitochondria , suggesting not only a novel role for mitochondria in the EV71 replication cycle but also an explanation of how EV71-derived 2Apro could approach MAVS . Taken together , our findings reveal a novel strategy employed by EV71 to escape host anti-viral innate immunity that complements the known EV71-mediated immune-evasion mechanisms .
When viruses infect host cells , the innate immune response is activated as the first line of defense against viral invasion . Pathogen associated molecular patterns ( PAMPs ) are sensed by host pattern recognition receptors ( PRRs ) , resulting the expression of type I interferon and proinflammatory cytokines [1] , [2] . These cytokines can induce an anti-viral state in the host cells and initiate host adaptive immunity , leading to limitation or clearance of the viral infection . Anti-viral innate immunity can be roughly divided into three phases: ( i ) the initiation phase , where PRRs recognize viral RNA and recruit specific signaling adaptor molecules; ( ii ) the signal-transduction phase , where adaptor molecules transduce signaling to activate IKK-related kinases that activate transcription factors , like interferon regulatory factor 3 ( IRF3 ) and nuclear factor-κB ( NF-κB ) ; and ( iii ) the effector phase , where IRF3 and NF-κB translocate to the nucleus and prime type I IFN synthesis . Type I IFNs then activate the signal transducers and activators of transcription ( STAT ) pathway on neighboring cells to induce synthesis of interferon-stimulated genes ( ISGs ) . RNA viruses are detected by membrane-bound Toll-like receptors ( TLRs ) and cytoplasmic sensors , including retinoic acid induced gene-I ( RIG-I ) and melanoma differentiation associated gene ( MDA-5 ) . Although RIG-I and MDA-5 are both RNA helicase domain-containing proteins that use mitochondrial anti-viral signaling protein ( MAVS , also called VISA , IPS-1 , Cardif ) to transduce signaling , they specialize in sensing different types of viruses [3]–[6] . Enterovirus 71 ( EV71 ) , which belongs to the Picornaviridae family , is a single-stranded , positive-sense RNA virus . EV71 infection usually causes childhood exanthema , also known as hand , foot , and mouth disease ( HFMD ) . Acute EV71 infection can also induce severe neurological disease , including aseptic meningitis , brainstem and/or cerebellar encephalitis , and acute flaccid paralysis [7] . EV71 outbreaks have been reported around the world since the first report in the United States in 1974 [8] . In recent years , the frequency and the severity of EV71 infection are increasing in China and pose a threat to human health and social stability . However , no effective vaccines or specific anti-viral treatments are currently available . Although the specific molecular mechanism underlying EV71 pathogenesis is not clear , EV71 virulence is associated with circumventing anti-viral immunity . While type I IFN administration protects mice against EV71 infection , anti-IFNα/β neutralizing antibody treatment exacerbates EV71-induced disease [9] . Recent studies show that the EV71-encoded 3C protease ( 3Cpro ) inhibits the RIG-I and MAVS interaction and is able to cleave TIR domain-containing adaptor inducing IFN-β ( TRIF ) , a key TLR3 adaptor molecule , to inhibit type I IFN production [10] , [11] . Another recent study showed that 2Apro , another EV71 protease , reduced IFN receptor I ( IFNAR1 ) expression that inhibited type I IFN signaling [12] . Although these known EV71-mediated inhibitory mechanisms affect the initiation and effector phases of the innate immune response , not much is known about the effect of EV71 infection on the signal transduction phase involving TLR3- or RIG-I/MDA5-mediated type I IFN production , a phase that is usually regulated by a sophisticated interplay between host and viral proteins under infection conditions . This study aimed to explore whether and how EV71 inhibits type I IFN production through regulating signal transduction pathways . We found that EV71 inhibited type I IFN responses upstream of IRF3 activation . MAVS , the common adaptor signaling molecule acting upstream of IRF3 , was cleaved during EV71 infection . MAVS cleavage was independent of host cellular protease activity , but was dependent on EV71-encoded protease 2Apro , where 2Apro cleaved MAVS at three residues with different degrees of cleavage . EV71 also induced morphological and functional changes to host-cell mitochondria , and the EV71 VP1 protein was found to associate with host-cell mitochondria . Overall , our findings reveal a novel virus–MAVS interaction that inhibits signal transduction induced by anti-viral innate immunity to evade the ensuing immune response .
Previous studies demonstrated that EV71 evolved mechanisms to counteract type I IFN production [10]–[12] . To confirm and further clarify whether and how EV71 inhibits type I IFN production and determine at which step inhibition occurs , type I IFN production was evaluated . First , we measured type I IFN activity in supernatant from Sendai virus ( SEV ) - or EV71-infected HeLa cells using the type I IFN-responsive 2FTGH-ISRE reporter cell line . While the supernatant from the positive control SEV-infected HeLa cells exhibited time-dependent type I IFN production , supernatant from EV71 infected cells contained negligible type I IFN production over 36 h ( Figure 1A ) . RT-PCR analysis showed that EV71 failed to induce mRNA expression of IFN-β or RANTES , a proinflammatory cytokine , in HeLa cells even though SEV could successfully do so ( Supplemental Figure S1A ) . To confirm these results , a luciferase reporter assay was performed to investigate whether SEV- and EV71-infection induced IFN-β and NF-κB promoter activation . EV71 barely activated the IFN-β and NF-κB promoters ( Supplemental Figure S1B ) . The above results suggest that EV71 inhibitory activity may occur upstream of the effector phase of type I IFN production . Based on the above results , we next looked at IRF3 dimerization , which is a critical step upstream of IFN-β transcription and production . IRF3 dimerization was monitored by native PAGE , and we found that EV71-infected HeLa cells did not induce IRF3 dimerization even though SEV was able to induce it in a time-dependent manner ( Figure 1B ) . This result indicates that EV71 might inhibit IFN-β production upstream of IRF3 activation . In order to confirm this result , native PAGE was performed on EV71-infected HeLa cells super-infected with SEV at different time points post-EV71 infection . The results showed that EV71 infection led to a pronounced , time-dependent decrease in SEV-induced IRF3 dimerization but did not interfere with SEV replication ( Figure 1C ) . This EV71-mediated suppression of SEV-induced IRF3 dimerization reinforced the idea that EV71 inhibited IFN-β upstream of IRF3 activation . MAVS is the unique adaptor molecule shared between the RIG-I and MDA-5 cytoplasmic PRRs , which acts upstream of IRF3 [3]–[6] . Many viruses , such as hepatitis C virus ( HCV ) [6] , [13]–[16] , GB virus [17] , hepatitis A virus ( HAV ) [18] , Coxsackievirus B3 ( CVB3 ) [19] , and rhinovirus [20] , specifically target MAVS in order to escape host innate immunity . Considering the important function of MAVS in both the RIG-I and MDA-5 signaling pathway , a time-course study was conducted to test MAVS expression levels during EV71 infection by western blot . We found that expression of full-length MAVS declined after EV71 infection , and two fragments appeared at approximately 30 kD in both EV71-infected HeLa cells and rhabdomyosarcoma ( RD ) cells ( Figure 2A–B ) . This result suggested that MAVS was cleaved during EV71 infection and that more than one cleavage residue may exist . In order to confirm that MAVS was indeed the source of these cleavage bands , two separate antibodies raised against different amino acid sequences of MAVS ( E-3 was raised against residues 1–135 of human MAVS , while AT107 was raised against residues 160–450 ) were used to probe the above-mentioned western blot . Indeed , the cleavage products were recognized by both antibodies , as exhibited by the yellow signal that appeared after merging the green ( E3 ) and red ( AT107 ) western blot images . This result confirmed that MAVS was the source of the cleaved products ( Figure 2A–B ) . MAVS is localized on the outer membrane of mitochondria , and this sub-cellular localization is crucial for its function in anti-viral signaling . We therefore examined whether any changes to the cellular distribution of its cleavage products occurred during EV71 infection by confocal microscopy . The results showed that MAVS co-localized with Mito-dsRed , an RFP-containing mitochondrial target construct , in mock-infected cells . However , EV71 infection dramatically disrupted this co-localization ( Figure 3A ) . To further confirm this , we separated the mitochondrial protein from the cytosolic protein by differential centrifugation . Western blot analysis was performed to determine the distribution of MAVS and its cleaved fragments; we clearly observed that MAVS was cleaved from the mitochondria , and the cleaved fragments were released into the cytoplasm ( Figure 3B ) . Viral infection induces cellular apoptosis as a consequence of the battle between the host cells and the virus . Apoptosis has been observed to occur in EV71-infected cells [21]–[23] , and the EV71-derived proteases 2Apro and 3Cpro have been reported to induce this process [24] , [25] . During virus-induced apoptosis , caspases are activated and lead to cleavage of some cellular proteins like PARP . Innate immune signaling proteins such as RIG-I , MDA-5 , and MAVS are also targeted by activated caspases in other viral infections [20] , [26]–[28] . These proteins also undergo proteasomal degradation through host- and viral-protein-mediated ubiquitin-ligating proteins , like host-derived RNF125 , RNF5 , and PCBP2 and the virus-derived hepatitis B virus ( HBV ) X protein [29]–[32] . To test whether EV71-induced MAVS cleavage is associated with cellular apoptosis and activated caspases , we first examined whether caspase activation occurred after EV71 infection in HeLa cells by western blot analysis of pro-caspase 3 , 8 , 9 , PARP , and EV71-VP1 during an infection time course . EV71 infection led to caspase 3 , 8 , and 9 activation as well as PARP cleavage . PARP cleavage began at 12 h post-infection and was nearly complete at 24 h ( Figure 4A ) , while MAVS cleavage was similarly detected at both 12 and 24 h post-infection ( Figure 2A ) , suggesting that MAVS cleavage accompanied cellular apoptosis . To further investigate whether MAVS cleavage is the result of activated caspases or proteasome degradation , we tested the effect of pan-caspase inhibitor Z-VAD-FMK and proteasome inhibitor MG132 on MAVS cleavage in mock- or EV71-infected HeLa cells . Western blot analysis showed that PARP cleavage and caspase-3 activation , but not MAVS cleavage , was inhibited by Z-VAD-FMK alone or Z-VAD-FMK in combination with MG132 . MG132 alone inhibited EV71 replication ( indicated by the decreased VP1 protein , which was also reported in other viral infections [33]–[35] ) , but could not rescue MAVS cleavage ( Figure 4B ) . Consistent with these results , neither the inhibitors alone nor their combined treatment could rescue IRF3 dimerization in EV71-infected cells as determined by native PAGE ( Figure 4C ) . Taken together , the above results indicate that MAVS cleavage is independent of cellular apoptosis and proteasome degradation . Mitochondria are well known for their crucial role in energy production , calcium homeostasis , and apoptosis . The presence of MAVS on the mitochondrial outer membrane indicates that this organelle has anti-viral functions . Recently , reports show that mitochondrial dynamics and membrane potential ( ΔΨ m ) are all required for MAVS-mediated anti-viral signaling , which underscores the importance of the mitochondrial microenvironment in anti-viral signaling [36] , [37] . As MAVS was cleaved during EV71 infection and accompanied cellular apoptosis , we evaluated whether other mitochondrial abnormalities were associated with EV71 infection . First , we measured membrane potential using Mito-probe JC-1 , a cationic dye that indicates mitochondrial depolarization by red-green fluorescence ratio reduction . Upon EV71 infection , an obvious loss of ΔΨ m began at 12 h ( Figure 5A ) . We next assessed mitochondrial outer-membrane permeability by measuring cytochrome c release , another indicator of mitochondrial abnormality , and found that EV71 infection led to a small amount of cytochrome c release from the mitochondria into the cytoplasm ( Figure 5B ) . To further explore mitochondrial abnormalities , we observed morphological changes by confocal microscopy of Mito-dsRed-transfected HeLa cells infected with EV71 . Dramatic morphological changes occurred , as the typical mitochondrial network structure observed in mock-infected cells became diffuse and unclear in EV71 infected cells . Moreover , mitochondria partially stained positive for an anti-EV71 virus antibody , indicating viral co-localization with mitochondria ( Figure 5C ) . Further in-cell western blot analysis demonstrated that the EV71 antibody was against the EV71 structural protein VP2 ( Supplemental Figure S2 ) . The extent of this partial co-localization indicated that mitochondria might only function at particular steps during the viral life cycle . The processed viral components of many viruses , like HBx of HBV [38] , NS3/4A and NS4A of HCV [6] , [13] , [14] , [16] , [39] , 2B of poliovirus [40] , and the 3ABC precursor of HAV [18] , have been reported to associate with mitochondria to induce morphologic and functional changes in the mitochondria , causing subsequent apoptosis or targeting MAVS to inhibit innate-immune signaling . Based on the above analysis , we tested whether mitochondria were involved in the EV71 viral replication cycle by evaluating whether the EV71 structural protein , VP1 , physically associated with mitochondria . Western blot analysis of mitochondria isolated from the cytoplasmic protein fraction showed that VP1 is mainly detected in the crude mitochondria as compared to the cytosol compartment ( Figure 6A ) . In order to exclude the possibility that the VP1 detected in the isolated mitochondria fraction was a result of endoplasmic reticulum ( ER ) contamination that is believed to be important for picornavirus replication , we performed a more rigorous protocol to isolate mitochondria ( using slower centrifugation speeds ) and further purified it by Percoll gradient fractionation ( Figure 6B ) [15] , [41] . Using specific markers for ER and mitochondria , western blot analysis demonstrated that the pure mitochondria were not contaminated with ER and that EV71 VP1 still associated with the mitochondrial compartment ( Figure 6C ) . Collectively , the above results strongly indicate that the EV71 viral replication cycle involves the mitochondria , suggesting that viral proteins expressed during EV71 propagation may cause mitochondrial abnormalities and induce MAVS cleavage . EV71 encodes two proteases , 2Apro and 3Cpro , that are important for processing viral protein precursors; they also reportedly cleave a variety of host-cell molecules that affect fundamental functions of the host cell . Since we found that MAVS was cleaved upon EV71 infection , we speculated that EV71 proteins executed this cleavage , especially as we previously excluded the role of cellular proteases and further detected the presence of viral protein on mitochondria . Since 2Apro has a strong inhibitory effect on host gene expression that makes it difficult to express and test in cultured cells , we first took advantage of a cell-free in vitro cleavage system—considered to be the most straight-forward approach to study picornavirus protease hydrolysis function [42]–[48]—to determine whether EV71-encoded 2Apro and 3Cpro proteases could directly cleave MAVS . We incubated recombinant EV71 2Apro and 3Cpro with HeLa cell extracts and detected MAVS cleavage by western blot using two antibodies that recognize different MAVS epitopes . EV71-infected HeLa cells were used as the positive control . We found that although both proteases generated cleavage bands , only 2Apro generated the same-sized cleavage bands as the EV71-infected cells . The appearance of these cleavage bands , approximately 30 kD in size , correlated with 2Apro treatment in a dose-dependent manner ( Figure 7A–B ) . Another band in both 2Apro and 3Cpro treated cell extracts ( Figure 7A–B , indicated by * ) was considered to be a non-specific cleavage product and will be discussed later . In order to further scrutinize the role of EV71 3Cpro , we transfected HeLa cells with increasing doses of a plasmid encoding GFP-tagged 3Cpro and 3ABC proteases , as HAV use the 3ABC precursor to cleave MAVS [18] . Neither of these proteins induced MAVS cleavage even when expressed at a high level in HeLa cells ( Figure 7C ) . This result was also consistent with our previous study showing that EV71 3Cpro could not interact with MAVS when over-expressed in live cells [11] . We next explored whether 2Apro exhibited any proteolysis ability on MAVS by transfecting a 2Apro-expressing plasmid into HeLa cells . eIF4GI , a known substrate of 2Apro , was used as an readout to indicate whether 2Apro was functional in this experimental system , as we know that 2Apro expression in this system may be weak since 2Apro protein was difficult to detect by western blot ( likely due to the concomitant restriction on its own expression from its inhibition effect on host gene expression ) . To our surprise , while eIF4GI cleavage was detected in this system , PABP , another 2Apro substrate [20] , [26] , and MAVS remained intact ( Supplemental Figure S3A ) . We speculated that this difference might be due to the varied sensitivities that these substrates have to 2Apro levels , and we tested this idea by a time-course study in EV71-infected cells . Since all mature EV71 viral proteins are derived from the same poly-protein precursor that undergoes subsequent post-translational cleavage , the amount of VP1 could indirectly reflect the varied expression of 2Apro and was therefore utilized to monitor 2Apro expression in this study . The results showed that eIF4GI cleavage appeared at 6 h after EV71 infection when VP1 protein was expressed at a low , not detectable level; in contrast , PABP and MAVS cleavage was observed at a later time point , at 12 h , when VP1 was abundantly expressed during infection ( Supplemental Figure S3B ) . This result supported our above speculation . Previous attempts to efficiently express target genes in mammalian cells used the prokaryotic T7 RNA polymerase and the internal ribosome entry site sequence ( IRES ) of encephalomyocarditis virus ( EMCV ) to avoid host transcription factors and permit mRNA translation in a capping-independent way [49] . Another study showed that the foot-and-mouth disease virus ( FMDV ) , which also belongs to the Picornaviridae family , could be efficiently rescued in a baby hamster kidney cell line ( BHK-21 ) stably expressing T7 polymerase [50] . Considering that FMDV has a similar genomic structure and encodes a similar protease to EV71 2Apro [45] , [51] and that our 2Apro-expressing plasmid contained both a T7 promoter and IRES sequence upstream of the 2Apro coding region , we exogenously expressed 2Apro and assessed its cleavage effect on MAVS in BSRT7/5 cells , a derivative cell line from BHK-21 that constitutively expresses T7 RNA polymerase [52] . 2Apro was indeed abundantly expressed in these cells , and the results showed that MAVS decreased with increasing 2Apro expression ( Figure 7D ) . However , the cleavage bands were absent in this system; this absence might be due to the amino acid sequence differences between human and hamster MAVS , or to highly efficient cleavage in this over-expression system , rendering the cleavage fragments unstable or short-lived . An analogous phenomenon was previously reported in CVB 3Cpro-mediated cleavage of MAVS and in HCV-mediated cleavage of TRIF [19] . Taken together , these results suggest that EV71 2Apro , but not 3Cpro , is the protease inducing MAVS cleavage upon EV71 infection . As EV71 2Apro is a cysteine protease , its major catalytic sites are His21 , Asp39 , and Cys110 . To further confirm that the catalytic enzymatic activity of 2Apro is responsible for cleaving MAVS , we introduced a mutation in 2Apro that changed amino acid 110 from Cys to Ala ( named 2Apro-110 ) , which destroyed and inactivated the catalytic site of 2Apro [12] , [47] , [53] . We incubated 2Apro-110 with HeLa cell extracts and used PABP as a positive control for picornavirus 2Apro enzyme activity . Western blot results showed that the mutated 2Apro lost the ability to induce cleavage of both MAVS and PABP in this cell-free cleavage system ( Figure 7E ) . Taken together , these results suggest that EV71 2Apro mediates MAVS cleavage during EV71 infection , and that the catalytic enzyme activity of 2Apro is required for cleaving the MAVS protein . In order to identify the 2Apro-targeted cleavage residue ( s ) within the MAVS protein , we took advantage of the Protease-Glo Assay system to screen the whole extra-membrane region of MAVS . In this system , synthesized oligonucleotides encoding 12-mer polypeptides of MAVS ( every 12 amino acids , with 6 amino-acid overlap ) were inserted in-frame into a pGlosensor-10F linear vector that contained a genetically engineered firefly luciferase . The constructs were then expressed in a protein expression system labeled with FluoroTect GreenLys and used as substrate for 2Apro . If 2Apro cleaved any of the expressed polypeptides , an increase in luciferase activity would be detected , and two cleaved products at 36 and 25 kD would emerge in gel analysis ( Figure 8A ) . Upon the first round of screening the 86 constructs we generated , we chose any plasmid exhibiting more than a 5-fold increase in luminescence density together with the visualized cleavage products in gel analysis as positive candidates; 10 constructs met this criterion ( Figure 8B , Table 1 ) . Some of these positive constructs may be false positives , since the linker region of pGlosensor-10F vector contains a Gly residue that is prone to being recognized as P1′ site of 2Apro substrate and cleaved by 2Apro [47] , [54]–[56] . The Gly residues were mutated to Ala , and these vectors were used in the second round of screening . Three constructs remained positive and were found to encode MAVS protein residues 201–212 , 243–254 , and 255–266 ( Figure 8C; Table 2 ) . Some characteristics are common among picornavirus 2Apro substrates , according to previous studies: the P1 position is preferentially occupied with a hydrophobic residue , and the P2 position is usually a Thr/Ser residue [47] , [54]–[56] . The amino acid composition of the three positive polypeptides revealed that the P1′ residues were composed of Gly209 , Gly251 , and Gly265 , respectively . We therefore constructed site-directed mutants ( from Gly to Ala ) of these potential cleavage residues and designated them as M209 , M251 , and M265 . Upon exposure to 2Apro , the results showed that these mutations conferred resistance to all three constructs ( Figure 8D ) . To further confirm the above results , we constructed plasmids encoding the non-mutated MAVS extra-membrane segment ( designated as MAVS-EM ) as well as a corresponding MAVS mutant containing Gly to Ala mutations at all three ( 209 , 251 , and 265 ) sites ( designated as MAVS-EM-3M ) . These two plasmids were expressed by in vitro translation in the presence of FluoroTect GreenLys . Gel analysis of the 2Apro-induced cleavage pattern demonstrated that 2Apro hydrolyzed MAVS-EM but failed to hydrolyze MAVS-EM-3M ( Figure 8E ) . Taken together , these results demonstrate that Gly209 , Gly251 , and Gly265 are the cleavage residues within MAVS that are targeted by EV71 2Apro . We also tested EV71 3Cpro in both the Protease-Glo assay screening for cleavage sites in the extra-membrane region of MAVS and in the cleavage assay testing cleavage ability on the in vitro translated MAVS-EM . Although two oligo sets ( encoding MAVS residues 87–98 and 147–158 ) appeared to be positive candidates in the Protease-Glo assay ( Supplemental Figure S4 ) , 3Cpro failed to cleave in vitro translated MAVS-EM ( Supplemental Figure S5 ) . This result was consistent with the results obtained from the 3Cpro and 3ABC over-expressed cells , and again demonstrated the inability of EV71 3Cpro to cleave MAVS . The discrepancy between the results may be explained by MAVS harboring potential 3Cpro cleavage sites that could be cleaved in the linear-polypeptide-based screening assay but not in the whole-protein-based cleavage assay due to conformational structure constraints that might block the approaching of 3Cpro protein . Considering that MAVS translated in vitro may be slightly different from MAVS expressed in mammalian cells , such as in its protein conformation , stable cell lines were established to express wild-type MAVS ( WT-MAVS ) and MAVS mutants . Among the MAVS mutants , each residue was mutated individually and designated as m-MAVS-209 , m-MAVS-251 , and m-MAVS-265 , and all three residues were also simultaneously mutated within one mutant ( m-MAVS-3M ) . Cell lysates from the above cell lines were incubated with 2Apro and then subject to western blot analysis to evaluate MAVS cleavage . Wild-type MAVS could be cleaved by 2Apro , resulting in two major cleavage fragments: CF209 , a ∼40 kD peptide cleaved from Gly209 , and CF251/265 , a ∼34 kD product . CF251/265 might contain a mixture of cleavage fragments from CF251 and CF265 cleaved from Gly251 and Gly265 , respectively . Gly251 and Gly265 lie close to each other within the protein , which might account for why these two cleavage fragments could not be distinguished from each other in the gel . While m-MAVS-3M is resistant to cleavage by 2Apro , m-MAVS-209 , m-MAVS-251 , and m-MAVS-265 exhibited different degrees of cleavage after incubation with 2Apro ( Figure 9A ) . 2Apro showed the strongest cleavage ability against Gly251 , followed by Gly209 and Gly265 . This comes from the evidence that CF209 was more abundant than CF265 in 2Apro-treated cell lysates of m-MAVS-251 but was relatively less than CF251 in 2Apro-treated cell lysates of m-MAVS-265 ( Figure 9A , lanes 8&10 ) . These results were also consistent with the luminescence density detection results in the previous Protease-Glo screening assay , which showed that the vector encoding residues 243–254 induced the highest fold increase of luminescence density ( 68-fold ) , compared to the vector encoding residues 243–254 ( 40-fold ) and 255–266 ( 13-fold ) ( Table 2 ) . Figure 9B schematically summarizes the cleavage fragments and the degrees of cleavage that we could conclude from the above analysis . To evaluate the cleavage order of each residue by the 2Apro protease , we performed a kinetic analysis of 2Apro on WT-MAVS , m-MAVS-251 , and m-MAVS-265 . Although all cleavage fragments exhibited a time-dependent increase upon incubation with 2Apro , the time they emerged slightly differed among them . CF251 emerged at 5 min , while CF209 and CF265 began to appear at 15 min ( Figure 9C–E ) . Moreover , this assay verified that CF251 had the strongest band intensity , followed by CF209 and CF265 ( Figure 9C–E ) , consistent with the results from Figure 9A . Taken together , these results suggest that 2Apro exerts varying proteolysis ability on the different cleavage residues contained in MAVS and that Gly251 is the dominant residue that 2Apro most strongly and rapidly cleaves . Since both MAVS and mitochondria are EV71 targets , we wondered whether normal mitochondria containing full-length MAVS could rescue the EV71-mediated inhibition of IRF3 activation . Zeng et al . had established a cell-free system demonstrating that mitochondria derived from SEV-infected cells could activate IRF3 in cytosol [57] , [58] . Taking advantage of this system , we separated the mitochondrial and cytosolic compartments from mock- , SEV- , and EV71-infected cells , and reconstituted the RIG-I signaling pathway by exchanging the different compartments . While the mitochondria from SEV-infected cells dimerized IRF3 in the presence of mock-infected cytosol ( Figure 10A , lane 3 ) , mitochondria from EV71-infected cells inhibited this process ( Figure 10A , lane 4 ) . Moreover , mitochondria from SEV-infected cells rescued IRF3 activation in EV71-infected cytosol ( Figure 10A , lane 6 ) . These results suggest that MAVS cleavage and the associated mitochondrial changes might be a direct cause of EV71-induced inhibition of the innate immune response . MAVS function requires mitochondrial localization . Since the EV71-induced MAVS cleavage occurred at three different residues between the proline-rich domain and the transmembrane domain , the N-terminal MAVS cleavage fragments would be released from the mitochondria . To test whether these cleavage fragments lost function in inducing type I IFN production , a series of deletion mutants from each cleavage residue was generated ( Figure 10B ) and transfected into HeLa cells with an IFN-β luciferase reporter plasmid . While full-length MAVS strongly activated the IFN-β promoter ( nearly 1200-fold ) , none of the deletion mutants could activate the promoter , suggesting that the EV71-induced MAVS cleavage inactivated the signaling cascade leading to type I IFN production ( Figure 10C ) .
EV71 is a member of the Enterovirus genus , Picornaviridae family . Its pathogenicity is likely related to its ability to evade host innate immunity . Although both the TLR3 and RIG-I/MDA-5 pathways recognize viral PAMPs and induce host anti-viral signaling during the innate immune response induced upon EV71 infection [1] , [2] , [59] , the type I IFN response usually resulting from these pathways is totally absent [11] . The mechanism behind this observation is not clearly understood , although circumventing strategies have been found in RIG-I and TLR3 pathways [10] , [11] . In this report , we reveal that another signaling molecule , MAVS , is cleaved by the EV71 viral protein 2Apro at multiple residues that results in inhibiting type I IFN production . This novel finding can help to explain the influence of EV71 on both RIG-I and MDA-5 signaling transduction pathways and is a good supplement to the current understanding of how EV71 escapes host innate immunity . The central role of MAVS in innate immunity predisposes it to being a target of many viruses . In recent years , several different viruses were reported to use various strategies to disrupt MAVS function . HCV-derived NS3/4A protease was the first viral protein reported to co-localize with MAVS at mitochondrial membranes and cleave MAVS at Cys508 [6] , [13] , [14] , [16] , and HBV-derived HBx protein was reported to bind MAVS and promote its degradation to inhibit IFN-β production [31] , [60] . More interestingly , viruses within the Picornaviridae family cleave MAVS through various mechanisms and at different sites . HAV , a picornavirus belonging to the Hepatovirus genus , cleaves MAVS at Gln428 by the protease precursor 3ABC [18] . Rhinovirus cleaves MAVS by its 2Apro and 3Cpro proteases as well as by activated caspase 3 . Coxsackievirus B3 ( CVB3 ) , another member of Enterovirus genus in the Picornaviridae family , cleaves MAVS at Gln148 by its 3Cpro [19] . Our finding that EV71 2Apro cleaved MAVS at Gly209 , Gly251 , and Gly265 provides a new insight into how virus-derived proteins and MAVS can interact . To our knowledge , our study is also the first to show that MAVS cleavage occurred at multiple residues to inhibit type I IFN production . All three cleavage residues reside within the region between the proline-rich domain and transmembrane domain of MAVS , and this region is relatively disorganized from a structural point of view and forms a reasonable docking structure for the approaching of 2Apro protease . Mukherjee et al . previously studied MAVS expression in CVB3- and EV71-infected cells . While they found that CVB3 cleaved MAVS into fragments between 40–50 kD , they failed to detect these cleavage products in EV71-infected cells even though MAVS expression was significantly reduced in both cases; they speculated that MAVS was cleaved at other sites during EV71 infection [19] . Our studies confirmed their speculation , as EV71-induced MAVS cleavage not only occurred at other residues but also by a new mechanism . This finding provides new information regarding pathogen diversity as well as host-pathogen antagonism . Due in part to the identification that mitochondrial-localized MAVS participates in the innate immune response , the idea that mitochondria not only play an important role in energy metabolism and cellular apoptosis but also provide a platform for virus-host interaction is now a generally accepted concept [3]–[6] . Consistent with this , some viral proteins also localize to the mitochondria to cleave MAVS as a way to circumvent innate immunity , like NS3/4A of HCV or the 3ABC precursor of HAV [6] , [13] , [14] , [16] , [18] . Also , mitochondrial dynamics and membrane potential have recently been recognized as essential for MAVS-mediated anti-viral signaling [36] , [37] . These examples highlight the function of mitochondria as a platform structure in innate immunity , where viruses rely on its membrane structure and constitution to complete replication , and host cells utilize its membrane communication mechanisms to sense viral PAMPs and induce anti-viral immunity . In our study , we detected EV71 VP1 protein on mitochondria , raising the possibility that mitochondria may function at some particular stage of EV71 propagation; our results also further support the idea that EV71 could use this localization to cleave MAVS and destroy mitochondria to evade host innate immunity and provide another example for host-pathogen antagonism occurring on this intracellular-membrane platform . This finding could also explain the previously reported interaction between EV71 3Cpro and RIG-I [11] . RIG-I is recruited to a region nearby the mitochondria upon activation and interacts with MAVS via its CARD domain; the known role of 3Cpro in this process suggests that its presence is proximal to the mitochondria . In the literature , mitochondria have only been identified as a replication site for alphanodavirus flock house virus ( FHV ) [61] , although the mitochondrial localization of HAV-derived 3ABC suggested an association with mitochondria in picornavirus replication [18] . Our current findings that EV71 VP1 co-localizes with mitochondria and that mitochondrial abnormalities were observed in EV71-infected cells strengthen the concept that mitochondria play a role in picornavirus replication . Future studies focusing on the specific mechanisms of mitochondria during the picornavirus life cycle should be carried out to further explore this concept . EV71-encoded 2Apro and 3Cpro proteases are responsible for processing poly-protein precursors to produce mature structural and non-structural viral proteins . Picornavirus proteases affect numerous host mechanisms . EV71 3Cpro had been identified as a strong antagonist of innate immunity , as it was shown to interact with RIG-I and cleave TRIF to inhibit the RIG-I– and TLR3-mediated anti-viral signaling [10] , [11] . Picornavirus 2Apro , on the other hand , has been shown to hijack host-cell gene expression by cleaving eIF4GI , eIF4GII , and PABP , among other things [10] , [11] . This gene “shutoff” mechanism also inhibits expression of IFN-stimulated genes and can therefore be considered another mechanism by which picornavirus regulates host innate immunity . Moreover , Enterovirus 2Apro was also previously shown to be essential for its own replication in type I interferon-treated cells [62] , and a recent study showed that EV71 2Apro reduces IFN receptor I ( IFNAR1 ) to inhibit type I IFN signaling , indicating that EV71 2Apro functions as an antagonist to anti-viral innate immunity . Our finding that EV71 2Apro strongly cleaves MAVS supports role for this protease in antagonizing innate immunity . Since our study as well as others showed that both EV71-encoded proteases target anti-viral innate immunity at multiple steps , it is possible they may act synergistically to ensure the effective immune-evasion of EV71 . Our study also attempted to evaluate the contribution of the different mechanisms used by EV71 2Apro and 3Cpro to antagonize innate immunity . We generated two mutated EV71 infectious clones , M-EV71-2A110 and M-EV71-3C40 , that contained mutations at residue 110 of 2Apro and at residue 40 of 3Cpro , respectively , as these sites had previously been demonstrated to be indispensable for innate-immune inhibition by 2Apro and 3Cpro in the above-mentioned study and in our previous study [11] . Unfortunately , we were not able to obtain EV71 mutants with these mutated proteases , as the mutations impeded EV71 production due to the critical nature of these residues in catalytic enzyme activity and in EV71 replication ( Supplemental Figure S6 ) . In this study , we provided direct biochemical evidence that EV71 2Apro protease cleaved MAVS using a cell-free in vitro system . This in vitro cleavage system is widely used and considered to be the most straight-forward approach to study the hydrolysis function of picornavirus proteases [42]–[48] . However , this system presented the following drawbacks as compared to the in vivo system: factors affecting the cleavage process in live cells might be omitted in the in vitro system , such as subcellular location; the in vitro cleavage-reaction buffer is different from the microenvironment in live cells and might cause slight conformational changes of the target proteins; and variation in the amount of recombinant protease , cleavage time , and temperature might induce non-specific cleavage that might confound the results . We speculate that these factors might help to explain the appearance of another MAVS cleavage band ( Figure 7A–B , indicated by * ) in our in vitro cleavage system that did not appear in EV71-infected cells , which we now think may represent a non-specific product . When mapping protease cleavage site ( s ) on a target molecule , the routine method is to construct a series of mutants based on cleavage band size and bioinformatic analysis according to the hydrolyzing characteristics of the protease , followed by co-transfection of the mutants and protease into cells to test the predicted outcome . This approach requires accurate prediction , and missing potential cleavage sites is a possibility , especially when multiple cleavage sites exist . This routine strategy was not appropriate to use in our study for the following additional reasons . First , we cannot successfully express EV71 2Apro at the required levels for verifying the speculated cleavage sites in regular cells , since 2Apro was reported to hijack host-cell gene expression and also affect its own exogenous expression in mammalian cells . Second , we failed to observe the cleavage bands in cells over-expressing MAVS upon EV71 infection , which we originally thought was due to the poor viral replication inhibited by innate-immune activation . Therefore , two strategies were adopted to circumvent these issues , including: ( i ) establishing HeLa cell lines that stably express MAVS and MAVS mutants in which no sustained IRF3 activation was observed; and ( ii ) using P2 . 1 cells to transiently over-express MAVS for EV71 infection experiments . The P2 . 1 cell line is derived from the HT1080 cell line; it cannot respond to type I and type II IFNs because it lacks functional Jak1 and expresses very low IRF3 levels [63] . Despite these strategies , we still failed to observe cleavage bands from exogenously transfected MAVS ( data not shown ) . Although the underlying reason is not yet clear , we speculate the following possibilities to explain these results: ( i ) the conformation and distribution of exogenously transfected MAVS might be different from endogenous MAVS; and ( ii ) exogenously transfected MAVS might have the potential to activate innate immunity and therefore induce and recruit MAVS-associated negative regulators that might prevent its interaction with downstream molecules . This latter possibility was hinted at by a report showing that PCBP2 is a negative regulator of MAVS-mediated signaling [29] , and association of MAVS with other proteins might also prevent any effect of EV71 . We therefore switched strategies and took advantage of the Protease-Glo assay system to screen the whole MAVS extra-membrane region . Using these methods , we successfully identified three MAVS residues cleaved by the EV71 2Apro and confirmed this in both the in vitro translated MAVS-EM and the stably expressed MAVS in HeLa cells . When using exogenous MAVS and MAVS mutants expressed in HeLa cells to evaluate MAVS cleavage , the cleavage fragments recognized by the HA antibody are located in the C-terminus of MAVS and its mutants; they are indeed the corresponding counterparts to the endogenous N-terminal cleavage fragments recognized by the anti-MAVS antibodies used in EV71-infected cells ( Figure 2A–B ) . This can be deduced from the molecular weight size and band intensity of the cleavage fragments . Full-length endogenous MAVS is approximately 65 kD in size , and the two cleavage fragments resulting from EV71 infection are both approximately 30 kD , where one appears above the 30 kD molecular weight band ( ∼31 kD ) and the other one appears below the 30 kD band ( ∼25 kD ) . These bands seem to be counterparts to and coincident with the observed 34 kD ( CF251/265 ) and 40 kD ( CF209 ) bands in Figure 9C–E , including their respective band intensities . Overall , we showed in this study that the EV71-derived 2Apro cleaves the key adaptor molecule MAVS as a strategy to evade anti-viral innate immunity at the signal transduction phase . Furthermore , we identified three key residues cleaved by the 2Apro protease activity on the extracellular fragment of MAVS . Our findings therefore reveal a new mechanism of EV71 viral protease-mediated evasion of host innate immunity .
Rhabdomyosarcoma ( RD ) cells and HeLa cells were purchased from ATCC . RD cells were cultured in MEM supplemented with 10% FBS and penicillin/streptomycin . HeLa cells were cultured in DMEM supplemented with 10% FBS and penicillin/streptomycin . 2FTGH-ISRE cells were a gift from Dr . Zhengfan Jiang ( School of Life Sciences , Peking University , China ) . BSRT7/5 cells were cultured in DMEM supplemented with 10% FBS and 1 mg/mL G418 . Enterovirus 71 ( EV71 ) is a Fuyang strain isolated from a child in the city of Fuyang with a clinical diagnosis of HFMD in 2008 ( GenBank accession no . FJ439769 . 1 ) , and was propagated in RD cells . Sendai virus ( SEV ) was kindly provided by Dr . Zhengfan Jiang and propagated in chicken embryos . The PGL3-IFNβ-Luc , pNifty-Luc , and pRL-Actin plasmids were gifts from Dr . Zhengfan Jiang . Mito-dsRed was provided by Dr . Xuejun Jiang ( Institute of Microbiology , Chinese Academy of Sciences , China ) . pEGFPC1-EV71-3ABC was constructed by inserting EV71 3ABC cDNA fragment into the Hind III and Sal I sites of the pEGFPC1 vector . The plasmid expressing EV71 2Apro was generated by PCR amplification from PEGFPC1-EV71-2A as described before [11] and cloned into pET 30a ( + ) vector . Plasmid expressing EV71 2Apro-110 was mutated by PCR using pET 30a ( + ) -2A as template . The MAVS construct and its mutants were generated by PCR amplification from GFP-MAVS ( provided by Dr . Zhengfan Jiang ) and cloned into the pcDNA3 . 1 ( + ) vector . pcDNA3 . 1-IRES-2A was a gift from Dr . Shih-Yen Lo ( Department of Laboratory Medicine and Biotechnology , Tzu Chi University , Hualien , Taiwan ) and described before [53] . Mouse monoclonal antibodies directed against β-Actin ( AC-15 ) and GFP ( GSN24 ) were purchased from Sigma . Rabbit polyclonal antibody against HA was purchased from Bethyl Laboratories . Rabbit polyclonal antibodies against IRF-3 ( FL-425 ) and cytochrome c ( 7H8 ) were purchased from Santa Cruz Biotechnology . Mouse anti-MAVS ( E-3 , monoclonal antibody raised against residues 1–135 of human MAVS ) and rabbit anti-MAVS ( AT107 , polyclonal antibody raised against residues 160–450 of human MAVS ) were obtained from Santa Cruz Biotechnology and Enzo Life Sciences , respectively . Another MAVS antibody , which reacts with human , mouse , and rabbit MAVS , was purchased from Signalway Antibody and used in western blot analysis of BSRT7/5 cells . Mouse anti-KDEL ( 10C3 , recognizes GPR78 and GPR94 with particular prominence ) , mouse anti-mitochondria ( MTC02 , recognizes a 60 kD non-glycosylated protein component of human mitochondria ) , rabbit anti-caspase 3 , and mouse anti-PABP ( 10E10 ) were obtained from Abcam . Rabbit anti-PARP , rabbit anti-caspase 8 ( D35G2 ) , and rabbit anti-caspase 9 were obtained from Cell Signaling Technologies . Mouse anti-enterovirus 71 was purchased from Millipore . Mouse anti-enterovirus 71 VP1 ( 3D7 ) was purchased from Abnova . Rabbit anti-Sendai antibody was purchased from MBL International Corporation . The general caspase inhibitor benzyloxycarbonyl-Val-Ala-Asp- ( OMe ) fluoromethylketone ( Z-VAD-FMK ) and proteasome inhibitor MG132 were purchased from Sigma and Calbiochem , respectively . HeLa cells ( ∼2×105 ) were seeded on 24-well dishes and transfected the following day by Lipofectamine 2000 ( Invitrogen ) with 200 ng of PGL3-IFNβ-Luc or pNifty-Luc and 5 ng pRL-Actin . Cells were co-transfected with 600 ng of the indicated plasmids or infected with EV71/SEV 24 h post-transfection . In all experiments , cells were lysed and reporter activity was analyzed using the Dual-Luciferase Reporter Assay System ( Promega ) . The type I IFN bioassay was performed as previously reported by Sun et al . [64] . Briefly , the supernatant from SEV- and EV71-infected cells were collected at the indicated times , added directly to 96-well dishes seeded with 2FTGH-ISRE cells , and luciferase activity was measured after 6 h and calculated with reference to a recombinant human IFN-β standard ( R&D system ) . Native PAGE was carried out as previously described [65] . Native gel ( 8% ) was pre-run with native running buffer ( 25 mM Tris and 192 mM glycine , pH 8 . 4 ) with 0 . 5% deoxycholate in the cathode chamber for 30 min at 25 mA on ice . Samples were prepared in the native sample buffer ( 62 . 5 mM Tris–HCl , pH 6 . 8 , 15% glycerol , and 1% deoxycholate ) , then loaded onto the gel and electrophoresed at 20 mA for an additional 1 h . Whole-cell extracts ( 20–100 µg ) were separated by 8%–15% SDS-PAGE . After electrophoresis , proteins were transferred to a PVDF membrane ( Bio-Rad ) . The membranes were blocked for 1 h at room temperature in 5% dried milk and then were probed with the indicated primary antibodies at an appropriate dilution overnight at 4°C . The following day , the membranes were incubated with corresponding IRD Flour 680- or 800-labeled IgG secondary antibodies ( LI-COR Biosciences ) and were scanned by the Odyssey Infrared Imaging System ( LI-COR Biosciences ) . Cells were fixed in 4% formaldehyde , permeabilized in 0 . 5% Triton X-100 , blocked in 1% BSA in PBS , and then probed with indicated primary antibodies for 1 h at room temperature . Following a wash , cells were incubated with their respective secondary antibodies for another 1 h . The cells were then washed and stained with 4 , 6-diamidino-2-phenylindole ( DAPI ) to detect nuclei . Images were captured with a laser confocal microscope ( Leica ) . Mitochondrial isolation was carried out by differential centrifugation . Briefly , cells were harvested and resuspended in HB buffer ( 210 mM mannitol , 70 mM sucrose , 5 mM HEPES , pH 7 . 12 , 1 mM EGTA , and an EDTA-free protease inhibitor cocktail ) and subject to homogenization . After 30 strokes , cell homogenate was centrifuged at 600×g for 10 min at 4°C . The supernatant was saved and subjected to further centrifugation at 10000×g for 10 min at 4°C . The pellet was washed once with HB buffer and designated as the crude mitochondrial fraction . The supernatant was further centrifuged at 12000×g and designated as the cytosol fraction after discarding the final pellet . Mitochondria purification was performed by Percoll gradient fractionation as previously described with minor modifications [41] , [66] , [67] . A schematic overview of the isolation and purification protocol is displayed in Figure 6B . Recombinant EV71 3Cpro was produced as described before [68] . To produce EV71 2Apro and 2Apro-110 , the respective plasmids were introduced into competent E . coli BL21 ( DE3 ) cells , and protein expression was induced by treatment with 200 µM IPTG at 18°C overnight . 2A-His fusion protein was purified by Ni-Agarose column . In vitro cleavage assay was performed with the indicated amount of recombinant protease incubated together with cell lysates in reaction buffer ( 50 mM Tris-HCl , pH 7 . 0 , and 200 mM NaCl ) at 37°C for 6 h or 30°C for 2 h . Mitochondrial membrane potential was analyzed using Flow Cytometry Mitochondrial Membrane Potential Detection Kit ( BD Biosciences ) by a BD FACS Canto II flow cytometer ( BD Biosciences ) . The experiments were carried out according to the manufacturer's instructions . Synthesized oligonucleotides encoding 12-mer peptides ( with six amino-acid overlap between two adjacent 12-mers ) for the MAVS extra-membrane region were inserted in pGloSensor-10F linear vector ( Promega ) . The resulting vectors were subjected to in vitro transcription/translation with TNT SP6 High-Yield Wheat Germ Protein Expression System ( Promega ) and FluoroTect GreenLys in vitro Translation Labeling System ( Promega ) according to manufacturer's instructions . The reactions were incubated at 25°C for 2 h . Then , 7 µg of recombinant EV71 2Apro or 3Cpro was added to 10 µL reactions with 10 µL 2× digestion buffer ( 100 mM Tris-HCl , pH 7 . 0 , and 400 mM NaCl ) . The digestion reactions were incubated for 2 h at 30°C , and a 10 µL aliquot was removed and subjected to 10% SDS-PAGE . The gels were scanned by a Typhoon gel scanner ( GE Healthcare ) to visualize the fluorescently labeled proteins . The remaining 10 µL was diluted 20-fold , and luciferase activity was measured using the Bright-Glo assay reagent ( Promega ) according to the manufacturer's instructions . In vitro transcription/translation of the MAVS extra-membrane region was performed by the TNT SP6 High-Yield Wheat Germ Protein Expression System Labeled with FluoroTect GreenLys . The DNA template for this assay was constructed by amplifying the MAVS coding region at residues 1–513 and cloned into the pF3AWG ( BYDV ) Flexi Vector ( Promega ) . HeLa cells were transfected with pcDNA3 . 1-MAVS and its mutants by Lipofectamine 2000 ( Invitrogen ) and selected in Zeocin ( 200 µg/mL ) to establish the cell lines stably expressing MAVS and MAVS mutants . In Vitro IRF3 activation assay was carried out as previously described by Zeng et al [57] , [58] . Briefly , HeLa cells were resuspended in Buffer A ( 10 mM Tris-HCl pH 7 . 5 , 10 mM KCl , 0 . 5 mM EGTA , 1 . 5 mM MgCl2 , 0 . 25 M D-mannitol , and EDTA-protease inhibitor cocktail ) and homogenated . Then , the homogenates were centrifuged at 1000×g at 4°C for 5 min; the supernatants were further centrifuged at 5000×g at 4°C for 10 min to separate the pellets ( P5 ) and the supernatants ( S5 ) . P5 was washed once with Buffer B ( 20 mM HEPES-KOH pH 7 . 4 , 0 . 5 mM EGTA , 0 . 25 M D-mannitol , and EDTA-protease inhibitor cocktail ) and resuspended in Buffer B . For each reaction , 10 µg P5 and 20 µg S5 were mixed in Buffer C ( 20 mM HEPES-KOH pH 7 . 0 , 2 mM ATP , 5 mM MgCl2 ) and incubated at 30°C for 1 h in a 10 µL reaction system . The reaction mixtures were then subjected to native PAGE , and the dimerization of endogenous IRF3 was detected by western blot . MAVS ( HGNC: 29233 ) ; RIG-I ( HGNC: 19102 ) ; MDA-5 ( HGNC: 18873 ) ; TLR3 ( HGNC: 11849 ) ; IRF3 ( HGNC: 6118 ) ; TRIF ( HGNC: 18348 ) ; Caspase 3 ( HGNC: 1504 ) ; Caspase 8 ( HGNC: 1509 ) ; Caspase 9 ( HGNC: 1511 ) ; PARP ( HGNC: 270 ) ; PABP ( HGNC: 8554 ) ; RANTES ( HGNC: 10632 ) ; IFN-β ( HGNC: 5434 ) ; PCBP2 ( HGNC: 8648 ) ; RNF125 ( HGNC: 21150 ) ; RNF5 ( HGNC: 10068 ) ; IFNAR1 ( HGNC: 5432 ) ; Cytochrome c ( HGNC: 19986 ) .
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Enterovirus 71 ( EV71 ) is the causative pathogen of hand , foot , and mouth disease ( HFMD ) . Since the 2008 outbreak of HFMD in Fuyang , Anhui province , China , HFMD has been a severe public health concern affecting children . The major obstacle hindering HFMD prevention and control efforts is the lack of targeted anti-viral treatments and preventive vaccines due to the poorly understood pathogenic mechanisms underlying EV71 . Viral evasion of host innate immunity is thought to be a key factor in viral pathogenicity , and many viruses have evolved diverse antagonistic mechanisms during virus-host co-evolution . Here , we show that EV71 has evolved an effective mechanism to inhibit the signal transduction pathway leading to the production of type I interferon , which plays a central role in anti-viral innate immunity . This inhibition is carried out by an EV71-encoded 2A protease ( 2Apro ) that cleaves MAVS—an adaptor molecule critical in the signaling pathway activated by the viral recognition receptors RIG-I and MDA-5—to escape host innate immunity . These findings provide new insights to understand EV71 pathogenesis .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"viral",
"immune",
"evasion",
"virology",
"biology",
"microbiology"
] |
2013
|
Enterovirus 71 Protease 2Apro Targets MAVS to Inhibit Anti-Viral Type I Interferon Responses
|
Chromosome congression and segregation in C . elegans oocytes depend on a complex of conserved proteins that forms a ring around the center of each bivalent during prometaphase; these complexes are then removed from chromosomes at anaphase onset and disassemble as anaphase proceeds . Here , we uncover mechanisms underlying the dynamic regulation of these ring complexes ( RCs ) , revealing a strategy by which protein complexes can be progressively remodeled during cellular processes . We find that the assembly , maintenance , and stability of RCs is regulated by a balance between SUMO conjugating and deconjugating activity . During prometaphase , the SUMO protease ULP-1 is targeted to the RCs but is counteracted by SUMO E2/E3 enzymes; then in early anaphase the E2/E3 enzymes are removed , enabling ULP-1 to trigger RC disassembly and completion of the meiotic divisions . Moreover , we found that SUMO regulation is essential to properly connect the RCs to the chromosomes and then also to fully release them in anaphase . Altogether , our work demonstrates that dynamic remodeling of SUMO modifications facilitates key meiotic events and highlights how competition between conjugation and deconjugation activity can modulate SUMO homeostasis , protein complex stability , and ultimately , progressive processes such as cell division .
Meiosis is a specialized form of cell division where chromosomes are duplicated once and segregated twice , in order to reduce the chromosome number by half to generate haploid gametes . In contrast to mitosis , oocyte meiosis in many species occurs in the absence of centrosomes , the microtubule organizing centers that nucleate microtubules and help to define the spindle poles . The mechanisms by which chromosomes congress and ultimately segregate on these unique acentrosomal spindles are not well understood . C . elegans oocytes utilize mechanisms for chromosome congression and segregation that are distinct from those used in mitosis . In these cells , end-on kinetochore-microtubule attachments are not apparent , and instead microtubules associate laterally with the chromosomes [1] . Additionally , segregation is kinetochore-independent , as kinetochores are normally disassembled during early anaphase , and kinetochore depletion does not affect chromosome segregation rates [2] . Although the exact mechanism driving chromosome segregation remains controversial , it is clear that both congression and segregation depend upon a large protein complex that forms a ring around the center of each bivalent in Meiosis I ( MI ) and around the sister chromatid interface in Meiosis II ( MII ) . These ring complexes ( RCs ) are comprised of a number of conserved cell division proteins , including the Chromosome Passenger Complex/CPC ( containing AIR-2/Aurora B kinase , ICP-1 , CSC-1 , and BIR-1 ) , the kinesin-4 family motor KLP-19 [1] , and the kinase BUB-1 [2] . During prometaphase , KLP-19 provides chromosomes with a plus-end directed force that is thought to facilitate congression to the metaphase plate [1] . Then in anaphase , separase ( SEP-1 ) is targeted to the RCs to cleave cohesin [3] , and the RCs are released and left at the center of the spindle as the chromosomes segregate [2 , 3] . Depletion of some individual RC components and/or preventing the assembly of the complex as a whole results in severe chromosome segregation defects [1–11] , demonstrating the importance of this complex during meiosis . Therefore , understanding how the RC assembles and is regulated will provide valuable insights into how chromosomes are accurately partitioned in oocytes . Although the individual contribution of each RC protein to the overall functions of the complex is not fully understood , previous studies have revealed some of the principles underlying RC assembly . Initial work demonstrated that certain components are required for others to load , with the CPC required for the proper localization of all other known RC components [1 , 2 , 12] . Moreover , a recent study showed that the small ubiquitin-like modifier SUMO , a reversible post-translational modification , regulates RC assembly [4] . In C . elegans , there is one SUMO ortholog ( SMO-1 , hereafter referred to as SUMO ) that can be conjugated to target proteins by the hierarchical actions of an E1 activating enzyme , an E2 conjugating enzyme ( UBC-9 ) , and SUMO-specific E3 ligases [13] . Evidence supporting a role for SUMO in RC assembly includes the demonstration that: 1 ) SUMO , UBC-9 , and GEI-17 ( a PIAS family E3 ligase ) localize to the RC , 2 ) RC assembly is GEI-17 dependent , 3 ) RC components AIR-2 and KLP-19 can be SUMOylated in vitro , and 4 ) other RC components such as BUB-1 have SUMO interaction motifs ( SIMs ) and can interact with SUMOylated proteins [4 , 14] . These findings support the view that a network of SUMO-SIM interactions between RC proteins drives the assembly of the complex . However , much still remains to be discovered about how SUMO contributes to RC organization and function . Importantly , the mechanisms driving RC disassembly in anaphase are even less understood . Normally , AIR-2/Aurora B leaves the RCs soon after their release from chromosomes in early anaphase and relocalizes to the microtubules [6 , 11] . At the same time , the released RCs appear to lose structural integrity , since they flatten by mid anaphase and then are absent by late anaphase [2 , 3] . However , we recently discovered that AIR-2 relocalization to microtubules and RC disassembly are delayed in the presence of a variety of meiotic errors , demonstrating that these processes are regulated . We also found that when the RCs remained intact , anaphase spindle morphology was altered in a manner that could potentially increase the fidelity of chromosome segregation [15] . Therefore , control of RC disassembly is a central feature of anaphase progression , making it important to understand . Here , we provide the first detailed description of RC disassembly in C . elegans oocytes and show that this process and other critical anaphase events rely on the dynamic remodeling of SUMO modifications . We found that SUMO promotes the stability of the RC and that RC disassembly is dependent on targeting the SUMO protease ULP-1 to the structures , suggesting that ULP-1 could promote RC disassembly upon removal of the E2/E3 enzymes in early anaphase . Moreover , we found that ULP-1 is active prior to anaphase and regulates aspects of RC assembly and maintenance independent of the known role for this family of proteases in SUMO maturation . Our findings therefore demonstrate that dynamic SUMO remodeling is required for key events that facilitate anaphase progression during oocyte meiosis and also demonstrate that a balance between SUMO E2/E3 enzyme and ULP-1 protease activity can regulate the SUMOylation status and thus the stability of essential protein complexes .
Since SUMO is RC-associated and is required for RC assembly [4] , we reasoned that SUMO removal might be required for the disassembly of these complexes in anaphase . Consistent with this hypothesis , previous imaging demonstrated that SUMO leaves the RCs sometime in anaphase , relocalizing across the spindle by late anaphase [4] . However , precisely when SUMO leaves the RCs was not addressed . Therefore , we set out to carefully assess SUMO localization in relation to other RC components ( Fig 1A and 1B ) . As shown previously , we found that SUMO is present on the RCs after nuclear envelope breakdown ( NEBD ) and by late anaphase had relocalized to spindle microtubules . Because a similar localization pattern is exhibited by AIR-2 [11] , an RC component previously suggested to be SUMO-modified [4] , we compared the behavior of these two proteins . Notably , the localization of these proteins differed in mid anaphase , with SUMO maintaining robust RC localization after AIR-2 relocalized to microtubules ( Fig 1A , row 4 ) , demonstrating that proteins other than AIR-2 are likely SUMOylated at this stage . Notably , in early anaphase spindles where a small population of AIR-2 had relocalized to microtubules , we saw that the microtubule-associated population of AIR-2 was not colocalized with SUMO ( Fig 1A , row 3 ) . These results suggest that if AIR-2 is SUMOylated when it is in the RC , this modification is either removed before AIR-2 relocalizes to microtubules or is undetectable in this small population . We also found that SUMO persisted in the RCs longer than separase/SEP-1 ( Fig 1B , row 2 ) , demonstrating that RC components leave the complex at different times and suggesting that the disassembly of these structures is a sequential process . In addition , we confirmed that UBC-9 ( SUMO E2 ) and GEI-17 ( SUMO E3 ) localize to the RCs as they form ( Fig 1C , rows 1–2 ) [4] , and we observed that they remain associated with these complexes until early anaphase ( Fig 1C , row 3 ) . However , in spindles where AIR-2 had relocalized from the RCs to the microtubules ( the stage at which the RCs are flattening and disassembling ) , the E2/E3 enzymes appeared diffuse across the spindle ( Fig 1C , rows 4–5 ) . These findings demonstrate that UBC-9 and GEI-17 removal from the RCs occurs around the time that the RCs lose structural integrity , consistent with the view that altering the SUMOylation status of the RC could play a role in disassembly . Given that RC disassembly appeared to be a stepwise process , we next set out to determine how early in anaphase this process was initiated . A recent study reported that following depletion of MEL-28 ( a nucleoporin responsible for targeting Protein Phosphatase 1/ PP1 to meiotic chromosomes ) , chromosomes separate at the metaphase to anaphase transition but spindles remain in an “early anaphase” configuration , where chromosomes are unable to move very far apart [16] . We therefore asked whether the disassembly of RCs was initiated before this stage , potentially due to their physical release from chromosomes , or after . To test this , we depleted MEL-28 and then assessed the localization of AIR-2; AIR-2 is a relevant marker since it is the first known RC component to leave the RCs in anaphase , and since RC disassembly and AIR-2 relocalization are thought to occur concurrently [2 , 15] . Following mel-28 ( RNAi ) , we found that AIR-2 was RC-associated in the majority of anaphase spindles ( 36/46 spindles; 78% ) ( Fig 2A ) , demonstrating that its relocalization to the spindle is not always triggered with anaphase onset . Moreover , SUMO colocalized with AIR-2 in these structures , demonstrating that the RCs retained multiple components under these conditions ( Fig 2B ) . These findings suggest that RC disassembly is not automatically triggered when the RCs are removed from chromosomes , and instead this process either requires MEL-28/PP1 function or relies on events after this point in early anaphase . Notably , while the RCs usually begin to flatten out as they disassemble in mid-anaphase , we noticed that following mel-28 ( RNAi ) they retained their ring-like shape ( Fig 2A and 2B ) , suggesting that they retained structural integrity despite their removal from chromosomes . Given our hypothesis that SUMO removal from the RCs promotes RC disassembly and our finding that SUMO localizes to the mel-28 ( RNAi ) stabilized RCs ( Fig 2B ) , we reasoned that SUMO may be required for maintaining the integrity of these structures . To test this idea , we took advantage of an experimental condition we discovered that resulted in spindles with a mixture of SUMOylated and unSUMOylated RCs . We were able to achieve this using a strain in which the SUMO E3 ligase GEI-17 is linked to an auxin-inducible degron tag . Long-term depletion of GEI-17 in this strain ( using a 4+ hour auxin incubation ) does not affect AIR-2 chromosomal localization , but completely prevents these AIR-2-marked rings from becoming SUMOylated [4] . In contrast , we found that shorter auxin treatments resulted in spindles where some of the AIR-2-marked RCs had substantial SUMO localization while SUMO was undetectable on others; the number of SUMOylated RCs covered the whole range of zero to six per spindle ( Fig 2C–2E; Supp . Fig 1 , 30 minute auxin incubation shown ) . Therefore , acute gei-17 depletion results in an “all or none” effect with regard to RC SUMOylation , with some RCs SUMOylated and others failing to either acquire or maintain SUMO; future experiments will be important to uncover the principles underlying this interesting switch-like behavior . However , relevant to the current study , this discovery enabled us to investigate the role of SUMO in anaphase RC stability , by combining acute GEI-17 depletion with mel-28 ( RNAi ) , so that we could compare the behavior of these SUMOylated and unSUMOylated RCs during the early anaphase arrest when the RCs are normally stabilized . Using this strategy , we found that SUMO has a role in early anaphase RC stabilization . First , while in prometaphase/metaphase there are both SUMOylated and unSUMOylated RCs , in anaphase we never observed RCs that did not contain SUMO ( Fig 2C and 2D ) , suggesting that RCs lacking SUMO do not maintain a ring-like structure once they are released from the chromosomes . Moreover , while in prometaphase/metaphase there were always six AIR-2-marked RCs per spindle , with a variable number of these containing SUMO ( average = 3 . 9 ) , in anaphase the average for both AIR-2-marked structures and SUMO-marked structures was similar ( average of 3 . 3 for AIR-2 and 3 . 4 for SUMO; Fig 2E ) , again suggesting that the RCs containing SUMO prior to anaphase onset are the only complexes that subsequently maintain their stability . We obtained similar results when we analyzed GEI-17-depleted spindles in the absence of mel-28 ( RNAi ) ( Supp . Fig 1 ) , demonstrating that the stabilization of SUMO-associated anaphase RCs was not dependent upon the mel-28 ( RNAi ) early anaphase arrest condition . During our analysis of GEI-17-depleted oocytes , we also made a surprising observation concerning AIR-2 release from chromosomes . After acute auxin-induced GEI-17 depletion , AIR-2 was retained on chromosomes in a significant number of anaphase spindles ( 12/60 ) , remaining associated with the inside surfaces of separating chromosomes ( a phenomenon never observed in wild-type anaphase ) ; we also noticed this phenotype following gei-17 ( RNAi ) ( 8/13 anaphases , 24 hour feeding RNAi used ) ( Fig 3A ) . We went on to test whether other CPC components also exhibit this retention on chromosomes after GEI-17 depletion , and we found that CSC-1 also remains chromosome-associated in anaphase , colocalizing with AIR-2 ( Fig 3B ) . This finding is exciting because it suggests a new role for SUMOylation in CPC release from chromosomes at the metaphase to anaphase transition , and it also illustrates the dynamic and complex nature of this modification during meiotic progression . Given that SUMOylation promotes RC stability , we next set out to identify factors that could remove this modification from the RCs during anaphase . In C . elegans , there are four SUMO proteases , ULP-1 , 2 , 4 and 5 [14] , which function to remove SUMO from target proteins [17] . Therefore , we depleted each of these proteins to assess whether any are required for RC disassembly . First , we assessed ULP-4 , since this protease was shown to regulate AIR-2 behavior in mitosis [14] . Interestingly , although ULP-4 localized faintly across the spindle and did not appear to localize to the RCs in either metaphase or anaphase , we observed some RC assembly defects upon ULP-4 depletion ( S2A and S2B Fig ) . While AIR-2 and SUMO were targeted to the structures , they were not properly connected to the chromosomes , often appearing stretched ( S2A Fig , arrow ) and sometimes seeming connected to RCs on other chromosomes ( S2A Fig , arrowhead ) . During anaphase , there were varying phenotypes; some spindles looked normal , while others had RC disassembly defects ( S2A Fig , row 3 ) or lacked SUMO altogether ( S2A Fig , row 4 ) . These findings implicate ULP-4 deSUMOylation activity in proper RC formation and could be indicative of a role for this protease in RC disassembly . However , since it is also possible that the RC disassembly defects could be a downstream consequence of the earlier metaphase defects , these experiments do not conclusively demonstrate an anaphase role for ULP-4 . Therefore , we turned our attention to the other proteases . Depletion of ULP-2 or ULP-5 did not have obvious effects on either metaphase RC morphology , AIR-2 anaphase behavior , or RC disassembly ( S2C Fig ) , so we did not characterize them further . Following long-term ulp-1 ( RNAi ) ( feeding RNAi for 5 days ) , we found that most spindles lacked SUMO ( Fig 4A , row 3 , S3A Fig , row 1 ) , consistent with a general role for ULP-1-family proteases in processing SUMO into a conjugatable form [17 , 18] . However , in some cases we observed persisting AIR-2 and SUMO structures in the center of the spindle in late anaphase ( Fig 4A , row 4 ) , suggesting that in the cases where SUMO achieved RC conjugation , RC disassembly was aberrant . Since this result potentially implicated ULP-1 in RC disassembly , we went on to partially deplete ULP-1 using 24–48 hour feeding RNAi , rationalizing that partial ULP-1 function would promote enough SUMO processing to allow us to more specifically assess a role for ULP-1 in anaphase ( Fig 4A , rows 6–8 ) . As predicted , a majority of spindles under these depletion conditions contained six SUMOylated RCs ( Fig 4B ) , and , consistent with our long-term depletion results , we observed instances of defective RC disassembly , with persisting structures in the center of the anaphase spindle containing AIR-2 and SUMO ( Fig 4A , row 7 ) . Other RC proteins such as BUB-1 and UBC-9 ( SUMO E2 ) also localized to these persisting structures ( S4A Fig ) , supporting the idea that this phenotype represents defective RC disassembly . Additionally , we also observed a small percentage of severely aberrant structures , in which chromosomes had segregated very far without extruding a polar body and SUMO and AIR-2 were faintly left behind at the center of what had been the spindle ( Fig 4A , row 8 ) ; we observed this same “unfinished meiosis” phenotype occasionally in our long-term depletion experiments ( Fig 4A , row 5 ) . These results demonstrate that ULP-1 is required for RC disassembly , AIR-2 relocalization to the microtubules , and completion of the meiotic divisions . Moreover , we found that ULP-1 constructs of varying lengths can deSUMOylate both AIR-2 and KLP-19 in vitro ( S5 Fig ) , two proteins previously hypothesized to be SUMOylated during RC assembly [4] . Although in vivo ULP-1 may have different or additional substrates , this result is consistent with the hypothesis that ULP-1 promotes RC disassembly by removing SUMO from an RC component or components . Given our evidence that ULP-1 plays a role in RC disassembly , we next assessed its localization . We found that ULP-1 localizes to the nuclear envelope in oocytes during diakinesis and then becomes RC-associated after NEBD ( Fig 5A , row 1–3 ) . ULP-1 then leaves the RCs by mid anaphase , the stage at which AIR-2 has relocalized to the microtubules and the RCs have flattened and are disassembling ( Fig 5A , row 5–6 ) . ULP-1 has a similar localization pattern during MII , with additional localization to spindle poles during metaphase II ( S3B Fig ) . These results suggest that ULP-1 is targeted to the RCs , where it could perform deSUMOylation event ( s ) in early anaphase to trigger disassembly . Note that we also observed a chromosomal population of ULP-1 in MI ( Fig 5A , rows 2–3 ) , but this staining was not fully removed after 5 day ulp-1 ( RNAi ) ( S3A Fig ) ; this is likely due to incomplete ULP-1 depletion in our RNAi conditions , but also opens the possibility that this localization is nonspecific . We also found that ULP-1 displayed kinetochore and spindle pole localization in the one-cell stage mitotic embryo ( S3C Fig ) , consistent with the previous demonstration that the SUMO pathway plays important roles in mitosis [14] . Next , we sought to understand how ULP-1 is targeted to the RCs . In a previous study , we assessed AIR-2 anaphase behavior in a range of depletion conditions , to characterize the response of oocytes to errors . Under most of these conditions , AIR-2 relocalization to microtubules was delayed but not prevented [15] . However , in the course of that analysis we found that depletion of RC component BUB-1 caused a severe defect in AIR-2 relocalization to microtubules , with persisting AIR-2 structures in the center of the anaphase spindle ( Fig 5B ) , reminiscent of ULP-1 depletion ( Fig 4A , row 7 ) . This suggested that BUB-1 may be more directly involved in RC disassembly , so we investigated a possible connection between BUB-1 and ULP-1 . Notably , these studies revealed that BUB-1 is required for proper ULP-1 localization; following bub-1 ( RNAi ) , ULP-1 retains its broad chromosomal staining but is no longer enriched on the RCs ( Fig 5C ) . Under these conditions , SUMO colocalizes with AIR-2 in the persisting structures at the center of late anaphase spindles during MI ( Fig 5D , row 1 ) , and these RC accumulations are also observed in the vicinity of the spindle if the oocyte is able to progress to MII ( Fig 5D , row 2 ) , similar to what we observed upon ULP-1 depletion ( Fig 4A ) . These SUMO/AIR-2 structures also contained other RC proteins , such as CSC-1 and UBC-9 ( E2 ) ( S4B Fig ) , suggesting that preventing ULP-1 localization to the RCs prevents SUMO removal from these structures , consequently inhibiting RC disassembly during anaphase . During this analysis , we noted that our bub-1 ( RNAi ) conditions resulted in a more severe phenotype than our ulp-1 ( RNAi ) conditions; we observed persisting AIR-2 structures in 26% of 24–48 hour ulp-1 ( RNAi ) anaphase spindles ( Fig 4A , row 7 ) , compared to 75% of bub-1 ( RNAi ) anaphase spindles ( Fig 5B ) , and AIR-2 was also completely excluded from microtubules following bub-1 ( RNAi ) . We speculate that the ULP-1 partial depletion conditions may allow for a small amount of active ULP-1 on the RCs . However , this difference could also indicate that BUB-1 might additionally be involved in AIR-2 regulation independent of ULP-1 . Taken together , these results support the hypothesis that ULP-1 is targeted to the RCs by BUB-1 , where it removes SUMO modifications in anaphase , facilitating RC disassembly . The finding that ULP-1 is present on RCs well before they disassemble next led us to explore the question of how ULP-1 is prevented from triggering RC disassembly prematurely . One possibility is that ULP-1 is inactive before anaphase and cannot remove SUMO from substrates at this stage . To test this hypothesis , we depleted ULP-1 and measured SUMO fluorescence intensity on the RCs prior to anaphase onset; we predicted that if ULP-1 is active then depletion of the protease would increase the amount of SUMOylation on metaphase RCs , which would be reflected by increased fluorescence . To ensure that any effects on SUMO levels were independent of a role for ULP-1 in SUMO maturation , we performed this experiment utilizing a worm strain expressing GFP::AIR-2 to mark the RCs and mCherry::SUMO ( GG ) , a form of SUMO that can be conjugated to substrates without processing by ULP-1 [14] . Given the expression of this conjugatable form of SUMO , ULP-1 depletion should in theory not affect SUMO availability for RC assembly , enabling us to probe a role for ULP-1 independent of its SUMO processing activity . After ULP-1 depletion ( via 44 hours of feeding RNAi ) , we found that although the GFP::AIR-2 intensity did not significantly change ( S6 Fig ) , the mCherry::SUMO ( GG ) intensity per RC was increased compared to control RCs ( Fig 6A , left ) . This result was also clear when we calculated the ratio of mCherry::SUMO ( GG ) to GFP::AIR-2 signal for each RC , to account for any subtle changes in the structure of particular RCs that might affect SUMO levels ( Fig 6A , right ) . These results suggest that ULP-1 is active prior to anaphase and capable of removing SUMO from substrates at that stage . We also tested the other three ULPs using the same assay and found that SUMO intensity did not significantly change after ULP-4 and ULP-5 depletion ( S7A and S7B Fig ) . However , ULP-2 depletion increased SUMO intensity ( S7C Fig ) , suggesting that while this protease does not appear to be required for overall RC assembly ( S2C Fig ) , it may more subtly regulate aspects of RC organization or function . In the course of the above experiments , we also made an unexpected discovery that further supports a pre-anaphase role for ULP-1 . When optimizing the ULP-1 partial RNAi conditions for this new strain , we noticed that longer depletions began to affect RC assembly despite the availability of conjugatable SUMO . For example , although all spindles following 48 hour RNAi contained six GFP-marked AIR-2 rings , nearly half of these spindles ( 30/62 ) had RC SUMOylation defects ( Fig 6B ) . Reminiscent of our gei-17 acute depletion results ( Fig 2C–2E , S1 Fig ) , this appeared to be a switch-like effect , with mCherry::SUMO ( GG ) localized robustly to some RCs but undetectable on others ( Fig 6B ) . This result further supports the idea that ULP-1 has a pre-anaphase role outside of maturing SUMO and suggests that RC-localized ULP-1 may contribute to the recruitment or maintenance of SUMOylated RC proteins . Interestingly , our results also suggested that 48 hour ULP-1 depletion affects AIR-2 levels , as RCs lacking SUMO appeared to have less AIR-2 ( Fig 6B , arrows ) . To quantify this effect , we measured the AIR-2 fluorescence intensity per RC after 48 hour ulp-1 ( RNAi ) and found that RCs lacking SUMO showed a significant decrease in the amount of AIR-2 fluorescence intensity compared to control spindle RCs ( Fig 6C , left ) . Additionally , the converse was also observed , with SUMOylated RCs showing an increase in the amount of AIR-2 present ( Fig 6B , right ) . These data illustrate that despite the fact that AIR-2 initially localizes to RCs before and independent of SUMO , the progressive recruitment and maintenance of AIR-2 may be dependent on ULP-1 and/or the SUMOylation state of the RC . Taken together these results suggest that ULP-1 not only plays a role in SUMO maturation , but also has an important role on RCs prior to anaphase , both in promoting RC assembly and then also acting to maintain proper RC SUMOylation levels once the complex assembles . We next hypothesized that since ULP-1 is active before anaphase and seems to compete for substrate with the E2/E3 enzymes , then removal of the E2/E3 enzymes in early anaphase could enable ULP-1 to remove SUMO modifications that trigger RC disassembly . Supporting this idea , we found that UBC-9 ( E2 ) leaves the RCs before ULP-1 in early anaphase ( GEI-17 ( E3 ) also leaves the RCs during this time ( Fig 1C ) , but we could not directly compare its localization to ULP-1 for technical reasons ) . When we co-stained early anaphase spindles with antibodies against ULP-1 and UBC-9 , we found spindles distributed equally into two categories: 1 ) spindles where both UBC-9 and ULP-1 were present on all six RCs or 2 ) spindles where there was significantly more ULP-1 than UBC-9; we never observed spindles with only UBC-9 present ( Fig 7A ) . These results support the view that prior to anaphase , RC SUMOylation is maintained by a balance between UBC-9/GEI-17 and ULP-1 activity . Then , removal of UBC-9 and GEI-17 from the RCs in early anaphase could enable ULP-1 to deSUMOylate RC components and trigger disassembly . We further hypothesized that if UBC-9 and GEI-17 were retained on RCs past early anaphase , the RCs would maintain their ring-like structures . To test this , we assessed whether these enzymes are retained when errors are present , since we previously demonstrated that in response to various meiotic errors and short temperature stresses , the oocyte delays AIR-2 relocalization to the microtubules and RC disassembly through mid to late anaphase [15] . Under these conditions , we found that the persisting RCs were strongly marked by SUMO , UBC-9 , GEI-17 ( Fig 7B ) , and ULP-1 ( Fig 5A ) , the latter three of which are normally absent from the RCs during this stage ( Figs 1C and 5A ) . This suggests that UBC-9 , GEI-17 , and ULP-1 are actively kept on the RCs during an error response to maintain a proper balance between conjugating and deconjugating activity , thus achieving a SUMOylation state that promotes RC stability .
In summary , our findings support a model in which dynamic remodeling of SUMO modifications drives a series of essential events during the meiotic divisions ( Fig 8 ) . First , SUMO is required for building the RC . SUMOylation events driven by UBC-9 and GEI-17 aid in RC assembly , enabling the targeting of other components to the structures [4] . Furthermore , we have demonstrated the importance of SUMO in promoting RC stability during anaphase . In prometaphase/metaphase , SUMO is not required for components such as those in the CPC to maintain a ring-like shape , likely since the chromosomes act as scaffolds at this stage . However , we found that SUMOylation is essential for maintaining the structural integrity of the RCs after they are released from the chromosomes , suggesting that it could act as a “glue” that provides structural support to the complex . This stabilization is important because it facilitates chromosome segregation . During early anaphase , chromosomes move towards spindle poles through microtubule channels [3 , 19] . Our previous work suggested that the RCs act as physical wedges within these channels , keeping them wide to allow chromosome movements during Anaphase A and also during an error response [15]; for this function , maintaining the stability and ring-like structure of the RCs is likely important . Our findings also demonstrate that RC disassembly in anaphase is an active process in which proteins are removed in a sequential manner from the RCs; this process is not automatically triggered when RCs are released from chromosomes and instead may rely on MEL-28 and/or PP1 function . Moreover , we have implicated deSUMOylation of RC component ( s ) as a major driving force . During prometaphase , ULP-1 is recruited to RCs by BUB-1 where it seems to compete with the E2/E3 enzymes for substrate . Then , at anaphase onset , removal of E2/E3 enzymes could shift this balance and allow ULP-1 to remove SUMO modifications that initiate the disassembly process . This would allow for AIR-2 relocalization to microtubules and for the RCs to lose structural integrity , flattening then breaking down , enabling the channels to close . Importantly , proper regulation and coordination of these events is essential for meiotic progression; in conditions where ULP-1 is not targeted to the RCs , RC proteins end up in persisting structures at the center of the spindle during anaphase and prevent the proper completion of the meiotic divisions . Interestingly , our studies suggest that the disassembly process is not driven by removing SUMO entirely from the RC , since 1 ) the SUMO signal persists longer than ULP-1 and other RC proteins such as SEP-1 and 2 ) the flattening RCs still have a SUMO signal . Therefore , we propose that removal of a small population of SUMO modifications from RC components in early anaphase helps to disengage specific protein-protein interactions and allows the RC to disassemble . This could also involve deSUMOylation events that allow for Ubiquitin-mediated degradation . Since we have shown that AIR-2 and KLP-19 can be substrates of ULP-1 in vitro , it is possible that deSUMOylation of one of these proteins is the key event required for RC disassembly . However , since the RC is a SUMO-SIM network [4] that has at least 12 known components [1 , 2 , 4 , 8 , 9 , 12 , 15] and may therefore contain other SUMOylated proteins , this process is likely more complex . Future studies building on this work will therefore be important to reveal principles underlying the dynamic remodeling of SUMO-SIM networks . Our data also demonstrate that SUMO remodeling not only regulates RC assembly and disassembly but could also serve to connect the RCs to the chromosomes and regulate their release in anaphase . First , we found that depletion of the SUMO protease ULP-4 led to defective RC morphology; although ring structures still formed , they often appeared to stretch off the chromosomes and link together . This result suggests that ULP-4 is required to create a stable connection between the RCs and the chromosomes . Since ULP-4 depletion did not affect the SUMOylation level of the RCs and the protease did not appear to localize to the RC , we think that it is somewhat unlikely that ULP-4 performs this function by regulating modifications on the RCs themselves . Alternatively , we hypothesize that because ULP-4 depletion affected the level of AIR-2 on the bivalents ( S7A Fig ) , its deSUMOylation activity instead promotes some other fundamental aspect of RC assembly . Notably , we also observed anaphase RC defects following ULP-4 depletion , raising the possibility that this protease is involved in RC disassembly alongside ULP-1 . However , the metaphase RC defects that we observed made it impossible to convincingly assign an anaphase-specific role for ULP-4; future studies will therefore be important to better understand ULP-4’s precise contributions to oocyte meiosis . Dynamic SUMO regulation also appears to be important for RC release from chromosomes . After GEI-17 depletion , we frequently observed that CPC components remained attached to chromosomes during anaphase , suggesting that release of the CPC from chromosomes is dependent on GEI-17-mediated SUMOylation . But given that AIR-2 normally loads onto chromosomes in the absence of SUMO and GEI-17 , these data suggest that the SUMOylation state of the RC is remodeled during metaphase to allow for CPC release . This could be a direct modification to the CPC just before anaphase to allow it to release from histone binding , but given that SUMO does not spread to the microtubules at the same time as the CPC , in this scenario the modification would have to be quickly removed before microtubule binding in early anaphase . This idea is reminiscent of prior work demonstrating that one component of the CPC , Survivin , is ubiquitinated and deubiquitinated to promote centromere binding and release during mitosis in HeLa cells [20] . Regardless of the specific modification regulating CPC release , our results are exciting because they suggest that even after RC assembly , the SUMOylation state of the RCs continues to be modified prior to anaphase onset in order to facilitate subsequent meiotic events . This idea that SUMO modifications are being dynamically remodeled throughout prometaphase and metaphase is also supported by our analysis of SUMO proteases . Depletion of either ULP-1 or ULP-2 increases the fluorescence intensity of SUMO on the RCs , implicating these proteases in the cleavage of SUMO from RC substrates . Moreover , ULP-1 depletion can also affect RC formation , independent of its role in maturing SUMO , demonstrating that SUMO remodeling is also likely occurring during RC assembly . These data provide further evidence that many enzymes are involved in the tight regulation of the SUMOylation states of RC proteins . In recent years , it has become apparent that SUMO plays an important role in the regulation of meiotic and mitotic progression , as many SUMOylated cell division proteins have been identified , SUMO localizes to the spindle in many organisms , and disruption in global SUMOylation and/or specific SUMOylated substrates generally results in severe spindle and/or chromosome segregation defects [21–29] . Given its reversible nature , SUMOylation is now appreciated as a useful post-translational modification for regulating protein localization and function during dynamic cellular processes such as cell division . Our study reinforces and expands upon this view , demonstrating a new role for SUMO in regulating anaphase progression in C . elegans oocytes by mediating the assembly and disassembly of an important protein complex . Moreover , our work reveals novel insight into the complexity of how SUMO/SIM networks can be regulated and remodeled , by targeting both SUMO E2/E3 enzymes and SUMO proteases to the protein complex to achieve fine-tuning and rapid changes in protein interactions . In addition to further examining how competition between conjugating and deconjugating enzymes regulates the overall SUMOylation state of the RC , it will be interesting to investigate how these enzymes achieve substrate specificity . Since the RC appears to be built in discrete layers [2] , one possibility is that access to substrates plays a key role in regulating substrate specificity . Additionally , phosphorylation states likely have a role in influencing protein targets , with the modification occurring on the enzyme itself and/or on the substrate RC proteins . Supporting this idea , 1 ) there are kinases in the RC ( AIR-2 , BUB-1 ) and 2 ) the phosphorylation state of many cell division proteins changes during the transition from metaphase to anaphase . Finally , many SUMO proteases have preferences for either mono-SUMOylation or various SUMO chain lengths , and this preference could act as another mode of regulation . In the future , it will be important to understand how SUMO E2/E3 enzymes and proteases act on specific substrates in order to facilitate changes in protein-protein interactions . Our study establishes the RC as an ideal model for addressing these questions , as it represents a protein complex whose progressive remodeling is regulated by a balance of SUMOylation and deSUMOylation activity . Future studies expanding upon this work will therefore not only uncover mechanisms acting to ensure the faithful segregation of chromosomes during oocyte meiosis but will also shed light on principles underlying the regulation of SUMO during dynamic cellular processes .
Throughout this manuscript , “wild-type” refers to EU1067 worms . “Control” refers to the RNAi vector control in the corresponding worm strain . All strains used in this study are listed below . EU1067: unc-119 ( ed3 ) ruIs32[unc-119 ( + ) pie-1promoter::GFP::H2B] III; ruIs57[unc-119 ( + ) pie-1promoter::GFP::tubulin] FGP29: gei-17 ( fgp1[GFP::FLAG::degron::loxP::gei17] ) I; ieSi38[sun-1promoter::TIR1::mRuby::sun-1 3’UTR + Cbr-unc-119 ( + ) ] IV SMW6: unc-119 ( ed3 ) ruIs32 [unc-119 ( + ) pie-1promoter::GFP::H2B] III; ruIs57[unc-119 ( + ) pie-1 promoter::GFP::tubulin]; him-8 ( me4 ) IV SMW23: fgpls [ ( pFGP79 ) pie-1promoter::mCherry::smo-1 ( GG ) + unc-119 ( + ) ]; ojIs50 [pie-1 promoter::GFP::air-2 + unc-119 ( + ) ] Feeding RNAi was performed as previously described [3 , 15 , 30 , 31] . Briefly , individual RNAi clones were picked from a feeding library [32 , 33] and grown overnight at 37°C in LB with 100μg/ml ampicillin . Overnight cultures were spun down , plated on NGM ( nematode growth media ) plates containing 100μg/ml ampicillin and 1mM IPTG , and dried overnight . Worms were synchronized by bleaching gravid adults and hatching isolated eggs overnight without food . For long-term RNAi ( ulp-1 , ulp-2 , ulp-4 , ulp-5 , and mel-28 ) , synchronized L1s were plated on RNAi plates and grown to adulthood at 15° for 5 days . When these RNAi conditions prevented proper germline formation or global SUMOylation , shorter-term RNAi was performed ( bub-1 , ulp-1 , and gei-17 ) . For these depletions , worms were grown on regular NGM/OP50 plates and then transferred to the RNAi plate 24–72 hours before preparing for immunofluorescence ( specific timepoints shown in figures and/or figure legends ) . Due to the inherent variability in the partial RNAi experiments , for gei-17 , bub-1 , and ulp-1 ( RNAi ) we have pooled multiple independent experiments ( and have performed multiple time points of feeding RNAi for bub-1 and ulp-1 ) so that quantifications shown in the figures accurately represent of all levels of depletion and degrees of phenotypic severity we observed . Note that strains can also differ in their RNAi efficiency , with strains carrying the GFP::histone transgene ( present in EU1067 ) being particularly RNAi-sensitive [34] , so we optimized RNAi conditions for each strain independently and only pooled data obtained using the same strain . We also assessed the level of depletion for different timepoints of RNAi for GEI-17 and BUB-1 by Western blot ( S8 Fig ) . We were unable to obtain a working antibody for ULP-1 Western blots . Immunofluorescence was performed as previously described [35] . Briefly , worms were picked into a drop of M9 buffer on poly-L-lysine slides and then cut to release oocytes . Slides were frozen in liquid nitrogen for 5–10 minutes , and then the coverslip was quickly removed . Embryos were fixed for 40–45 minutes in -20°C methanol , rehydrated in PBS , and blocked in AbDil ( PBS plus 4% BSA , 0 . 1% Triton X-100 , 0 . 02% Sodium azide ) . Primary antibodies were diluted in AbDil and incubated overnight at 4°C or at room temperature for 2 hours . Secondary antibodies were diluted in AbDil and incubated for 1 hour at room temperature . Hoechst 33342 ( Invitrogen ) was diluted 1:1000 in PBST ( PBS + 0 . 1% Triton X-100 ) and incubated for 10–15 minutes at room temperature . Slides were washed with PBST between antibody incubations and mounted in 0 . 5% p-phenylenediamine in 90% glycerol , 20mM Tris , pH 8 . 8 . The following antibodies were used for immunofluorescence: rabbit anti-AIR-2 ( 1:1000; gift from Jill Schumacher ) , rat anti-AIR-2 ( 1:1000 ) [15] , rabbit anti-SEP-1 ( 1:350; gift from Andy Golden ) , mouse anti-α-tubulin-FITC ( 1:500; Sigma ) , mouse anti-SUMO ( 1:500 ) , rabbit anti-ULP-1 ( 1:350 ) , rabbit anti-ULP-4 ( 1:300 ( S2B Fig row 2 , 3 ) or 1:1500 ( S2B Fig row 1 ) ) , sheep anti-UBC-9 ( 1:800 ) , and rabbit anti-GEI-17 ( 1:600 ) ( SUMO , ULP-1 , ULP-4 , UBC-9 , and GEI-17 antibodies were gifts from Ronald Hay ) . Alexa-fluor conjugated secondary antibodies ( Invitrogen ) were used at 1:500 . CSC-1 and BUB-1 antibodies were generated by Covance in rabbits using recombinant GST-BUB-1 ( amino acids 287–661 ) and GST-CSC-1 ( full length ) proteins ( purification described in the next section ) . Serum was then affinity purified and antibodies were used at 1:1500 ( BUB-1 ) and 1:1000 ( CSC-1 ) . His-tagged SMO-1 , UBC-9 , and GEI-17 were purified using previously published methods [36] except that after Ni resin purification the proteins were loaded onto a size-exclusion column ( AKTA prime plus , HiLoad 16/600 Superdex 200pg ) , and the protein was collected and concentrated . ULP-1 catalytic domain ( “ULP-1 CD” , amino acids 470–697 ) and a longer fragment of ULP-1 ( amino acids 284–697 ) were made by cloning the corresponding nucleotide sequence into a 6X his-tagged modified pET vector , transforming into BL21 DE3 cells , and growing at 20°C overnight after inducing with 0 . 5mM IPTG . The two proteins were purified following previously published methods for the ULP-4 catalytic domain [36] . We also attempted to generate full-length ULP-1 but had difficulties with low expression levels and severe degradation . GST-AIR-2 , GST-KLP-19 ( amino acids 371–1084 ) , GST-BUB-1 ( amino acids 287–661 ) , GST-CSC-1 , and GST were made using the pGEX-6P-1 vector , and by transforming into BL21 DE3 pLysS Escherichia coli cells . Cultures were induced with 0 . 2mM IPTG and grown overnight at 20°C . After harvesting , cells were resuspended in a buffer containing protease inhibitors and 1X PBS , 10mM EGTA , 10mM EDTA , 1mM PMSF , 0 . 1% Tween , and 250mM NaCl , lysed by sonication , centrifuged for 40 minutes at 11 , 000K , and the supernatant was rotated with GST resin for 1 . 5–2 hours . The proteins were purified using a batch method , by rotating for 10 minutes with the appropriate buffer and spinning resin down at 2100rpm . Resin was washed 3X using a buffer containing 1X PBS , 250mM NaCl , 1mM DTT , 1mM PMSF , and 0 . 1% Tween . Protein was eluted using a buffer containing 50mM Tris pH 8 . 1 , 10mM reduced glutathione , and 75mM KCl and concentrated . SUMOylation reactions were performed using 2mM ATP , 5μg his-SMO-1 , 200nM his-UBC-9 , 12 . 5 or 50nM his-GEI-17 , and 100ng of SAE1/SAE2 ( Boston Biochem ) . 1μg of substrate protein ( GST , GST-AIR-2 , or GST-KLP-19 ) was used for each reaction . The reactions were performed in 50mM TrisHCl pH7 . 5 containing 5mM DTT , 5mM MgCl2 , and incubated for 4 hours at 37°C . Immediately following this reaction , an aliquot was removed for a gel sample , and then the deSUMOylation assays were performed by adding 1μM his-ULP-1 CD or his-ULP-1 ( amino acids 284–697 ) directly to the SUMOylation reaction tube and incubating at 37°C for an additional hour . The following antibody concentrations were used for Western blotting: mouse anti-SUMO ( 1:10 , 000; gift from Ronald Hay ) , rat anti-AIR-2 ( 1:5000 ) , and rabbit anti-GST ( 1:200; Santa Cruz ) . Rabbit KLP-19 antibody was generated by Covance using GST-KLP-19 stalk ( amino acids 371–1084 ) , and then affinity purified and used at 1:5000 . SMW23 worms were grown at 15°C for optimal RNAi efficiency but transferred to 25°C 16 hours prior to the experiment for optimal mCherry::SUMO ( GG ) expression . Worms were picked into a 7μl drop of 15°C M9 on a slide , and then the M9 was wicked away . A 10μl drop of 100% ethanol was placed on the worms , the drop was evaporated , and this was repeated twice . The slide was mounted using a solution of 50% diluted Hoechst ( 1:1000 in M9 ) and 50% mounting media ( 0 . 5% p-phenylenediamine in 90% glycerol , 20mM Tris , pH 8 . 8 ) . Slides were imaged within 48 hours of preparation . Every repetition had its own control performed and imaged at the same time . Images were deconvolved , and a sum projection was made from 11 slices encompassing an entire ring structure . One to three ring structures ( from different sum projections ) were analyzed per image , with the limitation being that a single ring had to be separable from other rings . A 22x20 pixel box was drawn around an area encompassing the RC , and the mCherry::SUMO ( GG ) and GFP::AIR-2 intensity of that area was recorded . The final SUMO and AIR-2 intensity values were then calculated by subtracting a 22x20 square of background SUMO or AIR-2 intensity from the ring SUMO or AIR-2 intensity . Worms were picked into a drop of 30°C M9 , incubated for 5 minutes at 30°C , and prepared for immunofluorescence as described above . Auxin was diluted in M9 to 1mM from a 400mM stock solution on the day of the experiment . Worms were picked into a drop of 1mM auxin ( or M9 for control experiments ) and incubated in a humidity chamber at 15°C for 20 , 30 , or 45 minutes . Worms were then cut and prepared for immunofluorescence as described above . All imaging was performed on a DeltaVision Core deconvolution microscope with a 100X objective ( NA = 1 . 4 ) ( Applied Precision ) . This microscope is housed in the Northwestern Biological Imaging Facility supported by the NU Office for Research . Images were obtained at 0 . 2μm z-steps and deconvolved ( ratio method , 15 cycles ) using SoftWoRx ( Applied Precision ) . All images in this study were displayed as full maximum intensity projections of data stacks encompassing the entire spindle structure unless otherwise noted . Although sometimes the displayed maximum projection image does not accurately show the number of RCs/chromosomes present since structures from different z-stacks can appear to merge , for our RC quantifications we analyzed the entire z-stack , examining SUMO and AIR-2 staining separately ( in grayscale ) and also together ( as a merge ) in order to accurately count and make claims about these structures . Entire stacks for two example images are shown in S1 and S2 Movies , to illustrate this quantification method . For SUMO and AIR-2 fluorescence intensity experiments in Fig 6 and S6 and S7 Fig , all data points for a given condition were compared with the control data points using a two-tailed t test . Data distribution was assumed to be normal , but this was not formally tested . 90 adult worms were picked onto empty plates , washed , and spun down in cold M9 twice . The M9 was reduced to approximately 20μl , then 20μl of 2x SDS sample buffer was added to the worms , and the sample was boiled for 10 minutes . The 40μl sample was run in a single lane . Westerns were probed with either rabbit anti-GEI-17 ( 1:5000 ) , rabbit anti-BUB-1 ( 1:10000 ) , or mouse anti-tubulin ( 1:5000 ) as the loading control .
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Most cells have two sets of chromosomes , one from each parent . Meiosis is a specialized form of cell division where chromosomes are duplicated once and segregated twice , in order to generate eggs ( oocytes ) or sperm with only one copy of every chromosome . This is necessary so that fertilization will produce an embryo that once again contains two complete copies of the genome . Using C . elegans as a model system , we have uncovered regulatory mechanisms important for the fidelity of these meiotic divisions . C . elegans oocytes use a kinetochore-independent chromosome segregation mechanism that relies on a large protein complex that localizes to the chromosomes . These protein complexes facilitate chromosome congression during metaphase and then are released from chromosomes in anaphase and progressively disassemble as the chromosomes segregate . We find that the stability and disassembly of these complexes is regulated by a protein modification called SUMO and by competition between enzymes that localize to the protein complex to either add or remove SUMO modifications . These findings provide insight into the mechanisms by which SUMO and SUMO enzymes regulate progression through cell division and illustrate a general strategy by which large protein complexes can be rapidly assembled and disassembled during dynamic cellular processes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
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"rna",
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"anaphase",
"metaphase",
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2018
|
Dynamic SUMO remodeling drives a series of critical events during the meiotic divisions in Caenorhabditis elegans
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Depletion of Wolbachia endosymbionts of human pathogenic filariae using 4–6 weeks of doxycycline treatment can lead to permanent sterilization and adult filarial death . We investigated the anti-Wolbachia drug candidate ABBV-4083 in the Litomosoides sigmodontis rodent model to determine Wolbachia depletion kinetics with different regimens . Wolbachia reduction occurred in mice as early as 3 days after the initiation of ABBV-4083 treatment and continued throughout a 10-day treatment period . Importantly , Wolbachia levels continued to decline after a 5-day-treatment from 91 . 5% to 99 . 9% during a 3-week washout period . In jirds , two weeks of ABBV-4083 treatment ( 100mg/kg once-per-day ) caused a >99 . 9% Wolbachia depletion in female adult worms , and the kinetics of Wolbachia depletion were recapitulated in peripheral blood microfilariae . Similar to Wolbachia depletion , inhibition of embryogenesis was time-dependent in ABBV-4083-treated jirds , leading to a complete lack of late embryonic stages ( stretched microfilariae ) and lack of peripheral microfilariae in 5/6 ABBV-4083-treated jirds by 14 weeks after treatment . Twice daily treatment in comparison to once daily treatment with ABBV-4083 did not significantly improve Wolbachia depletion . Moreover , up to 4 nonconsecutive daily treatments within a 14-dose regimen did not significantly erode Wolbachia depletion . Within the limitations of an animal model that does not fully recapitulate human filarial disease , our studies suggest that Wolbachia depletion should be assessed clinically no earlier than 3–4 weeks after the end of treatment , and that Wolbachia depletion in microfilariae may be a viable surrogate marker for the depletion within adult worms . Furthermore , strict daily adherence to the dosing regimen with anti-Wolbachia candidates may not be required , provided that the full regimen is subsequently completed .
Onchocerciasis and lymphatic filariasis are neglected tropical diseases that are caused by the filarial nematodes Onchocerca volvulus and Wuchereria bancrofti , Brugia malayi and Brugia timori , respectively [1] . During onchocerciasis vision loss and severe dermatitis can occur , whereas one third of lymphatic filariasis patients develop lymphedema and hydrocele [2 , 3] . Due to the severity of these debilitating diseases that present a huge socioeconomic problem in endemic countries , the World Health Organization ( WHO ) has targeted both diseases for elimination [2 , 3] . Therefore , mass drug administrations have been performed over the last decades with ivermectin for onchocerciasis in sub-Saharan Africa , ivermectin plus albendazole for lymphatic filariasis in sub-Saharan Africa , and diethylcarbamazine ( DEC ) plus albendazole for lymphatic filariasis outside of Africa [2 , 3] . However , these regimens are mainly microfilaricidal , leading to the loss of the filarial offspring , the microfilariae . They also temporarily inhibit the embryogenesis of the female adult worms , but do not kill the adult worms , i . e . produce a macrofilaricidal effect . Therefore , these treatments need to be given on an annual or biannual basis to reduce transmission of the filariae for the reproductive life-span of the female adult worms , which can be as long as 15 years for onchocerciasis . Recently , moxidectin , which demonstrates a prolonged clearance of microfilariae in comparison to ivermectin , was registered as a new drug for onchocerciasis , providing the potential to more effectively reduce transmission between treatment rounds and accelerate elimination of onchocerciasis [4] . Furthermore , administration of a triple therapy consisting of ivermectin , DEC and albendazole was introduced for lymphatic filariasis , and it is now recommended by the WHO in areas non-endemic for onchocerciasis [5] , as it leads to a prolonged depletion of the microfilariae and may also provide macrofilaricidal efficacy [6 , 7] . However , potential drug-induced serious adverse events in areas co-endemic for the filarial nematode Loa loa in the case of moxidectin as well as L . loa and O . volvulus [8] in the case of the triple therapy , may hamper the implementation of those treatments in those areas . Furthermore , with lower endemicities of onchocerciasis and lymphatic filariasis , the cost effectiveness of community-based MDA programs is reduced [9] , and modelling studies suggest that the reduction of required treatment rounds by triple therapy will be less significant in areas of lower endemicity [10] . Therefore , new macrofilaricidal drugs are needed for case management and for clearance of residual foci in order to eliminate onchocerciasis and lymphatic filariasis . Such macrofilaricidal efficacy can be achieved by drugs that target Wolbachia endosymbionts , which are present in most human pathogenic filariae , including those causing lymphatic filariasis and onchocerciasis , but , notably , not in L . loa . Treatment of either onchocerciasis or lymphatic filariasis patients with doxycycline for 4–6 weeks has been shown to deplete Wolbachia bacteria , leading to permanent sterilization of the female worms and providing a slow and therefore safe macrofilaricidal effect [11–16] . However , the need for prolonged treatment ( at least 4 weeks ) and contraindications in children under age 8 and in pregnant and lactating women prevent the broader use of doxycycline for filarial diseases . ABBV-4083 is a tylosin analogue which has improved potency and oral bioavailability compared to tylosin and has been shown to be efficacious in rodent models using the filarial nematodes Litomosoides sigmodontis , B . malayi and Onchocerca ochengi [17 , 18] . Thus , ABBV-4083 represents a promising anti-Wolbachia candidate , and its safety in humans has recently been assessed in a phase 1 clinical trial ( https://www . dndi . org/diseases-projects/portfolio/abbv-4083/ ) . In the current studies we addressed several points that are important for informing the design of phase 2 clinical trials of anti-Wolbachia candidates . These involved i ) the determination of the kinetics of Wolbachia depletion in adult worms during and after treatment; ii ) the investigation of alternate dosing regimens , i . e . the comparison of a once ( QD ) and twice daily ( BID ) treatment; iii ) the assessment of whether Wolbachia depletion in microfilariae can be used as surrogate marker for the depletion in adult worms; and iv ) the impact of a later catch-up of missed treatment days . For these experiments we used the L . sigmodontis rodent model [19 , 20] , as L . sigmodontis harbors Wolbachia endosymbionts and was previously used for pre-clinical studies of direct-acting and Wolbachia-targeting drug candidates , with good predictivity of the parasitological outcomes of later doxycycline clinical trials [21–27] . In the L . sigmodontis model , infective L3 larvae are transmitted during the blood meal of the tropical rat mite Ornithonyssus bacoti . The larvae migrate to the thoracic cavity , where they molt into adult worms within 30 days after infection ( dpi ) [28] . Starting ~8 weeks after infection , female worms release microfilariae , which circulate in the peripheral blood . Experiments were performed in L . sigmodontis-infected mice with treatment start after the molt into adult worms ( 35/36dpi ) and in L . sigmodontis-infected jirds after the development of microfilaremia ( 3–4 months after infection ) . Two different rodent hosts were chosen , as only 50% of BALB/c mice develop microfilaremia , which is comparable to human lymphatic filariasis [29] , and BALB/c mice clear the infection around 100 dpi [20] . In contrast , the susceptibility of jirds to infections with L . sigmodontis is increased , leading to the development of microfilaremia in the majority of animals and infections that last for more than one year [20] . Thus , jirds were used in our long-term experiments investigating microfilariae and embryogenesis , whereas mice were initially used to test the drug-efficacy against the female adult worms . Our studies reveal that Wolbachia reduction occurs as soon as 3 days after treatment onset and continues during the weeks following the end of treatment . Wolbachia depletion by ABBV-4083 was not significantly improved by BID treatment in comparison to QD treatment . The kinetics of Wolbachia depletion in microfilariae coincide with the Wolbachia depletion in female adult worms , indicating its value as potential early clinical marker of efficacy . Finally , missed treatments could be given at later time points without impairing the Wolbachia depletion and parasitological outcome .
All animal experiments were performed in accordance with the European Union Directive 2010/63/EU and were approved by the Landesamt für Natur , Umwelt und Verbraucherschutz , Cologne , Germany ( AZ 84–02 . 04 . 2015 . A507 ) . Animal welfare was scored on a scale from A-C for symptoms considering appearance , injuries , weight loss , and behavior . A score of A required daily observations of the symptoms , a score of B , consultation of the project leader or a veterinarian , and a score of C the immediate euthanization of the affected animal . For euthanization , animals were exposed to an overdose of isoflurane . Compound ABBV-4083 , Lot 2263872–0 , was used for testing and provided by AbbVie . Doxycycline ( Sigma Aldrich , St . Louis , MO , USA ) was used as control and was stored at 4°C and suspended shortly before application in distilled water . ABBV-4083 was also stored at 4°C and homogenized in 0 . 5% hydroxypropylmethylcellulose/0 . 02% Tween 80 ( Sigma Aldrich ) at 4°C overnight on a magnetic stirrer . This preparation was aliquoted , stored at -20°C and used for up to 5 subsequent treatment days . Female jirds ( Meriones unguiculatus ) and female BALB/c J mice were obtained from Janvier ( Saint-Berthevin , France ) and were housed in individually ventilated cages at the animal facility of the Institute of Medical Microbiology , Immunology and Parasitology , University Hospital Bonn , with free access to food and water . The animals were maintained on a 12h day/night cycle at 20–26°C and a humidity of 30–70% . For enrichment , animals were provided wooden sticks and nestlets . Female BALB/c mice ( 8–10 weeks old ) were naturally infected by exposure to mites ( O . bacoti ) containing L . sigmodontis L3 larvae . The same batch of mite-containing bedding was used to infect all animals of one study design at one time point as previously described [30] . Treatment started 35 or 36dpi for all study designs in mice and all treatments were given by oral gavage at the indicated doses either QD or BID . In total , three mouse studies were performed: Mouse study I: Four experimental groups were included in this study . Two groups received 75mg/kg QD ABBV-4083 ( n = 6/group ) for 3 or 7 days , whereas two additional groups were treated QD for 3 or 7 days with an equal volume of vehicle ( n = 6 ) . In this experiment blood was obtained 1 and 7 h after the first and last morning gavage and pipetted onto DBS filter cards ( Whatman 903 Protein saver card , Sigma-Aldrich , Germany ) for pharmacokinetic analyses . Vehicle and ABBV-4083 groups were euthanized 38 and 42dpi . Mouse study II: Ten experimental groups were included in this study . Two groups received 75mg/kg QD ABBV-4083 ( n = 6/group ) for 5 days , two groups received 75mg/kg QD ABBV-4083 ( n = 6/group ) for 10 days and two groups 75mg/kg BID ABBV-4083 ( 150mg/kg/day; n = 6/group ) for 5 days . Doxycycline ( n = 6 ) was administered at the human bioequivalent dose of 40mg/kg BID for a suboptimal duration of 10 consecutive days . Three groups of control mice were treated once daily for 5 and 10 days with an equal volume of vehicle ( n = 6 ) . Blood was obtained by puncture of the facial vein 1 and 7 h after the last morning gavage and pipetted onto DBS filter cards for pharmacokinetic analyses . Necropsy and further analyses were performed one day after treatment end at 40 , 45 , and 63dpi ( 5 , 10 and 28 days after treatment start ) . Vehicle-treated mice were euthanized 40 , 45 , and 63dpi , doxycycline controls at 63dpi . Mouse study III: Five experimental groups were included in this study . Three groups received 43 , 50 , or 75mg/kg QD ABBV-4083 ( n = 5/group ) for 5 days , whereas one group was treated for 5 days with 43mg/kg BID ABBV-4083 ( n = 5/group ) . Controls received an equal volume of vehicle twice a day for 5 days ( n = 5 ) . In this experiment blood was obtained 2 , 8 and 24 h after the first morning gavage ( before the 8h and 24h treatment ) and pipetted onto DBS filter cards for pharmacokinetic analyses . Vehicle and ABBV-4083 groups were euthanized 41dpi . Female jirds ( 8–9 weeks old ) were naturally infected with L . sigmodontis L3 larvae by exposure to infected O . bacoti mites , using the same batch of mite-containing bedding to infect all animals of one study . At 11 weeks after infection ( wpi ) , infected jirds were checked for peripheral microfilarial counts and microfilaremic animals were allocated to the different treatment groups . Treatment of jirds was initiated 12-15wpi and all treatments were given by oral gavage . Jird study I: Six experimental groups were tested in this study . Group 1 received 100mg/kg QD ABBV-4083 for 7 consecutive days ( n = 6 ) , and groups 2–4 received 100mg/kg QD ABBV-4083 for 14 consecutive days ( n = 6 per group ) . Group 5 received 40mg/kg BID doxycycline for a suboptimal duration of 14 consecutive days ( n = 6 ) . Group 6 received an equal volume of ABBV-4083 vehicle and was used as control ( n = 6 ) . Necropsy of group 1 was performed 1 week after treatment start ( wpt ) , group 2 at 2wpt , group 3 at 4wpt and groups 4–6 at 14wpt . Jird study II: Seven experimental groups were tested in this study . Groups 1–6 received ABBV-4083 at 25mg/kg QD for 7 , 10 or 14 consecutive days ( n = 6–7 per group ) , 7 days with missed treatments on day 6 and 8 ( n = 6 ) , or 14 days with missed treatments on day 6 , 8 , 13 and 15 ( n = 6 ) . Missed treatments were given the following days so that the animals received 7 or 14 treatments in total over a period of 9 or 18 days , respectively . Controls received an equal amount of vehicle once per day for 14 days ( n = 6 ) and as a positive control jirds were treated for 14 days with 50mg/kg QD ABBV-4083 ( n = 6 ) . Necropsies were performed at 8wpt . At necropsy infection of mice and jirds was confirmed by flushing the pleural cavity with 1ml PBS and by screening for adult worms in the thoracic cavity as well as the peritoneum . Isolated worms were separated , sex determined , counted and individually frozen for subsequent Wolbachia analysis or stored in 70% ethanol for analysis of embryogenesis . For jird experiments , peripheral blood microfilariae levels were quantified at 1- to 2-week intervals by microscopy starting at 11wpi until the day of necropsy . For blood collection , the vena saphena was punctured and 10μL of peripheral blood were directly transferred into 190μl of red blood cell lysis buffer ( BioLegend , San Diego , CA , USA ) and stored at 4°C until analysis . After resuspension , 10μl of the suspension were transferred to a microscopic slide and microfilariae were counted from the complete slide using a microscope . If less than 10 microfilariae were counted , the tubes were centrifuged at 400g for 5 minutes , the supernatant was discarded , and the pellet resuspended and completely transferred to a microscope slide for counting . Depletion of endosymbiotic Wolbachia was determined by qPCR using primers for Wolbachia single copy gene ftsZ ( GenBank Accession No . : AJ010271 ) and normalized to the L . sigmodontis actin gene ( act ) ( GenBank Accession No . : GU971367 ) as previously described [27] . If present , 10 female worms per animal were frozen at -20°C for later analysis of the Wolbachia ftsZ/act ratio by duplex real-time PCR using Qiagen’s QuantiNova on a Rotorgene Q 5-Plex ( Qiagen , Hilden , Germany ) . The PCR was performed in triplicate . The following primer pairs ( MicroSynth; Switzerland ) and TaqMan probes ( biomers; Germany ) were used: LsFtsZ FW cgatgagattatggaacatataa , LsFtsZ RV ttgcaattactggtgctgc , LsFtsZ TQP 5’6-FAM cagggatgggtggtggtactggaa 3’TAMRA , LsActin FW atccaagctgtcctgtctct , LsActin RV tgagaattgatttgagctaatg , LsActin TQP 5’HEX actaccggtattgtgctcgatt 3’TAMRA . The qPCR consisted of 45 cycles with a melting temperature of 95°C for 5 sec and an annealing temperature of 58°C for 30 sec . The standard curve used was a mix of LsFtsZ and LsActin plasmids . For Wolbachia quantification from microfilariae , 50μL of peripheral blood were lysed with 950μL RBC lysis buffer ( Biolegend ) at room temperature for at least 5 minutes . After centrifugation at 400g , the supernatant was discarded , and the pellet resuspended in 200μl AE buffer ( Qiagen ) . A known number of peripheral blood microfilariae ( if present 10–30 microfilariae ) were stored in AE-buffer at 4°C until analysis . The PCR was performed as described for the adult worms . As microfilariae are less variable in size as adult worms , ftsZ values per microfilaria are shown . ABBV-4083 efficacy was determined based on the reduction of Wolbachia ftsZ/act from female adult worms ( primary efficacy parameter ) . Secondary efficacy parameters included in jird experiments the reduction of Wolbachia ftsZ gene from microfilariae , the inhibition of embryogenesis and the clearance of microfilariae . Statistical analyses were performed using GraphPad Prism software Version 8 . 02 ( GraphPad Software , San Diego , USA ) . Distribution of data was tested for normality by d‘Agostino & Pearson test . Differences between multiple groups that were not normally distributed were tested for statistical significance using Kruskal-Wallis followed by Dunn’s multiple comparison test . Normally distributed data of multiple groups were tested for statistical significance using 1-way-ANOVA followed by Sidak’s multiple comparison test . p values ≤ 0 . 05 were considered statistically significant .
To assess the kinetics of Wolbachia depletion in L . sigmodontis adult filariae during and subsequent to ABBV-4083 treatment , we conducted three studies in L . sigmodontis-infected mice . In the first , animals were treated at 35dpi with either a high dose ( 75mg/kg QD ) of ABBV-4083 previously shown to be effective or vehicle QD for 3 or 7 days ( Fig 1A ) . Necropsies were performed in the ABBV-4083 or vehicle treated animals one day after the last treatment ( 38 , 42dpi , respectively ) . ABBV-4083 concentrations in blood obtained at two separate time points after oral dosing in mice were comparable to those obtained in satellite pharmacokinetic studies at the same dose [18] . In the second study , two additional dosing durations were examined to more completely assess the acute kinetics of Wolbachia depletion . In addition , the impact of a washout period after the end of treatment was assessed . Mice received 75mg/kg ABBV-4083 for 5 days QD ( 1x 75mg/kg per day ) with necropsies either one day after the last treatment ( 40dpi ) or a washout period of ~3 weeks ( 63dpi; Fig 1A ) . Additional groups received ABBV-4083 QD for 10 days and were analyzed at 45dpi ( no washout ) and 63dpi ( washout ) . Separate vehicle controls for each necropsy day and doxycycline controls ( 40mg/kg BID 10 days ) with necropsy at 63dpi were included . ABBV-4083 treatment reduced endosymbiotic Wolbachia in a treatment duration dependent manner that correlated across both studies . After treatment end , 3 , 5 , 7 or 10 days of QD treatment reduced the Wolbachia ftsZ/act ratio compared to the respective vehicle control by 84 . 9% , 91 . 5% , 94 . 0% and 98 . 7% , respectively ( Fig 1B ) . Wolbachia levels continued to decline during the washout period , and at 63dpi , the 5- and 10-day QD treatment regimens reduced the Wolbachia ftsZ/act ratio compared to the respective vehicle control by 99 . 94% and 99 . 98% , respectively . All differences in Wolbachia burden by ABBV-4083 treatment compared to the respective vehicle controls were statistically significant ( p<0 . 01 after 3d treatment; p<0 . 001 after 5 , 7 , 10d treatment ) . Taken together , these results indicate that the Wolbachia levels decline in an approximately log-linear fashion between 3 and 10 days of treatment with ABBV-4083 and continue during the weeks following the end of treatment . As observed previously [18] , control animals receiving a suboptimal 10-day doxycycline treatment exhibited a significantly smaller reduction in Wolbachia ftsZ/act ratio ( 93 . 3% , p<0 . 001 ) compared to animals treated with ABBV-4083 for the same duration ( 10 days ) . Two additional arms in the second study above allowed the comparison of 5 days of BID dosing of ABBV-4083 to either 5 or 10 days of QD dosing , with necropsies either one day after the last treatment or after a washout period ( Fig 1A ) . At treatment end , 75mg/kg ABBV-4083 twice daily for 5 days produced a decline in Wolbachia only marginally better ( 94 . 8% compared to the respective vehicle control ) than the same dose once daily for 5 days ( 91 . 5% , p>0 . 99 ) , and inferior to the same total dose given over 10 days once daily ( 98 . 7% , p<0 . 05 , Fig 1B ) . Because of the high ( >99 . 9% ) responses at this dose , no statistically significant differences between ABBV-4083 dosing regimens were observed after the washout period . A third study in mice also compared QD and BID regimens , but at lower doses . Mice received 5 days of ABBV-4083 treatment with one of four regimens ( 43mg/kg QD , 43mg/kg BID , 50mg/kg QD or 75mg/kg QD ) at 36dpi and were necropsied at day 41dpi ( Fig 1C ) . Vehicle controls received 5 days of BID treatments . Five days of ABBV-4083 treatment reduced the ftsZ/act ratio in female worms in a dose dependent manner analyzed one day after treatment end at 41dpi ( Fig 1D ) . ABBV-4083 treatment at 43mg/kg QD reduced the Wolbachia ftsZ/act ratio in female adult worms by 64 . 2% , at 43mg/kg BID by 79 . 2% , at 50mg/kg QD at 77 . 6% and at 75mg/kg QD at 94 . 5% compared to vehicle treated controls . All differences in Wolbachia burden were statistically significant in comparison to vehicle controls ( p<0 . 001 ) . The above results indicate that in L . sigmodontis-infected mice , five days of once daily dosing of ABBV-4083 at 75mg/kg are sufficient to reduce Wolbachia levels by >90% and that depletion continues to >99% after a washout period . At both this and a lower dose , BID treatment led to a small , non-significant improvement in Wolbachia depletion in comparison to QD treatment regimens for the same duration . While the adult worm burden starts to naturally decline in L . sigmodontis infected BALB/c mice ~70dpi , ~3 weeks after the development of microfilaremia [20] , jirds are more permissive to long-term infections with L . sigmodontis , allowing an analysis for >5 months during patent ( microfilariae-positive ) infections [20] . We therefore assessed the effects of ABBV-4083 treatment on the Wolbachia depletion kinetics in female adult worms in jirds and compared it to the depletion observed in microfilariae , to ascertain the potential of Wolbachia depletion in microfilariae as a clinical surrogate indicator . Microfilariae positive jirds were treated QD with 100mg/kg ABBV-4083 for 7 or 14 days with necropsies at 1 , 2 , 4 and 14wpt . Controls received an equal volume of vehicle for 14 days QD or a suboptimal treatment with doxycycline ( 40mg/kg BID , 14 days ) and were analyzed at 14wpt ( Fig 2A ) . Treatment with ABBV-4083 reduced the Wolbachia levels in female adult worms in a time-dependent manner ( Fig 2B ) . At 1 , 2 and 4wpt , 92 . 5% , 97 . 0% and 99 . 7% of Wolbachia were depleted , respectively , in comparison to the vehicle controls . After an extended washout ( 14wpt ) the Wolbachia levels remained 99 . 9% depleted . Suboptimal two weeks of doxycycline treatment were not sufficient for long-term clearance of the Wolbachia: a 132% increase was observed in this group compared to the vehicle controls at 14wpt . In accordance with the Wolbachia depletion in female adult worms , Wolbachia ftsZ levels in microfilariae of the ABBV-4083 and doxycycline treated groups were reduced by 1wpt by 97 . 5 and 97 . 8% , respectively , with only one out of six animals in both groups having detectable Wolbachia in microfilariae ( Fig 2C ) . At 5wpt microfilariae from all 14-day ABBV-4083 and 14-day doxycycline treated animals had no detectable Wolbachia ftsZ . Whereas ABBV-4083 treated animals continued to have no detectable Wolbachia ftsZ levels until the end of the analysis at 13wpt , 1 out of 6 doxycycline treated animals had detectable Wolbachia ftsZ levels in microfilariae at 9wpt again and by 11 and 13wpt all microfilariae of doxycycline treated animals had detectable Wolbachia ftsZ levels ( mean Wolbachia ftsZ reduction of 96 . 1% , 25 . 4% and 20 . 4% at 9 , 11 and 13wpt ) , reaching levels equivalent to the start of the study . These data indicate that Wolbachia depletion in microfilariae temporally correlates with the depletion in female adult worms ( p = 0 . 02; r2 = 0 . 432 at 13/14wpt; Fig 2D ) , presenting a potential surrogate marker for the efficacy of Wolbachia-targeting compounds . In addition to the depletion kinetics of Wolbachia in adult worms and microfilariae , the impact of ABBV-4083 treatment on the clearance of peripheral microfilariae along with the inhibition of the embryogenesis of female adult worms over time was assessed . Before treatment start all jirds included in this study were microfilariae positive . At 11wpt the first animal treated for 14 days with ABBV-4083 ( QD 100mg/kg ) cleared all microfilariae from the peripheral blood . By 14wpt 5 out of 6 ABBV-4083 treated jirds were amicrofilaremic and the one remaining microfilariae positive jird had declined from 809 at baseline to 1 microfilaria / 10μl blood ( Fig 3A ) . Consistent with previous findings [18] , all suboptimal doxycycline treated animals were microfilariae positive at 14wpt . In accordance with the peripheral blood microfilariae counts over time , no marked reduction of the embryogenesis was observed one week after treatment onset with ABBV-4083 ( QD 100mg/kg ) , with similar ratios of eggs , morulae , pretzel and stretched microfilariae stages in the uteri of the analyzed worms as in the vehicle control ( Fig 3B ) . Four weeks after treatment onset with ABBV-4083 , a significant reduction in eggs ( p<0 . 01 ) , pretzel ( p<0 . 05 ) and stretched microfilariae ( p<0 . 01 ) stages occurred in comparison to the vehicle controls and at 14wpt , a complete lack of stretched microfilariae stages in the ABBV-4083 treated group was observed along with statistically significantly reduced early embryonal stages ( eggs & pretzel: p<0 . 001 , morulae p<0 . 01 ) . In comparison , suboptimal 2 weeks of doxycycline treatment reduced the number of early embryonal stages ( egg p<0 . 001 and morulae p<0 . 05 ) by 14wpt , whereas two female worms harbored stretched microfilariae at that time point . In summary , ABBV-4083 treatment in jirds cleared microfilariae from the peripheral blood , completely inhibited embryogenesis and reduced all embryonal stages by 14wpt . Since strict adherence to multi-day drug administration can present a challenge to successful therapy , we assessed the impact of missed treatments on the efficacy of ABBV-4083 in a preclinical model . In this study three groups of jirds received 25mg/kg ABBV-4083 QD for 7 , 10 or 14 consecutive days , respectively . Two other groups received discontinuous 7 and 14 treatments , skipping on days 6 and 8 , and days 6 , 8 , 13 and 15 , respectively , with once daily administration subsequently continuing to complete the regimen ( Fig 4A ) . A lower , suboptimal dose of 25mg/kg ABBV-4083 was used in order to allow the identification of less prominent changes in the efficacy of continuous and discontinuous treatments . Positive controls received ABBV-4083 QD at 50mg/kg for 14 consecutive days and vehicle treated animals served as negative control . Eight weeks after treatment start , jirds treated with ABBV-4083 QD at 50mg/kg for 14 days had a Wolbachia reduction of 99 . 8% in comparison to vehicle controls ( Fig 4B ) . Lower doses of ABBV-4083 QD at 25mg/kg depleted the Wolbachia ftsZ/act ratio in a treatment duration dependent manner by 47 . 3% , 67 . 9% and 98 . 4% after 7 , 10 and 14 consecutive days of treatment , respectively . Discontinuous daily treatment with 7 or 14 doses did not impair the Wolbachia depletions compared to continuous treatment , resulting in a reduction by 70 . 8% and 99 . 1% , respectively ( Fig 4B ) . Wolbachia depletion in microfilariae at 8wpt ( Fig 4C ) correlated with the Wolbachia depletion in the adult worms ( p<0 . 0001; r2 = 0 . 377 , Fig 4D ) , reaching a mean Wolbachia reduction in microfilariae of jirds treated with ABBV-4083 QD at 50mg/kg for 14 days of 100% and at QD doses of 25mg/kg for 7 , 10 and 14 consecutive days of 29 . 9% ( 6/6 animals with detectable Wolbachia in microfilariae ) , 90 . 6% ( 2/6 animals with detectable Wolbachia in microfilariae ) and 90 . 2% ( 1/7 animals with detectable Wolbachia in microfilariae ) , respectively . Non-consecutive treatments for 7 and 14 days resulted in a Wolbachia reduction in microfilariae of 88 . 9% ( 4/5 animals with detectable Wolbachia in microfilariae ) and 96 . 9% ( 1/6 animals with detectable Wolbachia in microfilariae ) , respectively . Although this study was not designed to continue long enough to fully ascertain the effect on peripheral microfilaremia , microfilariae levels started to decline at 8 weeks after treatment start ( Fig 4E ) , reaching statistical significance in animals treated for 14 days with 50mg/kg ABBV-4083 ( p<0 . 01 ) and 25mg/kg ABBV-4083 on 14 non-consecutive days ( p<0 . 05 ) . Consistent with this trend and with the Wolbachia reduction shown in Fig 4B , later embryonal stages ( pretzel and stretched microfilariae ) were absent in the majority of female adult worms isolated from animals that received 14 days of treatment ( Fig 4F ) . Reductions in later embryonal stages were independent of continuous or discontinuous ABBV-4083 treatment ( Fig 4F; absence of pretzel stages: 14-day continuous group 68 . 7% ( 11/16 worms ) , 14-day discontinuous group 100% ( 8/8 worms ) , 50mg/kg group 100% ( 6/6 worms ) ; absence of stretched microfilariae stages: 14-day continuous group absent in 93 . 7% ( 15/16 worms ) , 14-day discontinuous group 87 . 5% ( 7/8 worms ) , 50mg/kg group 100% ( 6/6 worms ) ) . The population of the early embryonic morula stage was also reduced in the groups treated for 14 days ( median reduction of 82% in 50mg/kg group; 87% in continuous group , 89% in discontinuous group ) compared to vehicle or 7-day treated animals ( median reduction in comparison to vehicle controls of 11% in continuous group and -13% in discontinuous group ) .
The present studies addressed several important aspects of the in vivo efficacy of the novel anti-Wolbachia agent ABBV-4083 [17 , 18] . Using highly suppressive doses , the kinetics of Wolbachia depletion in female adult worms were investigated and the potential of using Wolbachia depletion in microfilariae as surrogate marker was ascertained . Using lower doses , the pharmacological aspects of more or less frequent dosing were analyzed , including comparisons of BID and QD treatment as well as the effect of nonsequential daily dosing as a model of incomplete adherence . These results provide useful guidance for the design of future clinical efficacy studies with ABBV-4083 and subsequent anti-Wolbachia candidates for the treatment of filariasis . One limitation of these studies is the use of an animal model employing a surrogate filarial nematode to model human filarial infections . Factors like the increased life expectancy of the human filarial nematodes , differences in Wolbachia densities and differences in the pharmacokinetics of ABBV-4083 in rodents and humans , as well as accessibility of the drug to the filariae ( adult L . sigmodontis worms in the thoracic cavity vs . adult O . volvulus worms in subcutaneous nodules ) may impact the translation of our results to human filarial infections . Furthermore , treatments in mice were performed before the occurrence of nematode patency , which will not be the case for clinical trials in filariasis patients . Nevertheless , our studies provide important information for designing aspects of a clinical program for ABBV-4083 , including the rational selection of dosing regimens and the timing for investigation of Wolbachia depletion in microfilariae as a surrogate marker to ascertain both drug activity and possible bacterial recrudescence . Furthermore , although the filarial nematode utilized in our studies differs from the human pathogens , the target of drug activity ( Wolbachia ) is highly similar , giving relevance to the pharmacological aspects of ABBV-4083 investigated here . In addition , 200mg doxycycline for 4 weeks were previously shown to have significant macrofilaricidal efficacy in both lymphatic filariasis as well as onchocerciasis [1 , 31 , 32] , indicating that drug exposures may be sufficient in the two anatomical sites ( serous cavities/lymph and subcutaneous/deep tissue nodules ) to mediate Wolbachia depletion . Future clinical studies will confirm the extent to which our findings are recapitulated in human clinical studies of ABBV-4083 , and potentially with other anti-Wolbachia candidates . One goal of the current studies was to characterize the kinetics of Wolbachia reduction in adult female worms after oral ABBV-4083 treatment . In both mice and jirds , the Wolbachia load declined in a time-dependent manner , beginning as soon as 3 days after treatment start and continuing through 14 days of treatment in an approximately log-linear fashion . Wolbachia levels continued to decline in the weeks following the discontinuation of treatment , a finding that was also observed for in vitro Wolbachia depletion in insect cells by doxycycline [33] . The depletion of Wolbachia was associated with profoundly disrupted embryogenesis , resulting in sterilization of adult female worms and the ablation of microfilariae production and release . The observed clearance of peripheral microfilaremia by ABBV-4083 was comparable to previous studies in L . sigmodontis-infected jirds using the anti-Wolbachia candidates ABBV-4083 and AWZ1066S starting at 8wpt and reaching complete absence of peripheral microfilariae by 12-15wpt [17 , 18 , 23] . Kinetic analysis of the embryogenesis indicated that as early as 4wpt the number of stretched microfilariae within the uteri were reduced by >90% , consistent with a half-life of the microfilariae within the peripheral blood of around 3–4 weeks [34] and a drop of peripheral microfilariae loads beginning around 8wpt . In contrast to the complete absence of microfilariae after two weeks of ABBV-4083 treatment , suboptimal treatment for two weeks with BID doxycycline maximally lowered microfilariae levels by 98 . 6% at week 13 , followed by the onset of a rebound by 14wpt , consistent with the recrudescence of Wolbachia observed in the adult worms from animals in that group . Similar results were observed in previous L . sigmodontis jird studies that used the suboptimal two-week regimen of doxycycline as a treatment time-matched control for ABBV-4083 and AWZ1066S [18 , 23] . Similarly , human clinical studies have shown that at least 4 weeks of doxycycline therapy is required to achieve a macrofilaricidal effect in lymphatic filariasis and onchocerciasis patients [1 , 8 , 15 , 31 , 32] . These data indicate the importance of evaluating Wolbachia depletion over a period of several months in the jird model , as shorter observation periods may miss the recrudescence of Wolbachia and the rebound of microfilariae . Through use of this extended analysis period , our results confirm that two weeks of ABBV-4083 QD 75mg/kg treatment are superior in regard to Wolbachia depletion , microfilariae clearance and disruption of embryogenesis in comparison to a suboptimal two weeks of doxycycline treatment [18] . Since both ABBV-4083 ( a macrolide ) and doxycycline ( a tetracycline ) are expected to be bacteriostatic on the basis of inhibition of prokaryotic protein synthesis , the differences in kinetics observed in our studies is likely due to the substantial difference in in vitro potency between the two agents [18] rather than a consequence of mechanistic distinctions . The results of the current studies verify that the efficacy of ABBV-4083 is both dose- and treatment duration-dependent and provide the basis for exploration of 7- to 14-day regimens in clinical studies . Importantly , the kinetics of Wolbachia depletion in female adult worms was recapitulated by the Wolbachia depletion in microfilariae . Thus , already at 1wpt , Wolbachia were reduced in the majority of microfilariae of doxycycline or ABBV-4083 treated animals and no Wolbachia were detectable by 5wpt in both groups . This coincided with a Wolbachia depletion of 92 . 5% and 99 . 7% in female adult worms at 1 and 4wpt of ABBV-4083 treated jirds , respectively . Similarly , while microfilariae of ABBV-4083-treated jirds had no detectable Wolbachia through to the end of the study at 13wpt , microfilariae from suboptimal doxycycline treated animals displayed a rebound of the Wolbachia similar to that observed in the adult worms . The measurement of Wolbachia levels in microfilariae may thus be a suitable indicator for the Wolbachia depletion and recrudescence in adult worms , allowing for similar kinetic studies on the Wolbachia in microfilariae during human clinical trials without the need for repeated surgical removal of the nodules harboring adult worms from onchocerciasis patients . Our laboratories have previously sampled microfilariae from the circulation of bancroftian filariasis patients at 3–4 months after commencement of 2–4 week doxycycline regimens in order to determine anti-Wolbachia efficacy . These clinical data indicate that average anti-Wolbachia depletion levels in microfilariae in patient cohorts shortly following drug removal are indicative of long-term macrofilaricidal activity , with declines of >90% being predictive of eventual significant curative activity at 18 months [14 , 35 , 36] . Our present data corroborate that sampling microfilariae in the periphery will likely be an acceptable surrogate indicator of anti-Wolbachia efficacy within adult filarial worms following treatment of the clinical candidate ABBV-4083 , and thus may be predictive of long-term efficacy . A first proof of concept that Wolbachia can be determined from microfilariae of O . volvulus patients as well was previously demonstrated by our laboratory [37] . However additional work is needed to allow the measurement from human skin samples that contain only a single or few microfilariae . The results of our studies suggest that BID vs . QD dosing of ABBV-4083 produces only a modest increase in efficacy , equivalent to a small increase in QD dose , despite the short plasma half-life of ABBV-4083 in mice [18] . Instead , increasing the number of days of QD dosing appears to have a greater effect on efficacy than BID dosing for a shorter duration . This observation is consistent with the fact that Wolbachia is a slowly replicating organism ( doubling time 14h in insect cells [38] and much longer in filarial worms [39] , where they cannot continue to multiply in adult worms without overloading and damaging their host ) and the observations that Wolbachia levels continue to decline after dosing is completed . Thus , these studies provide support for investigation of a QD rather than BID dosing regimen of ABBV-4083 in clinical studies of efficacy irrespective of its half-life in humans . Finally , in the context of a regimen of 7 or 14 daily doses of ABBV-4083 , omitting 2 or 4 days of dosing , respectively , did not impair Wolbachia depletion in comparison to consecutive 7 or 14 daily treatments , provided that the missed daily doses were given after the originally planned end of treatment to complete the 7- or 14-day treatments with the regimen . This result may also be related to the slow replication rate of Wolbachia . This study was designed to mimic a pattern of incomplete adherence to a 7- or 14-day regimen in humans . The equivalent anti-Wolbachia efficacy of matched continuous and discontinuous groups in Fig 4B suggests that clinical efficacy may not be compromised by some missed doses , as long as the missed doses are given at the end of treatment , consistent with our clinical trials on doxycycline where we also allowed missed treatments to be completed at the end of treatment [15 , 40 , 41] . In this regard , instructing patients to complete the entire regimen with once daily dosing even if days are missed may be useful . In conclusion , our results demonstrate that treatment duration rather than BID vs . QD treatment primarily determines the efficacy of ABBV-4083 . Furthermore , they indicate that some degree of variable , incomplete adherence to the dosing regimen may be acceptable without seriously impairing the Wolbachia depletion efficacy , provided that the regimen is subsequently completed . The Wolbachia depletion was shown to occur within days after start of anti-Wolbachia therapy , and levels continued to decline during the weeks following the end of treatment . The depletion of Wolbachia is associated with profoundly disrupted embryogenesis , sterilization of adult female worms , and the ablation of microfilarial production and release . Finally , the correlation of Wolbachia decline and recrudescence between adult worms and microfilariae in this model provides a basis for exploration of Wolbachia levels in skin microfilariae as a surrogate indicator for anti-Wolbachia activity in O . volvulus infection .
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Onchocerciasis and lymphatic filariasis represent debilitating neglected tropical diseases that are caused by parasitic filarial nematodes . Current efforts to eliminate onchocerciasis are hampered by the lack of drugs that lead to permanent sterilization of the adult worms or provide a macrofilaricidal effect , i . e . kill the adult worms . In the past , doxycycline has been shown to deplete Wolbachia endosymbionts of filarial nematodes , leading to permanent sterilization and macrofilaricidal efficacy in filariae causing both onchocerciasis and lymphatic filariasis . However , contraindications and a requirement for at least 4 weeks of doxycycline treatment impair its broader use , creating a need for drugs with a shorter treatment regimen and potentially fewer contraindications . ABBV-4083 is such an anti-Wolbachia candidate that was efficacious in several filarial animal models and has recently been tested in a phase 1 clinical trial . The present studies addressed several points that are important for subsequent phase 2 clinical trials , namely the comparison of once vs . twice-per-day treatments , the impact of missed treatments , and a comparison of the kinetics of Wolbachia depletion in adult worms and microfilariae , the latter of which has the potential to be a surrogate indicator to avoid the necessity of surgically removing nodules with adult worms at repeated time points .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
[
"antimicrobials",
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"drugs",
"tropical",
"diseases",
"microbiology",
"parasitic",
"diseases",
"wolbachia",
"antimalarials",
"nematode",
"infections",
"developmental",
"biology",
"filariasis",
"pharmaceutics",
"antibiotics",
"neglected",
"tropical",
"diseases",
"pharmacology",
"onchocerciasis",
"bacteria",
"lymphatic",
"filariasis",
"doxycycline",
"helminth",
"infections",
"blood",
"anatomy",
"embryogenesis",
"physiology",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"drug",
"therapy",
"organisms"
] |
2019
|
In vivo kinetics of Wolbachia depletion by ABBV-4083 in L. sigmodontis adult worms and microfilariae
|
There is a worldwide upscale in mass drug administration ( MDA ) programs to control the morbidity caused by soil-transmitted helminths ( STHs ) : Ascaris lumbricoides , Trichuris trichiura and hookworm . Although anthelminthic drugs which are used for MDA are supplied by two pharmaceutical companies through donation , there is a wide range of brands available on local markets for which the efficacy against STHs and quality remain poorly explored . In the present study , we evaluated the drug efficacy and quality of two albendazole brands ( Bendex and Ovis ) available on the local market in Ethiopia . A randomized clinical trial was conducted according to the World Health Organization ( WHO ) guidelines to assess drug efficacy , by means of egg reduction rate ( ERR ) , of Bendex and Ovis against STH infections in school children in Jimma , Ethiopia . In addition , the chemical and physicochemical quality of the drugs was assessed according to the United States and European Pharmacopoeia , encompassing mass uniformity of the tablets , amount of active compound and dissolution profile . Both drugs were highly efficacious against A . lumbricoides ( >97% ) , but showed poor efficacy against T . trichiura ( ~20% ) . For hookworms , Ovis was significantly ( p < 0 . 05 ) more efficacious compared to Bendex ( 98 . 1% vs . 88 . 7% ) . Assessment of the physicochemical quality of the drugs revealed a significant difference in dissolution profile , with Bendex having a slower dissolution than Ovis . The study revealed that differences in efficacy between the two brands of albendazole ( ABZ ) tablets against hookworm are linked to the differences in the in-vitro drug release profile . Differences in uptake and metabolism of this benzimidazole drug among different helminth species may explain that this efficacy difference was only observed in hookworms and not in the two other species . The results of the present study underscore the importance of assessing the chemical and physicochemical quality of drugs before conducting efficacy assessment in any clinical trials to ensure appropriate therapeutic efficacy and to exclude poor drug quality as a factor of reduced drug efficacy other than anthelminthic resistance . Overall , this paper demonstrates that “all medicines are not created equal” .
Currently , there is a worldwide upscale in the implementation of programs to control and to eliminate a selection of 10 neglected tropical diseases [1 , 2] . Among these , soil-transmitted helminthiasis causes the highest burden on public health . It is estimated that more than 1 . 4 billion people were infected with at least one of the four STH species: the roundworm Ascaris lumbricoides , the whipworm Trichuris trichiura and the two hookworm species Necator americanus and Ancylostoma duodenale , resulting in a global burden of approximately 5 . 2 million disability-adjusted life years ( DALYs ) ( 20% of the total number of DALYs attributable to neglected tropical diseases ) [3 , 4] . To control the morbidity caused by STH , mass drug administration ( MDA ) of a single oral dose of a benzimidazole anthelminthic drug ( ABZ or MEB ) is recommended in communities where the prevalence of any STH exceeds 20% [5] . To date , major pharmaceutical companies such as GlaxoSmithKline ( ABZ , Zentel ) and Johnson and Johnson ( MEB , Vermox ) are donating these medicines to WHO , which subsequently distribute these medicines to its recipient countries . In Ethiopia , the donated medicines are made available for the patients through government hospitals and health centers . The therapeutic efficacy of these products at the WHO-recommended dosages ( i . e . single dose of 400 mg ABZ or 500 mg MEB ) has recently been evaluated in two consecutive multinational trials [6 , 7] . These trials showed that the therapeutic efficacy measured in terms of egg reduction rate ( ERR ) , varied both between drugs and STH species: both drugs showing high efficacy against A . lumbricoides ( > 98% ) and poor efficacy against T . trichiura ( ~64% ) , and ABZ being more efficacious against hookworms compared to mebendazole ( 96% vs . 80% ) . In addition to these two donated brands , there is a wide range of other brands available on local markets of STH endemic countries , which are often more accessible to the local people , but for which the efficacy or quality remain poorly explored [8] . The latter is particularly important in countries where resources are limited to monitor the quality of drugs , and hence in which prevalence of substandard , falsified or illegal drugs is substantial [9–14] . Although the quality of medicines has a direct influence on therapeutic efficacy , this remains poorly studied for benzimidazole anthelminthic drugs against STH infections . Therefore , we assessed both the efficacy and quality of two brands of ABZ commonly administered for the treatment of individual STHs in Ethiopia , namely Bendex and Ovis .
The study protocol was approved by the Ethical Committees of Jimma University ( Ethiopia ) ( reference no RPGC/282/2014 ) and of the Faculty of Medicine , Ghent University ( Belgium ) ( ref . no 2013/1114; B670201319330 ) . The study is registered under clinicaltrial . gov identifier number NCT02420574 ( https://clinicaltrials . gov/ct2/show/NCT02420574 ? term=NCT02420574&rank=1 ) . The school authorities , teachers , parents , and the children were informed about the purpose and procedures of the study . The written consent form was prepared in two commonly used local languages ( Afaan Oromo and Amharic ) and handed over to the children’s parents/guardians after explaining the aim , confidentiality and entire procedure of the clinical trial . Only those children ( i ) who were willing to participate and ( ii ) whose parents or guardians signed the written informed consent form were included in the study . Moreover , an additional separate written informed consent form for children older than 12 years was prepared , read and handed over to them and their additional written informed consent obtained ( S1 Checklist ) . Samples of the two ABZ brands ( Bendex , India , CIPLA Ltd , batch no: x21253 and Ovis , Korea , DaeHWa Pharmaceuticals , batch no: 2020 ) with a label claim of 400 mg/tablet and expiry date of November 2015 were purchased from private community pharmacy in Jimma town , Ethiopia . The quality of the drugs was evaluated by investigating three efficacy-critical quality attributes: ( i ) the mass uniformity , ( ii ) the amount of the active compound , and ( iii ) the dissolution of the tablets .
In total , 679 subjects were recruited of which 418 subjects were enrolled and randomized across the two brands of ABZ ( nBendex = nOvis = 209 ) . T . trichiura was the most prevalent ( 69 . 4% ) , followed by A . lumbricoides ( 53 . 6% ) . Hookworm infections were found in 28 . 2% of the subjects . In total 388 subjects completed the trial ( nBendex = 197; nOvis = 191 ) , resulting in a compliance rate of more than 90% . There was no significant difference in mean age ( Bendex: 10 . 3 years vs . Ovis: 10 . 3 years , p = 1 . 00 ) , sex ratio ( Bendex: 1 . 07 vs . Ovis: 0 . 87 , p = 1 . 00 ) and mean fecal egg count ( FEC ) ( A . lumbricoides: Bendex: 8 , 706 egg per gram of feces ( EPG ) vs . Ovis: 7 , 935 , p = 0 . 69; T . trichiura: Bendex: 909 EPG vs . Ovis: 769 , p = 0 . 45; hookworm: Bendex: 355 EPG vs . Ovis: 335 , p = 0 . 79 ) between the two arms at baseline . Both brands showed high efficacy against A . lumbricoides ( Bendex: 98 . 7% vs . Ovis: 97 . 8% , p = 0 . 64 ) , and poor efficacy against T . trichiura ( Bendex: 24 . 4% vs . Ovis: 20 . 4% , p = 0 . 81 ) . For hookworm infections , Ovis was more efficacious than Bendex , though the difference was marginally significant ( Bendex: 88 . 7% vs . Ovis: 98 . 1% , p = 0 . 05 ) . Based on the WHO criteria to classify the efficacy of anthelminthic drugs ( Table 1 ) , both brands had satisfactory and reduced efficacy against A . lumbricoides and T . trichiura , respectively . For hookworms , Ovis had a satisfactory efficacy , whereas Bendex had a doubtful efficacy . A pairwise comparison of baseline parameters and drug efficacy between Bendex and Ovis are presented in S1 Table .
Assessing the quality and in-vivo efficacy differences between different brands of ABZ tablets are very critical to avoid indiscriminate use of different brands that could influence intended therapeutic outcomes . In the present study , we evaluated comparative in-vivo efficacy and in-vitro quality of two commonly available brands of ABZ tablets ( Bendex and Ovis ) that are used to treat STH infection . The in-vivo efficacy results of two brands of ABZ against A . lumbricoides and T . trichiura determined in terms of ERR , suggest the susceptibility difference between the two STHs . Therapeutic efficacy of antihelmnthics can be influenced by various factors such as infection intensity and susceptibility of parasites . Thus the reduced efficacy of both brands against T . trichiura observed in the present study could be due to high level of infection intensity [17] and/or genetic modification of beta-tubulin gene [26 , 27] . Since concentration of API at the target site of the parasites could be low due to metabolism and/or limited absorption of the drug by the parasite [28 , 29] , the reduced efficacy of both brands against T . trichiura might also be associated with the pharmacokinetics of ABZ in the parasite . The reduced efficacy of the two brands against T . trichiura observed in the present study is comparable to the results reported in the previous studies [6 , 17] . The present finding , i . e . high prevalence of T . trichiura among other STHs in the study area together with the reduced efficacy results in ERR observed for single dose of ABZ 400 mg tablets , is supported by various literature findings [30–33] . This emphasizes the urgent need for alternative drugs and/or development of novel anthelmintic drugs to tackle this efficacy problem . Medicines quality is a critical factor that could affect efficacy of drugs against parasites [34] and for biopharmaceutical classification system ( BCS ) class II [35] drugs like ABZ that have low solubility and high permeability , dissolution is the rate-limiting step for drug absorption . Hence , in-vivo/in-vitro correlation between blood concentration profile and dissolution profile may be expected . Since the bioavailability of ABZ to the host is very low and also shows variability between individuals [36] , a decreased dissolution could significantly worsen bioavailability , which in turn diminishes in-vivo efficacy of both the parent drug and therapeutically active metabolite ( ABZ sulphoxide ) . Moreover , the capacity of anthelminthics to dissolve appropriately is an essential characteristic that allows proper drug uptake by the parasites and therefore assures the appropriate drug efficacy . Therefore , the four times decreased dissolution of Bendex compared to Ovis ( Fig 2 ) which could influence both local and systemic concentration is a plausible explanation for the efficacy difference between the two brands against hookworms , which are blood sucking parasites . Previous studies already indicated the differences in uptake and metabolism of benzimidazole drugs among different helminth species [28 , 29] , which may explain that the efficacy difference between the two brands was only observed against hookworms . Though mass uniformity and content of API per tablet are critical quality attributes that could influence efficacy , comparable quality of both brands with respect to mass uniformity and ABZ content observed in the present study ( Table 2 ) explain the efficacy difference between the two brands against hookworms is not associated with mass uniformity and content of API . Considering a single point dissolution specification for ABZ tablets as described in the USP , i . e . Q ≥ 80% dissolved in 30 min [22] , there is a statistically significant ( p ≤ 0 . 05 ) difference observed between Bendex ( Q = 20% ) and Ovis ( Q = 85% ) ( Fig 2 ) . Also , the area under the dissolution curve between defined time points ( 0 , 15 , 30 , 45 min ) or DE quantifies the poor in-vitro dissolution of Bendex . While Ovis thus complied to the USP dissolution specifications , Bendex on the contrary did not . Although an undesirable polymorphic solid state of albendazole could be one of the reasons for the difference in dissolution behavior of API [37–39] , the IR spectra ( Figs 3 and 4 ) of Bendex and Ovis showed no significant difference between the two brands . The absence of significant observed shifts in IR-absorption indicates the similarity in polymorphic form of ABZ in both brands . Thus , the significant difference in dissolution observed between the two brands could not be associated with different polymorphic forms of ABZ . Whatever the reason of dissolution difference is , e . g . excipient and processing manufacturing conditions and stability , the poor dissolution behavior of Bendex observed in the present study is in accordance with the previous reports in which 41 samples out of 72 samples of solid oral dosage forms including ABZ tablets , different generic formulations of albendazole tablets and carbamazepine immediate-release products did not comply with the established acceptance criteria [40–42] . In general , it is important to note that quality of medicines could be one of the factors influencing outcomes of clinical trials . For instance , literature indicates the association of poor quality of locally manufactured antimalarial drugs: Sulfadoxine-Pyrimethamine with clinical failure of malaria treatment in Pakistan [43] . Therefore , the results of the present study and a recent report by Newton and his colleagues [44] point to the requirement of guidelines for quality assurance of medicines used in clinical trials . Subjects lost at follow-up per STH species were the limitations of this study . In conclusion , this study demonstrated that the two investigated brands of ABZ tablets are efficacious against A . lumbricoides and hookworm while both brands had reduced efficacy against T . trichiura . However , there was a significant difference between the two brands of ABZ against hookworm . While both brands showed comparable tablet mass uniformity and albendazole content , the in-vitro dissolution release profile between the two brands was significantly different , explaining the clinical efficacy difference observed . The results of the present study underscore the importance of assessing the chemical and physicochemical quality of drugs before conducting efficacy assessment in clinical trials to ensure appropriate therapeutic efficacy and to exclude poor drug quality as a factor of reduced drug efficacy other than anthelminthic resistance . Our in-vivo efficacy study clearly indicates the importance of appropriate quality medicines .
|
Soil-transmitted helminths ( STHs ) infect millions of children worldwide . To fight STH , large-scale de-worming programs are implemented in which anthelmintic drugs ( either albendazole ( ABZ ) or mebendazole ( MEB ) ) are administered . However , there is a wide range of other brands , which are even more accessible , but for which the efficacy and quality remain poorly explored . We evaluated efficacy against STHs and quality of two ABZ brands commonly available on the local markets in Ethiopia ( Bendex and Ovis ) . Both brands showed high efficacy against roundworm infections and poor efficacy against whipworms . However , for hookworm infections , Bendex was significantly less efficacious than Ovis . In terms of drug quality , a significant difference was observed in the dissolution profile , with Bendex having a significantly slower dissolution rate than Ovis . Since dissolution behavior is critical for a drug to be appropriately absorbed into the helminth ( through host-blood and/or parasite-cuticle ) and produce therapeutic efficacy , the poor dissolution of Bendex compared to Ovis can explain the observed difference in efficacy against hookworms . Our results emphasize the importance of periodically assessing of drug quality to ensure appropriate therapeutic efficacy and to exclude poor drug quality as a potential factor of reduced drug efficacy other than drug resistance .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Assessment of Efficacy and Quality of Two Albendazole Brands Commonly Used against Soil-Transmitted Helminth Infections in School Children in Jimma Town, Ethiopia
|
Modeling the local absorption and retention patterns of membrane-permeant small molecules in a cellular context could facilitate development of site-directed chemical agents for bioimaging or therapeutic applications . Here , we present an integrative approach to this problem , combining in silico computational models , in vitro cell based assays and in vivo biodistribution studies . To target small molecule probes to the epithelial cells of the upper airways , a multiscale computational model of the lung was first used as a screening tool , in silico . Following virtual screening , cell monolayers differentiated on microfabricated pore arrays and multilayer cultures of primary human bronchial epithelial cells differentiated in an air-liquid interface were used to test the local absorption and intracellular retention patterns of selected probes , in vitro . Lastly , experiments involving visualization of bioimaging probe distribution in the lungs after local and systemic administration were used to test the relevance of computational models and cell-based assays , in vivo . The results of in vivo experiments were consistent with the results of in silico simulations , indicating that mitochondrial accumulation of membrane permeant , hydrophilic cations can be used to maximize local exposure and retention , specifically in the upper airways after intratracheal administration .
Local administration of therapeutic agents or bioimaging probes is commonly used to maximize concentrations at a desired site of action and to minimize side effects or background signals associated with distribution in off-target sites . However , in the specific case of inhaled , small molecule therapeutic agents or bioimaging probes , cell impermeant molecules may rapidly disappear from the sites of deposition via mucociliary clearance [1] , [2] . Conversely , cell- permeant small molecules can rapidly diffuse away and disappear from the site absorption , down their concentration gradient [3] . Therefore , we decided to explore an integrative simulation approach ( Figure 1 ) to study how the physicochemical properties of small molecule probes may be optimized to maximize local targeting and retention in the upper respiratory tract . Previously , we constructed multiscale , cell-based computational models of airways and alveoli to predict the relative absorption , accumulation and retention of inhaled chemical agents [4] . In these models , the transport of small molecules from the airway surface lining to the blood or from the blood to the airway surface lining were modeled using ordinary differential equations ( ODEs ) [5] , [6] . These ODEs described the transport of drug molecules across a series of cellular compartments bounded by lipid bilayers ( Figure 1A , ) , which form the surface of each airway generation , modeled as a tube ( Figure 1B ) . For a monoprotic base , the concentration of molecule in each subcellular compartment was divided into two components: neutral and ionized [7] , [8] . Accordingly , two drug specific properties were used as input to simulate the transport process across each lipid bilayer: the logarithms of the octanol∶water partition coefficient of the neutral form of the molecule ( i . e . , logPn ) and the pKa of the molecule . The logarithm of the octanol∶water partition coefficient of the ionized form of the molecule ( i . e . , logPd ) can be derived from logPn or it can be incorporated as an independent input parameter that can be measured or calculated with cheminformatics software . For different compartments with different pHs and lipid fractions , the free fraction of the neutral and ionized forms of molecules was calculated according to the molecule's pKa , logPn , and logPd , using the Henderson-Hasselback equation and the laws of mass action . Anatomically , the structure of the airways was modeled as a tree-like branching system of cylinders with progressively narrowing diameter [9] ( Figure 1C ) . Starting with the trachea as the trunk of the tree and ending in the alveoli as the leaves , each branching segment corresponded to an airway ‘generation’ characterized by a particular surface area , blood flow , and cellular organization [4] , [10] . Histologically , the walls of the airways or alveoli were modeled as multiple layers of epithelial , interstitial and endothelial cells separating the air from the blood . Several structural and functional differences between the airways and alveoli are noteworthy: 1 ) cartilage and smooth muscle are present only in the interstitium of the airways; 2 ) the surface area of the alveoli is two orders of magnitude larger than airways; and 3 ) while the blood flow to the alveoli corresponds to 100% of cardiac output from the right ventricle , the blood flow of the airways is approximately 1% of the cardiac output from the left ventricle [11] , [12] . To predict a molecule's absorption and retention in each airway generation , the transport properties of small molecules across cellular membranes , as well as the local partitioning of molecules into lipid in different subcellular compartments can be calculated with the Fick and Nernst-Planck equations to describe the transport of the neutral and charged species of the molecule [4] . In simulations , combinations of logP and pKa spanning a range of values were used as input to simulate the changes in concentration of molecules of varying chemical structure , as they are absorbed from the airway surface lining liquid into the blood or vice versa . Here , we applied this cell-based transport model as a virtual screening tool , to identify compounds with differential distribution profiles in airways and alveoli , after intratracheal ( IT ) or intravenous ( IV ) administration . In addition , two innovative in vitro cell based assays were developed to assess the absorption and retention of molecules across multiple layers of cells along the lateral ( Figure 1 D–F ) and transversal planes of a cell monolayer ( Figure 1G ) ) . Finally , in vivo microscopic bioimaging experiments were performed to visualize the distribution of fluorescent probes in the lung after either IT or IV administration ( Figure 1H ) . The results revealed that the mitochondrial sequestration of hydrophilic , cell-permeant cations can provide an effective mechanism for maximizing their local exposure and retention at a site of absorption . Accordingly , mitochondriotropic cations may be useful as fiduciary markers of local , inhaled drug deposition patterns in the upper respiratory tract .
All of the equations and default parameter values were based on our published model [4] . The ODEs that describe this lung pharmacokinetic ( PK ) model were solved numerically in a Matlab® simulation environment ( Version R2009b , The Mathworks Inc , Natick , MA ) . The ODE15S solver was used to address the issue of the stiffness in ODEs , and the relative and absolute error tolerance was set as 10−12 to minimize numerical errors . The Matlab scripts used for virtual screening and simulation purposes are provided , together with detailed instructions for running them , in the Supplementary Materials ( Text S1 , S2 , S3 , 4 , S5 , S6 ) . The results of detailed parameter sensitivity analysis are also provided in the Supplementary Materials ( Text S7 ) . For virtual screening , the airway and alveoli were linked to a systemic pharmacokinetic model through their respective blood compartments using a single compartment PK elimination model ( eq . 1 ) [13]: ( 1 ) Where Vb is the volume of the blood compartment; Cb is the concentration in the blood; and CL is the clearance . The same initial dose ( 1 mg/kg ) was used as an input parameter to simulate IT instillation experiments in the airways and alveoli , respectively . For virtual screening , clearance in the systemic circulation was set to zero . The logPn ( −2 to 4 with interval of 0 . 1 units ) and the pKa ( 5 to 14 with interval of 0 . 2 units ) of monobasic compounds were independently varied and used as input parameters , in all possible combinations . For each set of physicochemical input parameters ( logPn and pKa ) two important pharmacokinetic indexes were calculated: 1 ) the percentage of mass deposited in the airways and alveoli ( relative to the total mass in whole lung ) ; and , 2 ) the concentration in the alveolar and airway regions , calculated as the sum of the masses in all the compartments in said regions of the lung divided by the sum of all the compartment volumes in that region . The area under the tissue concentration curve ( AUC ) for the airways and alveoli was calculated using the trapezoidal rule . The AUC ratio of airways to alveoli after inhalation was calculated by dividing the AUC of the airways by the AUC of the alveoli for every combination of logPn and pKa that were used as input . For comparison , simulations were also run to simulate an intravenous ( IV ) bolus injection , with the initial concentration in venous blood as calculated with eq . 2: ( 2 ) The volume of venous and artery blood was set to 13 . 6 and 6 . 8 ml , respectively [13] , [14] . The concentration in the blood was fixed ( clearance set to 0 ) with the assumption of no significant plasma protein binding and a drug concentration blood to plasma ratio of 1 . Based on the results of virtual screening , two fluorescent probes were selected for further testing: Hoechst® 33342 ( Hoe , Molecular Probes , CA , USA ) to represent a highly hydrophobic , weakly basic molecule that can serve as a reference marker for a readily absorbed probe with limited intracellular retention; and , Mitotracker® Red ( MTR , Molecular Probes , CA , USA ) to represent a more hydrophilic cation that could serve as a candidate fiduciary marker for local inhaled drug deposition and absorption patterns . MTR was modeled with a single , fixed positive charge and a logPd = 0 . 16 . Hoe was modeled as a lipophilic , monobasic molecule with a pKa = 7 . 8 and a logPn = 4 . 49 ( calculated with ChemAxon , www . chemaxon . com ) . These physicochemical properties were used as input parameters to calculate the time dependent changes of the probe concentrations in the airways and alveoli , respectively . For simulations of IT instillation , the same initial concentration ( 1 mM ) of MTR and Hoe was assumed as the initial condition for the airways and alveoli . The same initial dose used for IT instillation was also used for IV administration . Blood clearance was fixed to 0 for simulations , unless otherwise noted . A customized transwell insert system was constructed using a polyester membrane with microfabricated pore arrays precisely machined using a focused ion beam ( Hitachi FB-200A ) [15] ( Figure 1 E ) . These membranes support cell growth and the pores serve as a point source for compound administration to single cells on a cell monolayer ( Figure 1F ) ) . The pore arrays were comprised of 3 µm diameter cylindrical pores , arranged 20 µm apart in a 5-by-5 square array . Pores were also arranged 40 , 80 and 160 µm apart in 3-by-3 symmetrical arrays . The pores were individually machined using a high brightness Ga liquid metal ion source coupled with a double lens focusing system . The perforated membranes were glued ( Krazy Glue® ) to the bottom of hollow Transwell® holder ( Costar 3462 or 3460 ) , creating a permeable support for cell growth ( Figure 1D ) . The integrity of the insert system was tested by adding 5 mM Trypan Blue ( dissolved in Hank's balanced salt solution; HBSS ) to the insert wells [15] . The insert was considered intact if there was no evidence of Trypan Blue leakage from the edge of the insert membrane . For assessing lateral cell-cell transport , Madin-Darby canine kidney ( MDCK ) cells were purchased from ATCC ( CCL-34™ ) and grown ( 37°C , 5% CO2 ) in Dulbecco's modified Eagle's medium ( DMEM , Gibco 11995 ) containing 10% FBS ( Gibco 10082 ) , 1× non-essential amino acids ( Gibco 11140 ) and 1% penicillin/streptomycin ( Gibco 15140 ) . MDCK cells were seeded on polyester membranes containing the pore arrays at a density between 1×105–2×105 cells/cm2 and were grown until a confluent cell monolayer formed ( Figure 1F ) . To evaluate the effect of pore arrays on cell monolayer intactness , MDCK cells were washed and incubated in transport buffer ( HBSS buffer supplemented with 25 mM D-glucose , pH 7 . 4 ) for 30 min followed by transepithelial electrical resistance ( TEER ) measurement using Millipore Millicell® ERS . Cell monolayers were used for experiments only if the background subtracted TEER values were higher than 100 Ω·cm2 and if the cells covering the pore arrays appeared as an intact monolayer . To assess cell-to-cell transport along the plane of the monolayer ( Figure 1D–F ) , fluorescent dyes were added into the basolateral compartment of the transwell system ( at time 0 ) . The dynamic staining pattern in the cells was imaged ( Nikon TE2000S epifluorescence microscope equipped with a triple-pass DAPI/FITC/TRITC filter set ( Chroma Technology Corp . 86013v2 ) ) . The 12-bit grayscale images were acquired using a CCD camera ( Roper Scientific , Tucson , AZ ) . For measurements , individual cells or nuclei in these images were manually outlined using the region tool in MetaMorph® software ( Molecular Devices Corporation , Sunnyvale , CA ) . The average and standard deviation of cellular or nucleus fluorescence intensity was measured using MetaMorph® , after subtracting the background fluorescence intensity estimated from the unstained regions of the monolayer distant to the pores . The rate of Hoe staining in the nucleus was measured as the slope of fluorescence increase normalized by the slope of increase in the first nucleus ( closest to the pore ) . Normal human bronchial epithelial ( NHBE ) cells ( Clonetics™ , passage 1; Lonza , Walkersville , MD ) were cultured ( 37°C , 5% CO2 ) and seeded ( passage 2 ) at 2 . 5×105 cells/cm2 on a Transwell® insert ( Corning Inc . , Lowell , MA; area: 0 . 33 cm2 , pore size: 0 . 4 µm ) in NHBE differentiation media ( Lonza , Walkersville , MD ) The apical media was aspirated after 24 h of cell seeding and the cells on the polyester membrane were maintained in media only in the basolateral compartment of the air-liquid interface culture ( ALC ) [16] , [17] . On day 8 of ALC , the integrity of the cell layers on the membrane was assessed by light contrast microscope and by transepithelial electrical resistance ( TEER ) [18] . After equilibration of the cell layers on the insert with pre-warmed HBSS buffer ( 10 mM HEPES , 25 mM D-glucose , pH 7 . 4 ) for 30 min ( 37°C , 5% CO2 ) , TEER values were obtained and cells with TEER values of ∼600 Ω•cm2 were used for the transport and retention assays [16] , [17] , [19] , [20] , [21] . NHBE cell multilayers grown on the inserts were examined with a Zeiss LSM 510-META laser scanning confocal microscope ( Carl Zeiss Inc . , Thornwood , NJ ) with a 60× water immersion objective on day 8 of ALC culture . For the confocal analyses , three different cell-permeant dyes were prepared by dilution with HBSS buffer 10 µg/ml Hoe; 2 . 5 µM LysoTracker® Green ( LTG , Molecular Probes , CA ) ; and 1 µM MTR ) . After the cell multilayers were washed with HBSS , 240 µl of dye mixture ( 80 µl of each dye in HBSS ) was added to the apical compartment and 600 µl of HBSS was added to the basolateral side . After 30 min , transport of the dyes across the cell layers was measured by placing the insert into a two-chambered slide ( Lab-TeK®; Thermo Scientific Nunc co . , Rochester , NY ) and acquiring images along the Z-axis ( interval , 1 µm ) in three fluorescence channels ( coherent enterprise laser ( 364 nm ) for Hoe , Argon laser ( 488 nm ) for LTG , and Helium neon 1 laser ( 543 nm ) for MTR ) . The distribution of probes applied in the apical compartment of the NHBE cell multilayer cultures was assessed in 3D reconstructions of the acquired images of probe distribution , using MetaMorph® software ( Figure 1G ) . The relative distributions of MTR , Hoe , and LTG dyes across the multilayers were assessed by imaging analyses through multiple Z-stacks . After background subtraction , the integrated intensity of each fluorescence channel per cell was summed in each cell layer and divided by the total integrated intensity in all the layers to calculate the percentage of relative distribution of the integrated fluorescence signal of each dye associated with inner layer or the exposed surface layer of the NHBE cell multilayer . The distribution of MTR and Hoe in airways and alveoli after IT and IV injection in live mice were determined by microscopic imaging of cryopreserved lung tissue sections and confirmed by visual inspection followed by quantitative imaging of high resolution tiled mosaics assembled from fluorescence images of tissue sections ( Figure 1H ) . For these experiments , male C57BL/6J mice ( Jackson Laboratory , Bar Harbor , ME; 8 weeks , 20–30 g ) were used and the protocol was approved by the University of Michigan's animal care and use committee in accordance with the National Institutes of Health Office of Laboratory Animal Welfare “Principles of Laboratory Animal Care . ” MTR ( 50 ug in 10 ul DMSO ) and Hoe ( 90 ul of 10 mg/ml in ddH20 ) were mixed so that the final concentration of MTR and Hoe was 0 . 94 and 14 . 61 mM , respectively . Mice received either 50 µl of dye mixture or 50 µl saline ( control ) via IV tail veil injection or IT instillation [22] . For IV administration , conscious mice were briefly restrained and for IT instillation mice were anesthetized with isoflurane gas , and the dose was delivered to the airway via the oral route as previously described . In order to study the differential regional distribution of fluorescent dyes in the lung , mice were anesthetized with ketamine/xylazine 40 minutes after dosing . A thoracotomy was performed and a heparinized blood sample was acquired by cardiac puncture . The trachea was cannulated ( 20G luer stub ) after which the lungs were inflated with ∼1 mL of a 30% sucrose-optimal cutting temperature ( OCT; Tissue-Tek , Sakura Finetek USA , Torrance , CA USA ) mixture and removed en bloc . The lungs were immersed in OCT and were immediately frozen ( at −80°C ) [23] . For microscopy , coronal lung sections ( 7 µm ) were imaged using an epifluorescence Olympus BX-51 microscope equipped with the standard DAPI , FITC and TRITC filter sets . A series of low-magnification ( ×4 ) left and right lung section images were electronically captured with an Olympus DP-70 high-resolution digital camera using Image J software ( ImageJ 1 . 44b , National Institutes of Health , USA; http://rsb . info . nih . gov/ij ) . In order to permit comparisons of image brightness and fluorescence , images for each lung section were acquired using the same illumination and image acquisition settings . Mosaics of the entire lung were tiled using Photoshop® ( version 4; Adobe Systems Inc . , San Jose , CA ) and quantitative image analysis was carried out using the integrated morphometric analysis function of MetaMorph® . Background subtracted fluorescence intensity values over the airways and alveoli were measured , as the integrated value of all pixels per unit area of the manually selected airway and alveolar tissue regions , using the images acquired with the DAPI channel . In turn , the same airway and alveolar tissue regions were used to measure the MTR fluorescence signal using the images acquired with the TRITC channel .
For virtual screening experiments , molecules with maximal tissue exposure ( AUC ) in the airways after inhalation were identified by using combinations of logPn and pKa as input parameters in a multiscale , cell-based lung transport model ( Figure 2 ) . For weak bases , lower lipophilicity and higher pKa promoted intracellular retention and led to greater local exposure relative to the alveoli ( Figure 2A , B ) . The calculated airway/alveoli exposure ratio ( Figure 2C ) ranged from 100 to 700 and increased with lowered logPn ( increasing hydrophilicity ) and higher pKa ( greater positively charged fraction at physiological pH ) Essentially , cell-permeant , hydrophilic molecules harboring a fixed positive charge showed the greatest accumulation and retention in the cells of the upper airway relative to the alveoli , following IT administration . To probe the role of the route of administration , simulations were also performed by independently varying logPn and pKa to calculate the mass deposition pattern in the airways and alveoli under steady state conditions after IV administration ( Figure 2D–F ) . In this manner we established the relationship between the physicochemical properties of small molecules and absolute and relative mass distribution in the airways ( Figure 2D ) and alveoli ( Figure 2E ) . Following IV administration , the majority of the mass was deposited in the alveoli irrespective of the physicochemical properties of the molecules ( Figure 2F ) ; the airways held less than 20% of total drug mass in the lungs . Compounds with low logPn and high pKa tended to exhibit the largest airway to alveoli mass ratios , which paralleled the results obtained after IT administration . In order to validate the results of these virtual screening experiments , two fluorescent bioimaging probes , MTR and Hoe , were selected for more detailed analysis . MTR is a cell-permeant , hydrophilic cation , and Hoe is a cell permeant , hydrophobic weak base . Based on the screening results ( Figure 3 ) and more detailed simulations ( Figure 3 ) , the concentration profiles of these two fluorescent molecules in the airways and alveoli were markedly different after IT ( Figure 3 A , B ) and more similar after IV ( Figure 3 C , D ) administration . When given IT , the predicted MTR concentration , 40 to 60 min after administration , was nearly 10-fold higher in the airways than in the alveoli ( Figure 3A ) . Conversely , the predicted concentration of Hoe in the airways was two-fold higher in alveoli than in airway ( Figure 3B ) . When given IV , the predicted concentration of MTR in the airways was almost the same as that in alveoli ( Figure 3C ) . However , the predicted concentration of Hoe in the airways was higher in alveoli ( Figure 3D ) . Thus , MTR should be retained in the airways specifically after IT administration , whereas Hoe should not be retained in airways relative to alveoli regardless of the route of administration . Next , cell based assays were used to establish the intracellular retention of MTR and Hoe at a site of absorption . For this purpose , a transwell insert system with micro-fabricated pores was constructed . After seeding MDCK epithelial cells on the patterned pore arrays and adding hydrophobic fluorescent compounds in the basolateral side of cell monolayer , the time course dye uptake in the cells sitting above the pores and the kinetics of lateral transport from the cells lying on top of the pore to the neighboring cells was visualized by fluorescence microscopy . Three hours after the addition of Hoe to the basolateral compartment , only cells that were within close vicinity of pores were stained , indicating that the cells formed a tight seal with the pores such that each pore fed almost exclusively into cells that were in immediate contact with the pores ( Figure 4 ) . Monitoring of the cell-to-cell diffusion of Hoe over time , indicated that the pores served as point sources of sustained dye supply to the adjacent cells ( Figure 4A–D ) and for cells grown on membranes with pores spaced by 80 µm ( Figure 4C ) or 160 µm ( Figure 4D ) , each pore could be considered as the single point source of dye molecules . Quantitative image analysis revealed that the rate of staining rapidly decreased as the distance of the cells from the pores increased ( Figure 4E , F ) . Remarkably , only cells in the vicinity of each pore were labeled . As controls , cells were stained with Hoe plus BCECF-AM from the basolateral compartment ( Figure 4G–I ) . BCECF-AM is a nonfluorescent cell-permeant ester , which generates a cell-impermeant , fluorescent molecule upon intracellular hydrolysis . While the extent of Hoe diffusion was dependent on the distance from the pores ( Figure 4G ) , the green fluorescence of the hydrophillic ester hydrolysis product ( BCECF ) was exclusively restricted to the first layer of cells that were in direct contact with pores ( Figure 4H , I ) . Similar to the Hoe staining pattern , MTR also exhibited a highly constrained diffusion pattern with most of the staining restricted to the vicinity of each pore ( Figure 5 ) . After two-hours of staining from the basolateral compartment with both Hoe ( Figure 5A ) and MTR ( Figure 5B ) , only cells within 60 microns of the pore being stained with both probes ( Figure 5C ) . The normalized fluorescence intensity of MTR and Hoe were similar in the first and second layers of cells , but MTR showed higher penetration into the third layer ( Figure 5D ) . In the transversal direction , the absorption and retention of MTR and Hoe across multiple layers of cells was also assessed in primary NHBE cells differentiated as multilayers in ALC ( Figure 6 ) . For the experiments , MTR and Hoe were simultaneously added in the apical side of the cells and intracellular accumulation was assessed using 3D reconstructions of the cell multilayers ( Figure 6 ) . As a positive control , LTG was also included in the apical HBSS buffer . Thirty minutes after the addition of probes to the apical compartment , both MTR and Hoe staining were constrained to the first , outer surface layer of cells ( Figure 6 , left ) . The cells beneath the surface layer of cells were stained with LTG ( Figure 6 , right ) , indicating that the limited penetration of both MTR and Hoe . Different transport patterns of MTR , Hoe and LTG across the cell multilayers were verified by image quantitation using MetaMorph® software in the multiple Z-stack images of NHBE cell multilayers . Approximately 96%±2 . 76% of MTR or 96%±2 . 48% Hoe of the dye was retained in the surface cell layer whereas 50%±15 . 62% of LTG fluorescence was associated with the surface cell layer . Tukey's multiple comparison test following ANOVA ( one-way analysis of variance ) test showed statistically significant difference between MTR and LTG ( p-value<0 . 0001 ) and also between Hoe and LTG ( p-value<0 . 0001 ) , but not between MTR and Hoe with p-value larger than 0 . 05 ( α = 0 . 05 ) . As an ultimate test of the results of in silico virtual screening experiments , mice were administered a mixture of MTR and Hoe by either IV tail vein or IT instillation and the distribution of the molecules in the lungs was assessed by fluorescent microscopy ( Figure 7 ) . Hoe distributed throughout the lungs regardless of route of administration ( Figure 7A , B ) with fluorescence in both alveoli and airways ( Figure 7C , D ) ) . Following IV administration , MTR also distributed throughout the lung in both airways and alveoli ( Figure 7E ) . Conversely , IT administered MTR resulted in highly uneven fluorescence distribution ( Figure 7F ) . Most importantly , the airway regions showed comparable MTR fluorescence in airway vs . alveoli after IV ( Figure 7G ) but higher MTR fluorescence intensity in airways compared with the alveoli following IT delivery ( Figure 7H ) . To confirm these observations quantitative image analysis was performed to compute background subtracted integrated intensity of alveolar and airway regions , to quantify the relative , differential fluorescence intensity distribution of Hoe and MTR in airway and alveoli . The fluorescence MTR/Hoe ratio ranged from 2 . 42 to 3 . 27 for IT administration . For MTR and Hoechst , the mean ( ± s . d . ) percent airway delivery was 23 . 9%±5 . 8% and 8 . 8%±2 . 7% , respectively ( based on 422 region measurements from a single lung ) . For IV administration , the fluorescence MTR/Hoe ratio ranged from 0 . 95 to 1 . 45 . The mean ( ± s . d ) percent airway delivery for MTR and Hoe were 7 . 5%±2 . 5% and 7 . 1%±1 . 8% , respectively ( based on 383 region measurements from a single lung ) . The images and measurements were consistent with local intracellular retention of MTR in the airways compared with Hoe , following IT ( but not IV ) instillation . These in vivo results paralleled the in silico simulation results ( Figure 3 ) . In order to identify the most important parameters that might explain the differences in local retention of MTR and Hoe , a parameter exchange analysis was performed using computational simulations . For this purpose , individual parameters of the airway were exchanged with those of the alveoli , one at a time , and the simulations were rerun to calculate the exposure ( AUC ) of MTR and Hoe . Based on the results of this simulation analysis ( Table 1 ) the volume of interstitial smooth muscle cells together with the volume of mitochondria were the primary factors determining the retention of MTR in the upper airways relative to alveoli . Secondarily , the surface areas of epithelial and endothelial cell layers were important , affecting retention in opposite directions . Taken together , these results suggest that the mitochondrial density per unit absorption surface area is the key histological organization parameter responsible for the higher retention of MTR in upper airways after IT administration .
In traditional pharmacokinetic studies , drug distribution in the lungs is analyzed in a homogeneous and well-stirred compartment [13] , [24] . Here , we have elaborated an integrated , cell-based approach to model local drug absorption and transport phenomena , aimed at identifying cell-permeant molecules that are retained in the cells of the upper airway upon local pulmonary administration via the inhaled route . This integrated approach can be exploited for bioimaging probe development or for optimizing the local concentration of pulmonary medications [25] , [26] . Locally acting , inhaled medications are of considerable interest for treating various pulmonary ailments , including asthma , chronic obstructive pulmonary disease ( COPD ) and pulmonary hypertension [11] , [27] , [28] . The therapeutic benefits of inhaled medications include targeted drug delivery , rapid onset of action , low systemic exposure with a resultant reduction in systemic side effects [29] , . Nevertheless , measuring local drug concentrations in the lungs is challenging . Previously , regional differences in local lung exposure have received little attention in the context of small molecule targeting and delivery . Inhaled drug development efforts ignore the possibility that local differences in drug exposure could influence regional differences in drug transport properties that are associated with structural and functional characteristics of the airways and alveoli [31] , [32] , [33] . Accordingly , the approach presented here is significant because it furthers our understanding of how inhaled drug molecules and bioimaging probes behave after local administration to the lungs . These findings have important implications in pulmonary drug development . Our simulations and experiments indicate that route of administration , histological organization and circulatory parameters can affect the retention and distribution of different molecular agents in various regions of the lung based on the lipophilicity and ionization properties of molecules , and as such , may be of pivotal importance for the optimization of drug targeting [25] , [26] . Specifically , we considered two major and clearly distinguishable regions of the lungs: the airways and the alveoli , which are histologically and physiologically distinct . Extensive studies have demonstrated that the regional lung deposition of drugs is largely dependent on the aerodynamic particle size generated by delivery devices [28] , [31] , [34] , [35] . Here , we introduce the concept that other parameters ( e . g . , the chemical properties of molecules ) may be as important for predicting the behavior of pulmonary delivered of drugs . This is evidenced from our simulations which indicated that , after absorption into the blood , the majority of drug mass ( >80% of total mass in lungs ) is predicted to accumulate in the alveoli because of its larger volume and higher lipid content and compounds with high lipophilicity and low pKa will accumulate to even a greater extent in the alveoli . Although inhaled drug targeting leads to most of the drug mass deposited in the upper airways , without significant intracellular retention , the molecules can be rapidly absorbed and circulate back to the lung to accumulate in the alveoli . In theory , only molecules that are retained in the cells of the upper airways at the local site of administration can be effectively targeted to the upper airways . To study the transport properties of small molecules in airways and in alveoli , we conducted simulations concentrated on characterizing the behavior of two fluorescent compounds , MTR or Hoe , because they exhibited large differences in simulated transport behaviors . In addition , two in vitro cell based assays were developed to test the local cellular uptake and retention properties of small molecules: 1 ) primary NHBE cell cultures comprised of cell multilayers differentiated on transwell insets in the presence of an air-liquid interface; and 2 ) MDCK cell monolayer cultures on microfabricted pore arrays to establish the lateral cell-to-cell transport kinetics of small molecules , along the plane of the cell monolayer . In the case of Hoe and MTR , both in vitro assays confirmed that the probes were taken up and largely retained by cells in the immediate vicinity of site of absorption and that the extent of diffusion followed a dye concentration gradient from the pores . Our in vitro findings indicated that the lateral cell-to-cell diffusion of MTR and Hoe was highly constrained . These in vitro results confirmed that both Hoe and MTR were retained intracellularly at a significant level in the presence of a transcellular concentration gradient both in the apical-to-basolateral and lateral directions . These results were also informative in terms of the time scale of intracellular accumulation and the relative labeling intensity afforded by these two fluorescent probes in the presence of a transcellular gradient . However , the in vitro assays did not reveal a major difference in the local retention of MTR and Hoe . Based on this observation , the behavior of these probes in these in vitro assays appeared most consistent with the predicted behavior of the probes in the alveoli . Nevertheless , the results of in vivo studies closely paralleled those obtained in silico , in that MTR was retained in airways upon local IT administration while Hoe distributed in both airways and alveoli irrespective of the route of administration . Although in vitro results were useful to confirm the high , local intracellular retention of the probes , the in silico model is a better representation of the three-dimensional organization and physiological parameters of the in vivo situation . Parameter sensitivity analysis indicates that mitochondrial uptake of hydrophilic cations , in relation to the surface area over which absorption occurs , is the critical histological component responsible for high exposure of MTR when given via IT instillation . This is because as MTR traverses from the lumen of the airway into the interstitium , it is rapidly taken up into the mitochondria , driven by the high negative membrane potential of the mitochondrial inner membrane . Conversely , release of MTR from the mitochondria out into the circulation is very slow because the membrane potential slows its release . In the case of alveoli , the alveolar epithelial cells have much higher apical and basolateral plasma membrane surface areas relative to the mitochondrial membrane surface area . The higher cell surface areas facilitate mass transport of MTR across the cells and into the circulation , which reduces MTR accumulation in mitochondria . In contrast to MTR , Hoe is a lipophilic weakly basic compound with a pKa of 7 . 5 . Therefore , at physiological pH , half of the Hoechst molecules exist in a highly membrane-permeant , neutral form . Transmembrane diffusion of the neutral form of Hoe is orders of magnitude faster than that of a cationic form . So there is no significant accumulation or retention of Hoe in either the airways or the alveoli . When administered by IV injection , the direction of distribution is from blood to the tissue . The distribution between blood and tissue is mostly a function of the partitioning or binding of molecules from the circulation to the tissue , which is dependent on the cell density of the tissue , the membrane content of the tissue , and the affinity of the probes for membranes and intracellular components in the tissue . Thus , after IV administration , both Hoe and MTR tended to partition more into alveoli than into the airways . In conclusion , we have elaborated an integrated in silico-to-in vitro-to-in vivo modeling approach which has applicability toward the optimization of site-specific targeting of locally-administered molecules . In the process , we have found that MTR is a candidate fiduciary marker for local drug deposition and absorption patterns in the airways . Due to the compartmental nature of the lungs , computational simulations can be linked to upstream process , such as pulmonary particle deposition , dissolution and mucus clearance , as well as to downstream processes that can be captured by pharmacodynamic models [36] , [37] , [38] . With additional effort this approach can be expanded to include macromolecules , acidic , zwitterionic molecules as well as molecules possessing multiple ionization sites , to further development of probes of lung structure and function [39] , [40] .
|
We have developed an integrative , cell-based modeling approach to facilitate the design and discovery of chemical agents directed to specific sites of action within a living organism . Here , a computational , multiscale transport model of the lung was adapted to enable virtual screening of small molecules targeting the epithelial cells of the upper airways . In turn , the transport behaviors of selected candidate probes were evaluated to establish their degree of retention at a site of absorption , using computational simulations as well as two in vitro cell-based assay systems . Lastly , bioimaging experiments were performed to examine candidate molecules' distribution in the lungs of mice after local and systemic administration . Based on computational simulations , the higher mitochondrial density per unit absorption surface area is the key parameter determining the higher retention of small molecule hydrophilic cations in the upper airways , relative to lipophilic weak bases , specifically after intratracheal administration .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"computer",
"science",
"medicinal",
"chemistry",
"computer",
"modeling",
"drugs",
"and",
"devices",
"chemistry",
"biology",
"computational",
"biology",
"chemical",
"biology"
] |
2012
|
A Cell-based Computational Modeling Approach for Developing Site-Directed Molecular Probes
|
Th9 cells are a subset of CD4+ T cells that express the protoypical cytokine , IL-9 . Th9 cells are known to effect protective immunity in animal models of intestinal helminth infections . However , the role of Th9 cells in human intestinal helminth infections has never been examined . To examine the role of Th9 cells in Strongyloidis stercoralis ( Ss ) , a common intestinal helminth infection , we compared the frequency of Th9 expressing IL-9 either singly ( mono-functional ) or co-expressing IL-4 or IL-10 ( dual-functional ) in Ss-infected individuals ( INF ) to frequencies in uninfected ( UN ) individuals . INF individuals exhibited a significant increase in the spontaneously expressed and/or antigen specific frequencies of both mono- and dual-functional Th9 cells as well as Th2 cells expressing IL-9 compared to UN . The differences in Th9 induction between INF and UN individuals was predominantly antigen-specific as the differences were no longer seen following control antigen or mitogen stimulation . In addition , the increased frequency of Th9 cells in response to parasite antigens was dependent on IL-10 and TGFx since neutralization of either of these cytokines resulted in diminished Th9 frequencies . Finally , following successful treatment of Ss infection , the frequencies of antigen-specific Th9 cells diminished in INF individuals , suggesting a role for the Th9 response in active Ss infection . Moreover , IL-9 levels in whole blood culture supernatants following Ss antigen stimulation were higher in INF compared to UN individuals . Thus , Ss infection is characterized by an IL-10- and TGFβ dependent expansion of Th9 cells , an expansion found to reversible by anti-helmintic treatment .
Upon antigen-specific stimulation , CD4+ T cells have the potential to differentiate into various T-helper ( Th ) cell subsets based on the pattern of transcription factors induced and cytokines produced [1] . Traditionally associated with the Th2 response , IL-9 is a member of the common γ chain cytokine family and exerts broad effects on many cell types including mast cells , eosinophils , T cells and epithelial cells [2 , 3] . However , more recently , a CD4+ T cell subset with the exclusive capacity to secrete IL-9 has been described [4 , 5] . This CD4+ T cell subset is thought to develop under the influence of IL-4 and TGFβ and to produce IL-9 , either singly or in conjunction with IL-10; these cells fail to produce IL-4 [6 , 7] . However , little data are available on the expression pattern of Th9 cells in humans . Th9 cells in humans were initially described as IL-9+and IL-17+ [8]; however , IL-9 producing CD4+ T cells distinct from Th1 , Th2 and Th17 cells have also been described [9 , 10] . Th9 cells , in humans , can play a protective ( tumors [11] ) as well as a pathogenic ( allergy [12] , atopy [13] , asthma [12] and auto-immunity [14] ) role in differing disease states . Although , Th9 cells have been implicated in resistance to intestinal helminth infection in animal models [5 , 15 , 16 , 17] , the role of Th9 cells in human intestinal helminth infections has never been explored . Human infections with Strongyloides stercoralis ( Ss ) appears to be controlled by a Th2 response [18 , 19 , 20] . Moreover , protective immunity to Ss larvae in mice is dependent on CD4+ T cells , and these cells typically produce IL-4 and IL-5 [21] . Finally , primary infections of rats or mice with the rodent parasites , S . ratti and S . venezuelensis respectively , results in a Th2 response , with production of IL-4 , IL-5 and IL-13 and concomitant suppression of IFNγ [22] . We have previously demonstrated that Ss infection is associated with down regulation of parasite-antigen specific Th1 and Th17 responses and up regulation of parasite-antigen specific Th2 responses [23] . Therefore , we sought to determine the regulation of Th9 cells in Ss infection by comparing frequencies of Th9 cells at baseline and following antigen-stimulation in infected ( INF ) with uninfected ( UN ) control individuals . We demonstrate that Ss infection was associated with elevated frequencies of spontaneously expressed or antigen induced mono and dual functional CD4+ Th9 cells . This was further confirmed by elevated levels of IL-9 production in whole blood cultures in INF individuals . The induction of Th9 cells was dependent on IL-10 and TGFβ and was reversible following anti-helmintic chemotherapy .
All individuals were examined as part of a natural history study protocol approved by Institutional Review Boards of both the National Institutes of Allergy and Infectious Diseases and the National Institute for Research in Tuberculosis ( NCT00375583 and NCT00001230 ) , and informed written consent was obtained from all participants . We studied a total of 66 individuals comprising of 43 clinically asymptomatic , Ss infected ( hereafter INF ) individuals and 23 uninfected , endemic normal ( hereafter UN ) individuals in Tamil Nadu , South India ( Table 1 ) . 28 of the INF individuals were used for in vitro culture and flow cytometry and ELISA while 15 of the INF individuals were used for cytokine neutralization experiments alone . Ss infection was diagnosed by the presence of IgG antibodies to two recombinant antigens—NIE and SsIR by the Luciferase Immunoprecipitation System Assay ( LIPS ) , as described previously [24] . Only those individuals who tested positive by LIPS assay to both antigens were classified as INF . This was further confirmed by specialized stool examination with nutrient agar plate cultures . All individuals were also negative for filarial infection by filarial antigen tests and for other intestinal helminths by stool microscopy . All INF individuals were treated single doses of ivermectin and albendazole and follow-up blood draws were obtained six months later in 15 individuals . All UN individuals were LIPS assay negative , negative for filarial or other intestinal helminths and without any signs or symptoms of infection or disease . There were no differences between the groups in terms of demographics or socio-economic status . Saline extracts of S . stercoralis somatic larval antigens ( hereafter SsAg ) and recombinant NIE antigen ( hereafter NIE ) were used for parasite antigens and mycobacterial PPD ( Serum Statens Institute , Copenhagen , Denmark ) was used as the control antigen . Final concentrations were 10 μg/ml for SsAg , NIE and PPD . Endotoxin levels in the SsAg was < 0 . 1 EU/ml using the QCL-1000 Chromogenic LAL test kit ( BioWhittaker ) . Phorbol myristoyl acetate ( PMA ) and ionomycin at concentrations of 12 . 5 ng/ml and 125 ng/ml ( respectively ) , were used as the positive control stimuli . Whole blood cell cultures were performed to determine the frequencies of intracellular cytokine-producing cells . Briefly , whole blood was diluted 1:1 with RPMI-1640 medium , supplemented with penicillin/streptomycin ( 100 U/100 mg/ml ) , L-glutamine ( 2 mM ) , and HEPES ( 10 mM ) ( all from Invitrogen , San Diego , CA ) and placed in 12-well tissue culture plates ( Costar , Corning Inc . , NY , USA ) . The cultures were then stimulated with SsAg , NIE , PMA/ionomycin ( P/I ) or media alone in the presence of the co-stimulatory reagent , CD49d /CD28 ( BD Biosciences ) at 37°C for 6 or 18 h , for intracellular cytokine staining or ELISA respectively . Fast Immune Brefeldin A Solution ( 10μg/ml ) ( BD Biosciences ) was added after 2 hours . After 6 hours , whole blood was centrifuged , washed using cold PBS , and then 1x FACS lysing solution ( BD Biosciences , San Diego , CA , USA ) was added . The cells were fixed using cytofix/cytoperm buffer ( BD Biosciences , San Diego , CA , USA ) , cryopreserved , and stored at -80°C until use . For cytokine neutralization experiments ( n = 15 ) , whole blood from INF individuals was cultured in the presence of anti-IL-10 ( 5μg/ml ) or anti-TGFβ ( 5μg/ml ) or isotype control antibody ( 5μg/ml ) ( R& D Sytems ) for 1 h following which NIE and brefeldin A was added and cultured for a further 23 h . The cells were thawed and washed with PBS first and PBS / 1% BSA later and then stained with surface antibodies for 30–60 minutes . Surface antibodies used were CD3 , CD4 and CD8 ( all from BD Biosciences ) . The cells were washed and permeabilized with BD Perm/Wash buffer ( BD Biosciences ) and stained with intracellular cytokines for an additional 30 min before washing and acquisition . Cytokine antibodies used were IL-4 , IL-9 and IL-10 ( all from BD Pharmingen ) . Flow cytometry was performed on a FACS Canto II flow cytometer with FACSDiva software v . 6 ( Becton Dickinson ) . The lymphocyte gating was set by forward and side scatter and 100 , 000 gated lymphocyte events were acquired . Data were collected and analyzed using Flow Jo software . All data are depicted as frequency of CD4+ T cells expressing cytokine ( s ) or as the mean fluorescence intensity ( MFI ) of cytokine expression within a particular subset . Mono-functional Th9 cells were defined as CD4+ T cells expressing IL-9 alone while dual-functional Th9 cells were those expressing IL-9 plus IL-10 and Th2 cells expressing IL-9 were those that also expressed IL-4 . Frequencies following media stimulation are depicted as baseline frequencies while frequencies following stimulation with antigens or P/I are depicted as net frequencies ( with baseline subtracted ) . Whole blood culture supernatants at 18 h was used for performing IL-9 ELISA . IL-9 was measured using the R&D Systems IL-9 ELISA Duoset kit , according to the manufacturer's instructions . Data analyses were performed using GraphPad PRISM ( GraphPad Software , Inc . , San Diego , CA , USA ) . Geometric means ( GM ) were used for measurements of central tendency . Statistically significant differences between the two groups were analyzed by Mann-Whitney test and multiple comparisons corrected by Holm’s correction . Statistically significant differences between pre- and post- treatment as well as following cytokine blockade were analyzed by Wilcoxon signed rank test .
To examine the baseline ( or steady state ) as well as antigen-stimulated expression pattern of Th9 cells in Ss infections , we cultured whole blood from INF and UN individuals with media alone or with SsAg , NIE , PPD or P/I and measured the frequency of CD4+ T cells expressing IL-9 or co-expressing IL-9 , IL-4 or IL-10 . A representative plot is shown in Fig 1 . As shown in Fig 2A , the baseline frequency of CD4+ T cells expressing IL-9 alone ( mono-functional Th9 cells ) or the frequency of CD4+ T cells co-expressing IL-9/IL-10 ( dual-functional Th9 cells ) was significantly increased in INF individuals . Similarly , the frequency of mono- and dual-functional Th9 cells was also increased significantly in response to SsAg and NIE stimulation ( Fig 2B and 2C ) . Interestingly , the frequency of Th2 cells expressing IL-9 was also significantly increased in response to antigen stimulation . In contrast , neither PPD ( Fig 2D ) nor P/I ( Fig 2E ) induced any significant difference in the frequencies of CD4+ Th9 cells between the 2 groups In addition , as shown in Fig 2F , the MFI of IL-9 expression on CD4+ T cells was significantly higher in INF individuals at baseline and following SsAg and NIE stimulation . Thus , Ss infection is associated with marked increases in the repertoire of mono- and dual-functional Th9 cells at steady state and following parasite—antigen stimulation as well as with marked enhancement of IL-9 expression on a per cell basis in CD4+ T cells . To determine the role of IL-10 and TGFβ in the modulation of Th9 cells in INF , we measured the frequency of these cells following stimulation with the parasite antigen NIE in the presence or absence of anti-IL-10 or anti- TGFβ neutralizing antibody in INF individuals ( n = 15 ) . As shown in Fig 3A , IL-10 neutralization resulted in significantly decreased frequencies of mono-functional and dual-functional Th9 cells in INF individuals . Similarly , as shown in Fig 3B , TGFβ neutralization also resulted in significantly decreased the frequencies of mono-functional and dual-functional Th9 cells in INF individuals . Interestingly , only TGFβ but not IL-10 blockade significantly decreased the frequency of Th2 cells expressing IL-9 . In addition , as shown in Fig 3C , the MFI of IL-9 expression on CD4+ T cells was significantly diminished following both IL-10 and TGFβ blockade , indicating that the cytokine expression on a per cell basis is also modulated by these regulatory cytokines . Thus , IL-10 and TGFβ play important roles in the antigen-induced expansion of Th9 cells in Ss infections . To determine the role of active infection in the regulation of mono- and dual-functional Th9 cells in Ss infections , we measured the Th9 response in a subset of INF individuals ( n = 15 ) , who had been treated with anti-helmintic chemotherapy six months earlier . As shown in Fig 4A , treatment of Ss infection resulted in significantly reduced frequencies of mono- and dual-functional Th9 cells in response to SsAg or NIE stimulation but not in response to PPD or P/I . In addition , as shown in Fig 4B , the MFI of IL-9 expression on CD4+ T cells is also significantly decreased in response to SsAg or NIE stimulation but not in response to PPD or P/I following treatment . Thus , the antigen-driven expansion of CD4+ Th9 cells in Ss infection is reversible ( for the most part ) following treatment of infection . To examine the baseline ( or steady state ) as well as antigen-stimulated production of IL-9 in Ss infections , we cultured whole blood from INF and UN individuals with media alone or with Ss Ag or P/I and measured the levels of IL-9 production in the supernatants at 18 h by ELISA . As shown in Fig 5A , the production of IL-9 at baseline and following Ss Ag stimulation was significantly increased in INF compared to UN individuals . Moreover , as shown in Fig 5B , this elevated production of IL-9 was significantly decreased in INF individuals following treatment . Thus , Ss associated enhancement of Th9 cell frequencies is reflected by a concomitant increase in IL-9 cytokine levels .
Although IL-9 is known to be expressed by several types of immune cells , IL-9 secreting CD4+ T cells are a predominant source of IL-9 in allergic inflammation and anti-parasite immunity [6 , 7] . Th9 cells in human diseases are known to contribute to both protective immune responses and pathological responses leading to immune mediated pathology [7] . The role of IL-9 in helminth infection was first suggested by animal studies showing that IL-9 transgenic mice infected with Trichuris muris or Trichinella spiralis had an increased Th2 response and faster expulsion of the parasite from the intestine [15 , 16 , 17] . While classically considered a Th2 cytokine , IL-9 has now been shown to produced by a distinct subset of CD4+ T cells that express IL-9 with or without IL-10 but in the absence of IL-4 [4 , 5] . Whether the source of IL-9 ultimately matters in the context of resistance to infection is still not clear . However , a more recent study has clearly shown that IL-9 is produced early during Nippostrongylus brasiliensis infection of mice by a non-Th2 CD4+ T cell subset and that its production from this subset is sufficient for host protection against worm infection [25] . Our study on the regulation of Th9 cells in Ss infection reveals the presence of increased frequencies of Th9 cells ( IL-9 single and IL-9/IL-10 double expressing ) at baseline in INF individuals compared to UN individuals . The increased frequency is further augmented upon stimulation with two different parasite antigens , indicating that the Th9 cells are parasite specific and respond to recall stimulation . In addition to classical Th9 cells responding to antigen-stimulation , we also observed increased frequencies of Th2 cells expressing IL-9 ( IL-4/IL-9 co-expressing ) , indicating that other CD4+ T cell subsets also respond to Ss infection with the capacity to produce more IL-9 . This confirms our previous data demonstrating elevated Th2 responses in Ss infection and its reversal following anti-helmintic therapy [23] . This study also confirms data from the animal models of helminth infection showing expansion of IL-9 expressing CD4+ T cells [25] . Although the kinetics of Th9 induction compared to the induction of Th2 cells cannot be determined from this study , our data clearly reveal that Th9 responsiveness ( that is separate from Th2 responses ) is a major feature of the antigen-specific T cell response in this human helminth infection . Moreover , our data also indicate that the enhanced frequencies of Th9 cells is reflected in elevated expression of IL-9 on a per cell basis in Ss infections . Finally , we also confirm that Ss infections are associated with elevated levels of antigen-stimulated IL-9 levels by using ELISA . Thus , by using two different methodologies , we verify the important association of enhanced Th9 responses in Ss infections and its reversibility following treatment . While we have demonstrated statistically significant changes in Th9 responses and have verified this with actual cytokine levels , we do acknowledge the fact that these changes are small in magnitude . Previous studies have shown that IL-2 , IL-4 and TGFβ are the primary factors that drive differentiation and expansion of Th9 cells [7] . Moreover , we have previously shown that Th2 and regulatory cytokines are increased in Ss infection [23] . Therefore , we wanted to examine the role of IL-10 and TGFβ in the induction of Th9 responses . In this study , we observed that IL-10 in addition to TGFβ also appears to play an important role in regulating the expansion of Th9 cells in Ss infection . The exact mechanism by which IL-10 modulates the expansion of Th9 cells is yet to be determined . Finally , we demonstrate an important role for the persistence of antigen in the maintenance of the Th9 response . Our data clearly illustrate that anti-helmintic treatment results in significantly depressed Th9 responses , implying that sustained , chronic infection is a major driver of Th9 maintenance . We postulate that the induction of IL-10 and TGFβ in chronic infection upregulates Th9 expression that is reversible upon anti-helmintic treatment . IL-9 is known to act on a wide variety of cells and perform multiple functions [26] . For example , IL-9 stimulates the growth , proliferation and survival of T cells [27 , 28] , enhances the production of IgE from B cells [29] , promotes the proliferation and differentiation of mast cells and hematopoietic progenitors and induces secretion of mucus and chemokines by mucosal epithelial cells [30] . Therefore , IL-9 has the ability to act as a critical cytokine in mucosal infections especially helminth infections and indeed , has been shown to be crucial driver of the host immune response against these infections in animal models . Our study extends these findings to human gastrointestinal helminth infection and shows that non-Th2 Th9 responses are a major feature of Ss infection , responses that likely require IL-10 and TGFβ for their maintenance .
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Strongyloides stercoralis is a common intestinal parasite affecting about 50–100 million people worldwide . It is characterized by a complex lifecycle involving both free- living and parasitic stages and the clinical manifestations range from asymptomatic infection to multi-organ failure . It has the propensity to cause disseminated disease and death in immunocompromised individuals . Therefore , an in depth understanding of the immune responses to this helminth parasite is warranted . However , what we know about the immunity to this infection is mostly derived from animal studies . Th9 cells are a subset of CD4+ T cells producing the cytokine—IL-9 . Since Th9 cells are increasingly recognized as being important in immunity to intestinal infection with helminths , we examined the induction and regulation Th9 cell responses to Ss infection utilizing infected and uninfected individuals from an endemic area in India . We show that Ss infection is characterized by profound alterations in the Th9 compartment and that this response is mainly regulated by the cytokines—IL-10 and TGFβ . In addition , we also demonstrate that active infection is a pre-requisite for this regulation and anti-Ss treatment can dampen enhanced Th9 responses .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2016
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IL-10- and TGFβ-mediated Th9 Responses in a Human Helminth Infection
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Schistosoma mansoni do not have de novo purine pathways and rely on purine salvage for their purine supply . It has been demonstrated that , unlike humans , the S . mansoni is able to produce adenine directly from adenosine , although the enzyme responsible for this activity was unknown . In the present work we show that S . mansoni 5´-deoxy-5´-methylthioadenosine phosphorylase ( MTAP , E . C . 2 . 4 . 2 . 28 ) is capable of use adenosine as a substrate to the production of adenine . Through kinetics assays , we show that the Schistosoma mansoni MTAP ( SmMTAP ) , unlike the mammalian MTAP , uses adenosine substrate with the same efficiency as MTA phosphorolysis , which suggests that this enzyme is part of the purine pathway salvage in S . mansoni and could be a promising target for anti-schistosoma therapies . Here , we present 13 SmMTAP structures from the wild type ( WT ) , including three single and one double mutant , and generate a solid structural framework for structure description . These crystal structures of SmMTAP reveal that the active site contains three substitutions within and near the active site when compared to it mammalian counterpart , thus opening up the possibility of developing specific inhibitors to the parasite MTAP . The structural and kinetic data for 5 substrates reveal the structural basis for this interaction , providing substract for inteligent design of new compounds for block this enzyme activity .
The purine metabolism in the human parasite Schistosoma mansoni was extensively studied in the 1970s and 1980s by Senft and collaborators [1–8] and Dovey [9] . These studies demonstrated that adults and schistosomules of S . mansoni do not have the capacity to synthesize purine nucleotides de novo , instead depending solely on the purine salvage pathway to meet their purine requirements . One striking difference between S . mansoni and the human host ( and also from other mammals ) is their phosphorolytic capacity to cleave adenosine into adenine [4] . The presence of enzymatic activity for adenosine phosphorylase ( AP ) ( EC None ) , yielding adenine and ribose-1-phosphate ( S1 Fig ) , was detected by Senft et al . [1] in both extracts and vomitus of S . mansoni . The free adenine base released in this reaction is utilized by adenine phosphoribosyltransferase ( APRT ) ( EC 2 . 4 . 2 . 7 ) to generate AMP , which in turn is converted into ADP and further into ATP via the action of adenylate kinase ( ADK ) ( EC 2 . 7 . 4 . 3 ) and nucleoside diphosphate kinase ( NDPK ) ( EC 2 . 7 . 4 . 6 ) enzymes . When cell-free extracts of S . mansoni were incubated with [14C]-adenosine in the presence of phosphoribosyl pyrophosphate ( PRPP ) , approximately 70% of the initial radioactivity was detected as nucleotides after 120 minutes . In contrast , when PRPP was omitted , negligible amounts of radioactivity were presented as nucleotides , even after 120 minutes of incubation [1] . When the worms were incubated with [14C]-adenine , the rapid formation of nucleotides was observed . These results were confirmed by Dovey et al . [9] , who demonstrated that schistosomules also have a significant adenosine phosphorylase activity ( 1 . 12 nmol min-1 mg-1 protein ) ; this activity was highest in schistosomule extracts . The AP activity was inhibited by adenosine analogue formycin A ( 75% inhibition at 1 mM ) . S . mansoni also exhibits high levels of APRT activity ( 2 . 32 ± 0 . 42 nmol min -1 mg -1 protein ) ; thus , adenosine phosphorylase-APRT chain reactions must be the main route for adenosine incorporation . This fact correlates with the relative abundance of adenosine in the serum of the mammalian host ( 0 . 2–5 . 0 μM ) hmdb . ca [9] . The use of adenosine analogues alone or in combination with nucleoside transport inhibitors has been employed both in vitro and in vivo [10–14] . One of these analogues , tubercidin ( 7-deazadenosine ) , is incorporated at the nucleotide level and inhibits Schistosoma motility with an IC50 of 1 μM , and Formycin A ( 8-aza-9-deazaadenosine ) inhibits 73% of the adenosine phosphorylase activity at 1 mM [15] . These examples indicate the feasibility of using purine analogues as a scaffold for the development of new antiparasitic agents . Because the S . mansoni genome does not codify an adenosine phosphorylase , one alternative to fulfilling this activity is 5´-deoxy-5´-methylthioadenosine phosphorylase ( E . C . 2 . 4 . 2 . 28 ) ( MTAP ) . This enzyme catalyzes the reversible phosphorolysis of 5´-deoxy-5´-methylthioadenosine ( MTA ) to free adenine and 5´-deoxy-5´-methythioribose-1-phosphate ( MTR1P ) ( S2 Fig ) . In humans , the MTAP participates mainly in the polyamine pathway [16] . The MTAP enzyme is present in some parasites ( Leishmainia donovani , S . mansoni , Trypanosoma cruzi and brucei ) and absent in others ( Giardia lamblia , Plasmodium falciparum and entamoeba invadens ) . When it is present , parasite MTAP is similar to the mammalian MTAP , although it does differ in some characteristics: it has a low KM for the adenosine and some of their analogues ( 2´deoxy or 2´ , 3´dideoxyribose analogues ) [17] . Here , we describe the structure and kinetics parameters of a S . mansoni MTAP ( SmMTAP ) and its active site mutants . By analyzing both sets of data , we can hypothesize that MTAP and AP , as described above in a series of articles , are the same entity in worms . Furthermore , a comparison of the KM levels reveals that S . mansoni MTAP can use adenosine with the same efficiency as MTA , which is one fundamental difference between S . mansoni and its host and reveals a potential target for schistosomiasis drug development .
The pET28a expression system , E . coli BL21 ( DE3 ) strains was purchased from Novagen . The cloning vector pTZ57R/T and Taq DNA Polymerase were purchased from Thermo Fisher Scientific . The Wizard Plus SV Minipreps DNA Purification System was purchased from Promega , and restriction enzymes were purchased from New England BioLabs . The xanthine oxidase from bovine milk and all nucleosides were purchased from Sigma-Aldrich . From the total mRNA from the adult worm , strand cDNAs were synthesized by RT-PCR employing the SuperScript III First-Strand Synthesis System from Promega . Forward ( 5’-CTGGCTAGCATGTCTAAAGTTAAGGTTGGAATTATTG-3’ ) and reverse ( 5’- CTGCTCGAGCCAATTTACTTCATGTTTATTTGTCATTAC-3’ ) primers containing NheI and XhoI restriction sites ( in italics ) were designed for subcloning into the pET28a expression vector . The RT product was used as a template for PCR; after adenylation , the amplification product was cloned into the pTZ57R/T vector and transformed into Escherichia coli DH5α cells . Transformants were selected using the chromogenic substrate X-gal and by colony PCR . The MTAP gene was digested with NheI and XhoI ( New England Biolabs ) and recovered on a 1% agarose gel using the Promega Wizard SV Gel and PCR Clean Up kit . The pET28a-MTAP construct was synthesized by treatment with T4 DNA ligase ( New England Biolabs ) using pre-digested pET28a vector with the same enzymes . The fusion plasmid was used in the transformation of Escherichia coli BL21 ( DE3 ) cells . The transformed E . coli was confirmed by PCR colony . Protein expression was performed in 1 L 2xYT medium in the presence of 50 μg/mL kanamycin , inoculated with an overnight culture . The cells were incubated at 37°C to an OD600 of approximately 0 . 6 and were induced with 0 . 1 mM of isopropyl β-D-thiogalactopyranoside ( IPTG ) for 4 h . The cells were harvested by centrifugation at 6000 g for 45 minutes at 4°C and lysed by sonication in 50 mM NaH2PO4 , pH 7 . 4 , 300 mM NaCl , 10 mM imidazol and 5 mM β-mercaptoethanol . The lysate was clarified by centrifugation at 9 , 000 g for 20 minutes at 4°C . The soluble fraction was applied on a Co-NTA agarose column ( Clontech ) and washed with 50 mM NaH2PO4 pH 7 . 4 , 300 mM NaCl , 20 mM imidazole and 5 mM β-mercaptoethanol . The protein was eluted with 50 mM NaH2PO4 , pH 7 . 4 , 300 mM NaCl , 200 mM imidazole and 5 mM β-mercaptoethanol . The enzyme was dialyzed against 20 mM Tris , pH 7 . 4 , 200 mM NaCl and 10 mM β-mercaptoethanol . All stages of SmMTAP production were visualized by SDS-PAGE . The single SmMTAP mutants ( S12T , N87T and Q289L ) were prepared by Mutagenex ( Suwanee-USA ) , and the double ( S12T/N87T ) and triple mutants ( S12T/N87T/Q289L ) were prepared by Cellco Biotech ( São Carlos-Brazil ) . The protocols for the expression and purification of the mutants were the same as those used for wild-type SmMTAP . The kinetic parameters for adenosine , MTA , 2-deoxyadenosine and phosphate ( PO4 ) were measured by coupled assay by xanthine oxidase [18] . In this method , the xanthine oxidase converts free adenine from MTA or Ado into 2 , 8-dihydroxyadenine , resulting in an increase in the absorbance at A305 nm ( ε = 15 . 500 AU [19] ) . Kinetic parameters were calculated in sextuplicate at room temperature in a 200 μL reaction mix containing 100 mM potassium phosphate buffer at pH 7 . 4 , an adequate quantity of substrate and 0 . 3 units of xanthine oxidase from bovine milk ( Sigma-Aldrich ) . The reaction was started by adding 50 nM of SmMTAP to the reaction mixture , and the OD305 was immediately monitored using a SPECTRAmax PLUS384 spectrophotometer ( Molecular Devices , USA ) . For PO4 kinetics 100 mM Hepes pH 7 . 4 was used . The kinetic parameters ( KM and kcat ) were derived from non-linear least-squares fits of the Michaelis-Menten equation in the Graphpad Prism software using the experimental data . The initial crystallization conditions for SmMTAP were determined by a Honeybee 961 robot ( Genomic Solutions ) using crystallization screen solutions from Hampton Research and Qiagen , where several conditions yielded crystals of varying quality . These conditions were optimized by varying the pH and PEG concentration , which resulted in diffraction-quality crystals that grew at 18°C using the hanging-drop technique . The best conditions were 100 mM Bis-tris or MES buffer , pH 6 . 1–6 . 7 and 14–18% PEG 3350 with 6 μl drops containing a 1:1 mixture of protein ( 6 mg/mL ) /crystallization solution , incubated in a 500 μL reservoir solution . The same procedure was employed for single , double and triple mutants . The triple mutants did not generate crystals , even in a new crystallization screening , due to solubility problems . The SmMTAP wild-type and mutant crystals were also grown in the presence of the ligands ( 5 mM ) MTA , adenosine , ribose 1-phosphate and tubercidin . The crystals were mounted in a nylon-fiber loop , cryoprotected with 20% glycerol or PEG200 in the mother solution and cooled in liquid nitrogen . Diffraction data were measured using synchrotron radiation on a Beamline MX2 of the Laboratório Nacional de Luz Síncrotron ( LNLS , Campinas , Brazil ) and Beamlines I02 , I04 and I04-1 at Diamond Light Source ( DLS , Harwell , UK ) . The data were indexed , integrated and scaled using the MOSFLM/SCALA programs [20 , 21] to the data from LNLS , and the Xia2 [22] and XDS [23] programs to the data from DLS . The SmMTAP-apo enzyme structure was solved by molecular replacement using Phaser [24] , employing the human MTAP monomer ( PDB ID 1CB0 [25] ) as a search model because it shares 47% of sequence identity . The remaining wild-type and mutant structures were also solved by molecular replacement , using one of the previously refined structures as a model . The refinement was carried out using Phenix [26] , and the model was constructed using COOT [27] with weighted 2Fo–Fc and Fo–Fc electron density maps . The full statistics of data collection and refinement are shown in Table 1 . In all cases , the behavior of RFree was used as the principal criterion for validating the refinement protocol , and the stereochemical quality of the model was evaluated with Molprobity [28] . The collection , processing and refinement are shown in Table 1 . The coordinates and structure factors have been deposited with the PDB under the following codes: SmMTAP-tubercidin 4L5A; SmMTAP-adenine in space group P212121 4L5C; SmMTAP-APO 4L5Y; SmMTAP-adenine 4L6I; SmMTAP-S12T-APO 5F73; SmMTAP-S12T-adenine 5F77; SmMTAP-S12T-MTA 5F76; SmMTAP-N87T-APO 5F78; SmMTAP- N87T- adenine 5F7J; SmMTAP-Q289L-APO 5F70; SmMTAP- Q289L-Tubercidin 5F7X; SmMTAP-S12T-N87T APO 5F7Z; and SmMTAP- S12T-N87T adenine 5FAK .
The gene model Smp_028190 , which was derived from the annotation of the S . mansoni genome [29] , was selected as a putative methylthioadenosine phosphorylase due to the presence of a conserved MTAP domain ( TIGR01694- evalue e-99 ) . Alignment of the nucleotide sequence of this model with S . mansoni transcript sequences from dbEST using blastn programs resulted in only the first 844 bases of this putative transcript of 888 bases being aligned to the transcript sequences . The 44 bases at the 3’ end of this model correspond to an exon predicted on bases 37729–37685 of the W chromosome and did not align to any transcript , which probably represents an error in the genome annotation . Indeed , the mapping of two ESTs that were partially aligned to this model ( Accesion number EX708725 . 1 and EX708663 . 1 ) into the genome suggest a different 3’ end exon on bases 46092–46194 of the W chromosome . We designed primers for the amplification of the full-length coding region of MTAP based on the 5’ end from the gene model and on this new 3’ end and were able to amplify a ~900 bp amplicon . Sequencing of this amplicon confirmed this corrected gene model based on EST evidence . This new transcript sequence was deposited on NCBI under accession number JQ071534 . 1 . The SmMTAP gene codes a protein with 299 amino acids and an expected molecular weight of 32 . 913 . 9 Da . SmMTAP shares 77% and 47% sequence identity with S . japonicum and human MTAP , respectively ( Fig 1 ) . The sequences from the two schistosomes have a 16-amino-acid insertion between beta strands 10 and 11 . This insertion does not have high sequence identity between schistosome MTAPs , sharing 43 . 7% identity versus 77% identity for the full sequence , respectively . After analyzing both alignments and the SmMTAP structure ( as discussed in detail below ) , we found three substitutions in the active site compared to the human MTAP . To characterize the structural differences between human and S . mansoni MTAPs active sites and the SmMTAP kinetic properties , we prepared three single mutants ( S12T , N87T and Q289L ) , a double mutant ( S12T/N87T ) and a triple mutant ( S12T/N87T/Q289L ) . We obtained the structures of the single mutants and the double mutant . The triple mutant protein was highly insoluble and did not crystallize in SmMTAP crystallization conditions or produce crystals , even after a new crystallization screening . Recombinant SmMTAP protein was successfully expressed using pET28a vector and purified in a single step using a cobalt affinity column , yielding ~120 mg per liter of 2xYT medium . SmMTAP expression and purification were visualized by SDS PAGE gel ( data not shown ) . The subunit molecular weight of the expressed and tagged protein was calculated to be 33 . 9 kDa , as observed by denaturing gel electrophoresis after purification . Crystallization and X-ray diffraction of SmMTAP crystals allowed the resolution of 13 different structures , including different ligands ( adenine , MTA , tubercidin and sulphate ) of the WT and mutated SmMTAP . With the exception of one orthorhombic structure ( P212121 ) ( in more than 100 datasets collected ) , SmMTAP crystallized in the monoclinic space group P21 with one or two trimers to an asymmetric unit . A comparison of structures from SmMTAP derived from 22 independent trimers observed in 13 different conditions ( Fig 2A ) allows a comprehensive description of the protein structure . The observed overall fold of SmMTAP is similar to that of members from NP-1 family of low-molecular weight purine nucleoside phosphorylases ( PNP ) , which consist of a central β-sheet distorted barrel surrounded by α-helices ( Fig 2A ) , as observed in other MTAPs from human and bacteria . Superimposition of the 66 monomers composing the studied trimers resulted in a solid structural framework , thus allowing the detection of several conformational modifications ( Fig 2B ) that were related to the binding of different ligands and mutations . The trimers and monomers are similar to within an RMSd of 0 . 23 and 0 . 22 Å , respectively , and the regions comprising residues 9–21 ( phosphate loop ) , 119–128 , 159–169 and 229–254 ( gate loop ) are the most flexible parts of the SmMTAP and undergo large conformational changes induced by ligand binding ( Fig 3 ) . The SmMTAP-adenine complex is the most common structure obtained ( 23 of the 66 monomers ) . Several structures from co-crystallization experiments with adenosine or MTA show adenine in the active site , even in the expected "apo" state . Further , adenine was identified in the SmMTAP active site after crystallization and structural solution and was probably acquired from the bacterial source . The Base Binding Sites ( BBS ) of two of the six SmMTAP-adenine complexes obtained ( SmMTAP P212121 and S12T mutant ) were fully occupied by adenine . In other complexes , adenine was present in only two subunits of the trimer . The subunits without adenine preferentially assume the conformation of the APO structure ( open conformation ) of the gate loop ( residues 231–242 ) and part of the helix 5 ( residues 240–252 ) . Indeed , this part of helix 5 assumes two different conformations in APO and bounded structures . These conformational changes mainly involve residues 233–241 . In 20 of the 30 monomers that are bound to adenine , the gate loop is in the closed conformation , which indicates that adenine binding could promote gate loop ordering . However , it is intriguing that subunit E of the double-mutant ( S12T/N87T ) and subunits B and C of the S12T mutant were found in the open conformation , even when they were bound to adenine . A disulphide bond between residues C233 and C242 was observed in the APO structures of S12T , N87T and S12T/N87T mutants . SmMTAP structures displaying this SS bond also have the gate loop locked in open form . It remains to be characterized why there is a preference for the formation of such bonds in these mutants and the biological relevance ( if any ) of this SS bond ( Fig 4 ) . In general , residues 229–242 , which comprise the catalytic residue D230 and the gate loop , are disordered in subunits without ligands . Residues 242–253 of the H8 helix in Apo subunits are in different orientations compared to the bound structure . The active site of SmMTAP was characterized using complexes with adenine , MTA , tubercidin and sulphate . Tubercidin is an adenosine analogue ( 7-deazaadenosine ) that shows potent activity against S . mansoni and is an inhibitor of adenosine phosphorylase activity [15] . All complexes were obtained from a crystal that was grown in the presence of 5 mM of ligand . Similar to other MTAPs , the active site is located near the interface between monomers . Residues H131 and Q289 from neighboring subunit also participate in the active site . Ligplus+ diagrams for all ligands are shown in Fig 5 . The active site is subdivided into three sub-sites: base , ribose/methylthioribose and phosphate binding sites , as described for NPs . The adenine molecule or adenine moiety of MTA forms three H-bonds within residues D230 ( 2 ) and D232 ( 1 ) and one H-bond with a water molecule , which is anchored by the side chain of residues D232 and S118 ( Fig 6A ) . In some structures , adenine does not bind in all subunits; the only WT structure whose BBS are all occupied is the orthorhombic SmMTAP adenine complex . Interestingly , in the S12T adenine complex , the presence of adenine does not cause the gate loop to close ( subunits B and C ) ; this is also observed in the S12T/N87T double mutant subunit E ( the modes of adenine binding in these structures are shown in Fig 6B and 6C ) . Despite these exceptions , the MTAP-adenine complex , which is the region near D230 , assumes a conformation in which D230 points toward the adenine molecule . In the MTAP tubercidin complex , all of the active sites are occupied; however , in four of the six subunits , D230 points away from the 7-deazaadenine moiety of tubercidin . Additionally , in subunits that interact within the base ( D and E ) , D230 does not possess canonical contacts with the base; in such cases , different rotamers are observed , and an greater distance between D230 OD1 and C7 of tubercidin is observed compared to the equivalent distance in SmMTAP adenine complex ( D230 OD1—N7 adenine ) . The presence of nitrogen in the N7 position appears to be necessary to orient and maintain the side chain of D230 in the canonical position ( Fig 6D ) . Tubercidin only forms two H-bonds within the BBS , one with D232 ( TUB N6—D232 OD1 ) and one with water 409 , which is conserved in all structures with ligand . In the Q289L-tubercidin complex , one BBS is in the canonical D230 conformation ( Fig 6D and 6E ) . A complex between the SmMTAP mutant S12T and MTA was also obtained; in this case , MTA bound all subunits , causing gate loop closure in the A and B subunits , whereas subunit C was in open conformation . The adenine moiety of the MTA forms the same interactions described above for adenine ( Fig 6F ) . All binding modes in BBS are visualized in Fig 6 . Comparing the BBS between bound and unbound structures reveals a large movement involving residues 230–232 ( usually residues 233–241 are absent in the APO structures ) ( Fig 7 ) , and residue T229 presents a different side chain rotamer . In APO structures , E230 usually points away from the active site , and F231 occupies part of the base binding site ( BBS ) ; this movement involves the reorientation of both the main and side chains . The distances between the Cαs of residues 230–232 are 2 . 1 , 6 . 7 and 6 . 4 Å , respectively . This movement was not observed for MTAPs from other organisms . The characterization of RBS was based on 16 monomers of tubercidin complexes ( 6 monomers of the wild type and 3 of the Q289L mutant ) , 3 monomers of MTA ( all S12T ) and 4 monomers of glycerol ( from the WT adenine complex ) . Ribose/MTR is a mainly hydrophobic binding site; however , four H-bonds were observed in this site . The RBS is formed by the H-bonded interacting residues N205 , M206 , D232 and Q289 and the non-bonded interacting residues A88 , H131 and V246 . The glycerol that was found in RBS in some structures with adenine came from the cryoprotection solution . The carbon atoms of glycerol lie in the same position of C3' , C4' and C5' in the ribose moiety of nucleoside . The glycerol forms four H-bonds: one with residue S12 , one with adenine N9 and two with the water molecules present in RBS . The ribose moiety of tubercidin forms four H-bonds within RBS ( O2'—N205 ND2; O2'—M206 N; N6—D232 OD1 and O5'—Q289 OE1 ) . This latter is only possible due to the presence of glutamine in position 289 ( leucine 279 in human MTAP ) ; however , this interaction was not observed in all subunits of the tubercidin complex . Indeed , analyzing the superposition of the 66 monomers obtained showed a high conformational plasticity for the side chain of residue Q289 . For MTA in the S12T structure , only one H-bond is formed in RBS ( MTA O2'—M206 N ) . The presence of the methylthio group in position 5 causes a displacement of the side chain of Q289 to accommodate the bulky group . No other significant differences are observed . The PBS is formed by the residues S12 , H55 , A88 , R54 and T207 , which form H-bonds with sulphate , and the non-bonded interacting residues G11 and N87 . The PBS was characterized by the presence of a sulphate molecule . The sulphate molecule forms 7 H-bonds with residues ( S12 , R54 , H55 , A88 , N205 and T207 ) in the phosphate binding site ( PBS ) and one water-mediated bond with T86 ( Fig 8 ) . The orthorhombic SmMTAP-adenine complex does not present sulphate/phosphate molecules in PBS and shows a different conformation for the phosphate loop S12; in this structure , S12 OG interacts with Q289 NE2 via the H-bond ( 2 . 88A ) . It is interesting to note that S12 and Q289 are two sequence substitutions compared to human MTAP . The S12 OG also interacts with glycerol O2 via a weak H-bond . When compared by superposition , the human MTAP structures [25 , 30] do not present larger deviations between structures; however , if we compare HsMTAP and SmMTAP , some differences emerge . The region comprising residues 13–28 shows a different conformation , which appears to be related to the presence of F14 ( L20 in HsMTAP ) and F250 , which reduces the space that S12 loop could assume . In the human structure , residues 23–25 form a 3–10 helix that is not observed in SmMTAP . Residues 13–28 lie in a low sequence identity region ( 32% identity ) . The beta turn between beta strands 8 and 9 presents a different conformation , but there are no structural consequences ( this is also observed in the low sequence identity region ) . The gate loop ( residues 231–242 ) is also in a different conformation in SmMTAP ( when it is present in wt structures ) and forms a 3–10 helix ( residues 237–239 ) . Although the BBS main residues are fully conserved between SmMTAP and human MTAP , their kinetic properties are entirely different ( discussed below ) . To investigate the structural basis of this kinetic difference , we located three residue substitutions in the active site , S12T , N87T and Q289L ( S . mansoni: human ) , that could be involved in substrate specificity . Indeed , because the base is the same for adenosine and MTA , these differences are located in both the phosphate and ribose/MTR binding sites . The substitution S12T appears to be conservative; however , S12 side chain could assume at least two main conformations: OG pointing towards or away from the PBS . For T18 in human MTAP , only one side chain conformation could interact with phosphate , whereas another consequence of S12 appears to increase the conformational plasticity of the phosphate loop . Serine at this position is observed in other nucleoside phosphorylases as a PNP from human [31] and S . mansoni [32–38] . As discussed above , the Q289 side chain could interact with S12 in SmMTAP ( Fig 9 ) ; the consequences of these substitutions are discussed in the section on SmMTAP mutants . The other substitution , N87T93 , is related to the phosphate binding site . In HsMTAP , T93 forms two H-bonds with phosphate by using both OG and main chain nitrogen ( as seen in PDB Sum site ) . In SmMTAP , N87 does not form these interactions within phosphate molecules , although its side chain forms two intra-chain interactions ( N87 OD1—S224 OG and N87 ND2—T207 OG1 ) . The last main difference is the substitution Q289L279 . This residue is situated in the last helix ( H8 ) from the other subunit and potentially interacts with the O5' adenosine ribose ( as seen in SmMTAP tubercidin complex ) and S12 . In HsMTAP , the side chain of L279 points towards the hydrophobic MTR binding site . Appleby et al . [25] noted that MTAP’s specificity for MTA and other 5'-deoxynuclosides or even for nucleosides with substitutions in 5' position ( as halogen , haloalkyl or alkylthio groups ) appears to be a consequence of hydrophobicity and lacking H-bond donors or acceptors in the ribose/MTR binding site . The presence of Q289 side chain in ribose/MTR BBS restores the ability to bind adenosine more efficiently . Indeed , in SmMTAP , the tubercidin complex Q298 OE1 forms a weak H-bond ( 3 . 19Å ) within the O5' of tubercidin , similar to the interaction between H257 and O5' that is observed in human PNP . The side chain of Q289 showed a large conformational plasticity . The presence of the mutation S12T does not alter the phosphate loop compared to the SmMTAP WT structures . The main and side chains of T12 forms the same H-bonds with sulphate molecules observed in human MTAP ( Fig 10 ) . The mutant N87T also does not present any large structural differences in comparison to the wt SmMTAP structures; as discussed above , the consequence of this mutation is that the capacity to form a H-bond with phosphate/sulphate in PBS is restored ( Fig 10 ) . An interesting consequence of the N87T mutation is the presence of two water molecules near the atoms OD1 and ND2 of T87 mutant occupying approximately the same position of N87 side chain . The bulkier group of N87 prevents the binding of water molecules at these sites . The human MTAP shows equivalent water molecules in these positions . One water molecule interacts with S244 and Y222 , and the other interacts with the R54 and T207 side chains; these residues are all conserved in human MTAP . The water interactions with T207 and S224 are the same and are formed by the N87 side chain , thus indicating a necessary conservation of these interactions , even losing the interaction with the phosphate molecule due to the N87 substitution . The mutant Q289L was prepared to investigate the consequence of the lost H-bond between Q289 side chain and the nucleoside observed in tubercidin SmMTAP complex . The Q289L tubercidin complex permits a direct comparison between structures . A small but consistent difference was observed between Q289L and the WT SmMTAP tubercidin complex , particularly in chain A: an increase in movement at the end of Helix 9 in the Q289L mutants . The L289 permits a larger movement in the end of the helix compared to Q289; when comparing these structures with human MTAP , a larger difference was also observed for residues 277–281 ( human MTAP numbering ) . We also could obtain the structure of the double mutant S12T/N87T in the Apo form and in complex with adenine . No significant differences were observed between double and single mutants . All of the small differences were discussed above in single mutant structures . Even in the double mutant structures , the position of sulphate/phosphate was conserved . The consequence of these mutations are explained in the kinetics section below . Using a coupled assay with xanthine oxidase ( 12 ) we determined the kinetic parameters for MTA , adenosine , PO4 and 2'-deoxyadenosine . All measurements were made in sextuplicate in at least three different preparations . The results are shown in Table 2 . The SmMTAP has a lower KM for adenosine ( 3 . 14 ± 0 . 16 μM ) , 2'-deoxyadenosine ( 3 . 97 ± 0 . 41 μM ) and MTA ( 3 . 63 ± 0 . 14 μM ) , as observed for other parasites ( 11 ) . The KM for PO4 was also obtained ( 65 . 58 ± 4 . 69 μM ) . These data show that SmMTAP is well suited for both substrates ( adenosine and MTA ) , in clear opposition to the human MTAP , which displays a higher affinity for MTA as substrate compared to adenosine ( KM = 1 . 8 and 184 μM , respectively ) . The parasite Trypanosoma brucei has an MTAP with low KM for MTA ( 2 μM ) and adenosine ( 21 μM ) . Searching the Brenda database , we failed to find an MTAP with a lower KM for adenosine and 2'-deoxy adenosine than we found for SmMTAP . Therefore , we could assume that SmMTAP is the adenosine phosphorylase discovered by Miech et al . [2] using S . mansoni extracts . Despite the low KM for the MTA , adenosine and 2'-deoxyadenosine , the SmMTAP has high kcat values ( Table 2 ) and is 7 . 1 times greater for MTA compared to the human enzyme [39]; unfortunately , we could not perform a direct comparison for adenosine kcat due to the lack of literature data . The kcat values for adenosine and 2'-deoxyadenosine are 75 . 2 and 50 . 5% , respectively; the MTA kcat values indicates a slight catalytic preference for MTA . This is also reflected in the kcat/KM relationship . As discussed above , we chose three residues in the RBS and PBS for site-directed mutagenesis ( S12 , N87 and Q289 ) to investigate their roles in the kinetic parameters of MTA , adenosine , 2-deoxyadenosine and PO4 . We also generated double S12T/N87T and triple mutants S12T/N87T/Q289L . Interestingly , these mutants all reduced the KM for MTA with the S12T and N87T , showing decreases of approximately 3- and 5-fold . A reduction in kcat was observed for S12T and N87T ( 3- and 5-fold decrease ) . The double mutant appears to be conservative with respect to these parameters and shows a small increase in the kcat/KM parameter ( 1 . 6X ) , thus indicating a conservative role of this pair of residues in MTA catalysis . A different scenario was observed for adenosine . The triple mutant increased the KM value 2 . 3-fold , whereas other mutants did not have great effects on KM . The double and triple mutants have conservative kcat values; however , S12T , N87T and Q289L reduce the kcat value by 3 . 4- , 5 . 3- and 3 . 4- fold , respectively . Nonetheless , a great difference was observed in the kcat/KM values with respect to the wild-type enzyme . All mutants affected this parameter , reducing it by 5 . 5-fold ( N87T ) , ~ 2-fold ( S12T , Q289L ) and 2 . 7-fold for the triple mutant . When 2'-deoxyadenosine was used as substrate , a similar decrease in KM was observed for S12T ( in comparison to adenosine ) ; however , the N87T mutant showed a 2 . 6X increase in KM in relation to the wt SmMTAP with adenosine . This increase was not observed in the double mutant . The kcat was significantly altered for S12T ( 4 . 6X decrease ) , N87T ( 2X increase ) , and Q298L ( 2 . 8X decrease ) , and both double and triple mutants showed increased kcat values ( 1 . 6X and 1 . 3X , respectively ) . The kcat/KM value did not exhibit significant differences in S12T , N87T and triple mutants; the Q289L showed a 2-fold decrease . These mutations were all unable to revert the adenosine catalysis by SmMTAP when using the corresponding human MTAP residues , even the Q289L mutant , the side chain of which could form an H-bond with the ribose moiety of adenosine . However , the triple mutant showed an increase in KM and a decrease of catalytic efficiency for adenosine . This could be explained by the fact that the residues that interact with the adenine moiety in BBS are fully conserved between human and schistosome enzymes . Therefore , the efficient catalysis of adenosine is a result of other substitutions in the S . mansoni enzyme and/or the larger movements of the gate loop ( which is not observed in human MTAP ) . This is in line with Hammes [40] , who provided a holistic vision of enzyme structures , where the net of H-bonds within the structure is responsible for and collaborate to realize catalysis . As discussed above , all of the planned mutations were performed in both RBS and PBS; the SmMTAP has a lower KM for PO4 compared to the human enzyme ( 65 . 58 versus 320 μM ) , and we successfully reverted the KM to the value for the human MTAP in both Q289L and the triple mutant . These mutants showed an increase in KM of 4 . 4X and 4 . 1X , respectively . Intriguingly , the results show an increase of 158X in the PO4 KM for the S12T mutant . In contrast , the N87T mutant shows KM values near the wt SmMTAP , thus reflecting the consequence of an extra H-bond within the PO4 site and could thus compensate for the presence of T12 in double and triple mutants . The kcat/KM was also significantly reduced by 43X ( Q289L ) , 303X ( triple mutant ) and ~2X ( double mutant ) , thus indicating the importance of Q289 in the catalysis and possibly reflecting the importance of reduced flexibility at the end of Helix 9 . An intriguing observation was the capacity of wt SmMTAP to catalyze adenosine cleavage in the absence of phosphate . In this case , SmMTAP was extensively dialyzed against Hepes buffer prior to its utilization for kinetic experiments . We were not able to obtain kinetic data for hydrolysis with MTA . The S12T and Q289L mutants did not provide reliable data either . The wt SmMTAP KM for adenosine in the absence of phosphate is 4 . 6 μM , which is 1 . 4X higher than that obtained in the presence of phosphate; however , the kcat is 2-fold lower , and the kcat/KM is reduced 2 . 78-fold . The N87T mutant shows a 2 . 8-fold increase in KM , an increase in kcat ( 1 . 8X ) and a decrease in kcat/KM ( 1 . 53X ) . In the double mutant , the KM is increased approximately 12 . 3X , kcat is reduced 2-fold and kcat/KM is 25 . 7-fold lower than in the presence of phosphate . The triple mutant resembles the double mutant , with a KM and kcat that are 5 . 5X and 2 . 5 lower , respectively and a 14-fold reduction in the kcat/KM relationship . Interestingly , both double and triple mutants could restore adenosine cleavage in the absence of phosphate; indeed , the Q289L and 3M mutants show higher KM values for phosphate and low kcat/KM , thus demonstrating its great impact on the KM and catalytic efficiency of these mutants . Catalysis of nucleosides by nucleoside phosphorylases in the absence of phosphate was observed for bovine uridine phosphorylase , where the enzyme produces a free base and a glycal as products of catalysis [41] , and for calf spleen purine nucleoside phosphorylase [42 , 43] . This could be the reason for our failure to obtain adenosine or MTA complexes for wt SmMTAP , for which we only observed adenine in the active site , even when using ammonium sulphate in crystallization conditions .
In humans , the MTAP enzyme functions solely in the polyamine pathway by removing MTA produced by spermidine and spermine synthases . Thus , MTAP phosphorolysis is the only way to metabolize MTA in humans and , more generally , in mammals [16] . MTA is produced in the polyamine pathway , where two MTA molecules are produced via the conversion of putrescine into spermine . The MTA phosphorolysis by MTAP produces adenine and 5’-methythioribose-1-phosphate ( MTR1P ) . The adenine base is salvaged by the action of APRT , and MTR1P is converted into methionine by the methionine salvage pathway ( MSP ) . One advantage of the MSP may be the cost of sulfur assimilation; therefore , the MSP keeps the sulfur inside the cell , especially for species that live in sulfur-restricted environments [44] . In contrast , species living in sulfur-abundant environments lack MTR kinases and the MSP . The same study reported an advantage for possessing a nucleosidase: the adenine could be used as a purine source . S . mansoni genome analysis using either GeneDB [45] and KEGG reveals that the parasite lacks all enzymes of a polyamine pathway: ornithine decarboxylase ( E . C 4 . 1 . 1 . 17 ) , spermidine synthase ( E . C 2 . 5 . 1 . 16 ) , and spermine synthase ( E . C 2 . 5 . 1 . 22 ) , which produces MTA . Another intriguing fact is the destination of MTR1P in the methionine salvage pathway . This pathway is also completely absent in S . mansoni; indeed , there is no coding sequence for any of the enzymes involved in the transformation of MTR1P into methionine: methylthioribose-1-phospate isomerase ( E . C 5 . 3 . 1 . 23 ) , methylthioribulose-1-phospate dehydratase ( E . C 4 . 2 . 1 . 109 ) , Enolase-phosphatase E1 ( E . C . 3 . 1 . 3 . 77 ) , Acireductone dioxygenase 1 ( E . C 1 . 13 . 11 . 54 ) and aromatic-amino-acid transaminase ( E . C 2 . 6 . 1 . 57 ) . In higher eukaryotes , methionine is an essential amino acid that is required in diet supplementation and is one of the limiting amino acids . However , this is not the scenario for S . mansoni because the parasite lives in blood; therefore , methionine is not difficult to obtain because the principal source of amino acids is the human blood , where hemoglobin is found in endless stock for the parasite . Human hemoglobin has 1 . 7% of methionine in its composition . Certainly , host-parasite interactions generate selective pressure , which drives the redrawing of metabolic pathways and incurs the loss of a significant number of genes associated with biosynthetic functions because the parasite depends on the host to obtain its supply of metabolites and precursors [46] . This is true for the purine , sterols and fatty acid "de novo" pathways in S . mansoni [4 , 47 , 48] . Payne and Loomis [49] analyzed the complete genome of protists , Dictyostelium and six animals for the retention and loss of the biosynthetic amino acids pathways and concluded “when an organism becomes a consumer by eating other organisms , all of the amino-acids are available in the diet and no longer need to be synthesized” . If these pathways are not used in other essential functions , they are useless and dispensable . Genes in these nonessential pathways accumulate deleterious mutations , become non-functional and are eventually deleted from the genome . The absence of polyamine and methionine salvage pathways , the high activity of adenosine phosphorolysis , and the low KM and high kcat for adenosine make the SmMTAP an exclusive component of the purine salvage pathway . Thus , it is an adenosine phosphorylase , as described by Senft and collaborators more than 30 years ago . Recently , Savarese and El Kouni [50] isolated two adenosine phosphorylases from S . mansoni extracts: the first one cleaves MTA , adenosine and 2’-deoxyadenosine to adenine and their respective sugar phosphates , and the second utilizes guanosine , inosine , adenosine and 2’ deoxyadenosine , the last in clear contrast to our observations of S . mansoni PNP , for which adenosine is a weak inhibitor [38] . However , the MTAP enzyme described by Savarese does not present hydrolytic activity against MTA , as we observed for recombinant SmMTAP; however , a very low hydrolytic activity ( in the error range of measurements ) was observed for adenosine . Thus , we could assume that the first extract is the MTAP described here due to their similar activity characteristics and that the latter is another uncharacterized PNP isoform due to its guanosine/inosine/adenosine cleavage capacity . However , a direct comparison is impossible due to the absence of SDS-PAGE for both fractions ( to evaluate the monomer molecular weight ( MW ) ) , analytical gel filtration and/or Dynamic Light Scattering ( DLS ) to estimate the MW of these enzymes in solution . The discovery of MTAP/AP properties from S . mansoni confirms the findings of Senft and co-workers , who described an activity of adenosine phosphorylase that was distinct from that of the purine nucleoside phosphorylase enzyme . Truly , our previous finding that adenosine is a weak inhibitor of SmPNP activity discards PNP as being responsible for this activity [38] . The S . mansoni genome data demonstrate a complete absence of the methionine salvage and polyamine pathways and suggest a different role of SmMTAP in parasite metabolism . Therefore , we can assume that SmMTAP/AP is the sole protein responsible for the conversion of adenosine ( and , to a lesser extent , MTA ) into adenine , which is subsequently converted into AMP by APRT ( the metabolic branch responsible for 70% of the conversion of the incoming adenosine into AMP ) . Site directed mutagenesis experiments could not revert the adenosine phosphorolysis phenotype due to fully conserved BBS residues; thus , other residues that are not directly involved in catalysis could be responsible for this activity . The gate loop , which undergoes large conformational changes between APO and ligand structures , could be involved , in contrast to the lack of conformational plasticity observed for human MTAP . The high adenosine phosphorolysis activity , in contrast to that observed in human MTAP , probably evolved due to selective pressure from the abundance of adenosine in human serum , which forced the parasite to be more adapted to utilize adenosine . This finding is in line with the absence of MTA producer enzymes and destination enzymes , which could handle the MTR1P product of MTA phosphorolysis . An intriguing open question is about the role of SmMTAP in the S . mansoni vomitus [51] , where the enzyme could be responsible for the conversion of adenosine into adenine , with the subsequent incorporation of adenine into the worm nucleotide pool . The presence of adenosine phosphorylase activity in Fisher medium after worm removal was observed by Sent et al . [7] . This finding is in line with the adenine incorporation in S . mansoni , which was 6 to 10 times higher than observed for mammalian cell lines [6] . This SmMTAP structure corroborates our efforts to solve all of the structures of the enzymes that are involved in purine salvage pathway; indeed , this is the fourth different structure solved by our group , following purine nucleoside phosphorylase [32–38] , adenosine kinase [52] , and adenylate kinase [53] . We hope that this effort increases the structural basis of purine metabolism in S . mansoni and , together with kinetic data , will aid in the development of new alternative treatments for this important but neglected disease , for which SmMTAP is described as a potentially chemotherapeutic target .
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The huge challenge in parasitic chemotherapy development is finding a specific compound to attack the parasite organisms without damaging their host . Schistosoma mansoni , which is the causative agent of schistosomiasis , is one of the major health concerns in the developing world . Purine bases are essential for organisms that make DNA , RNA and energetic molecules during parasitic growth and egg laying . The parasites depend entirely on re-utilizing existing purines , not being able to synthesize them from more simple molecules . The adenosine phosphorylase is an important activity for this process and kinetic assays we performed with this S . mansoni MTAP confirm that it displays this specific activity that is not present in the human metabolism . Therefore , understanding the properties of this enzyme is an important step in achieving an efficient anti-schistosomiasis drug with minimal collateral effects to humans .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
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"schistosoma",
"invertebrates",
"schistosoma",
"mansoni",
"glycosylamines",
"crystal",
"structure",
"chemical",
"compounds",
"phosphates",
"helminths",
"enzymes",
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"matter",
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"animals",
"organic",
"compounds",
"purines",
"crystallography",
"adenosine",
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"proteins",
"transferases",
"chemistry",
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2016
|
Crystal Structure of Schistosoma mansoni Adenosine Phosphorylase/5’-Methylthioadenosine Phosphorylase and Its Importance on Adenosine Salvage Pathway
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Vaccinia mature virus requires A26 envelope protein to mediate acid-dependent endocytosis into HeLa cells in which we hypothesized that A26 protein functions as an acid-sensitive membrane fusion suppressor . Here , we provide evidence showing that N-terminal domain ( aa1-75 ) of A26 protein is an acid-sensitive region that regulates membrane fusion . Crystal structure of A26 protein revealed that His48 and His53 are in close contact with Lys47 , Arg57 , His314 and Arg312 , suggesting that at low pH these His-cation pairs could initiate conformational changes through protonation of His48 and His53 and subsequent electrostatic repulsion . All the A26 mutant mature viruses that interrupted His-cation pair interactions of His48 and His 53 indeed have lost virion infectivity . Isolation of revertant viruses revealed that second site mutations caused frame shifts and premature termination of A26 protein such that reverent viruses regained cell entry through plasma membrane fusion . Together , we conclude that viral A26 protein functions as an acid-sensitive fusion suppressor during vaccinia mature virus endocytosis .
Virus entry represents the initial stage of infection and is a target for developing new antiviral therapeutics . Poxvirus is a family of enveloped DNA viruses with genomes of ~200 kilobases [1] . Vaccinia virus , an orthopoxvirus , is a model system for investigating poxvirus entry into host cells , producing mature ( MV ) and extracellular virus ( EV ) [2–4] . Vaccinia MV attaches to cell surface glycosaminoglycans and extracellular matrix laminin [5–10] . It then clusters at lipid rafts , triggering the integrin β1-CD98-PI3K signaling cascade [11 , 12] to induce actin-dependent endocytosis that may [13] or may not involve apoptotic mimicry [14–16] . After internalization , vaccinia MV is trafficked in vesicles inside the cells , with subsequent endosomal acidification triggering viral membrane fusion with the vesicular membrane to release viral cores into the cytoplasm [17–20] . How vaccinia virus triggers fusion with host cells remains unclear . Many enveloped viruses contain a viral fusion protein that induces conformational changes at low pH [21–23] . Conformational change of viral fusion proteins exposes a hydrophobic terminal fusion peptide [24 , 25] or internal fusion loop [26 , 27] that can be inserted into host membranes . Subsequent conformational changes and oligomerization of viral fusion proteins then triggers fusion of viral and host membranes . Vaccinia MV employs a highly conserved eleven-component fusion protein complex to mediate virus fusion with cells [3 , 28] , but how it functions remains unknown [4] . Vaccinia MV exhibits broad infectivity , acting via endocytosis or plasma membrane fusion [9 , 29 , 30] , depending on vaccinia virus strains and cell types [17 , 31] . We previously demonstrated that the WR strain of vaccinia MV uses viral A26 protein for the endocytosis pathway , whereas deletion of A26 protein induced the plasma membrane fusion pathway in HeLa cells [32 , 33] . However , loss of A26 protein renders viral MV particles resistant to bafilomycin ( BFLA ) without loss of fusion activity , suggesting that A26 protein is the target of acid regulation , not the viral fusion complex [32] . The A26 protein binds to A16 and G9 proteins of the viral entry fusion complex at neutral pH and , when purified MV was treated with acidic buffer , the A26-A27 protein complex dissociated from MVs at low pH [33] , inspiring our model wherein A26 protein is an acid-sensitive fusion suppressor of MV ( Fig 1A ) . In this model , A26 protein binds to viral fusion complex to suppress MV fusion at neutral pH . However , the acidic pH of endosomes triggers conformational changes in A26 protein , which is subsequently released from the viral fusion protein complex , resulting in viral and vesicular membrane fusion . In the absence of A26 protein , viral fusion protein complex becomes fusion-competent at neutral pH , triggering efficient fusion with the plasma membrane , consistent with electron microscopy ( EM ) data [32 , 33] . Here , we provide genetic , biochemical and structural evidence that A26 protein is a fusion suppressor that regulates vaccinia MV membrane fusion through acid-dependent conformational changes .
We generated two N-terminal deletion constructs of the A26 open-reading frame ( ORF ) , in which we removed amino acids ( aa ) 1–75 or aa 1–320 of A26 protein ( Fig 1B ) . Each deletion construct was fused in-frame with N-terminal flag sequences and inserted into a non-essential thymidine kinase ( tk ) locus of the WR-ΔA26 virus , which deleted the A26 ORF ( Fig 1B ) . We did not generate any C-terminal deletions of the A26 ORF because this region is required for A26 protein binding to viral A27 protein and subsequent packaging into MV particles [34 , 35] . Recombinant viruses expressing WR-A26 ( 76–500 ) or WR-A26 ( 321–500 ) were isolated and plaque-purified . A recombinant vaccinia virus expressing full-length flag-tagged A26 protein , named WR-A26 , was also included [36] . HeLa cells were infected with individual virus at a multiplicity of infection ( MOI ) of 5 plaque-forming units ( PFU ) per cell and harvested at 24 hours post infection ( hpi ) to determine MV growth ( Fig 1C ) . Control WR-A26 grew approximately 100-fold at 24 hpi . WR-A26 ( 76–500 ) exhibited significantly reduced MV yield in the same timeframe , whereas WR-A26 ( 321–500 ) yield was similar to that of control WR-A26 virus . None of these three viruses exhibited defective virus assembly , with each presenting large amounts of MV in cytoplasm of infected cells at 24 hpi ( Fig 1D ) , consistent with previous results demonstrating that A26 protein is not required for MV assembly [10] . We purified MV particles via CsCl gradient purification and found that WR-A26 , WR-A26 ( 76–500 ) and WR-A26 ( 321–500 ) presented similar morphologies under EM ( enlarged MV images in the insets of Fig 1D ) . Immunoblot analyses of purified MV also revealed comparable A26 protein levels in MV particles of WR-A26 , WR-A26 ( 76–500 ) and WR-A26 ( 321–500 ) ( Fig 1E ) . We assessed whether the reduced growth of WR-A26 ( 76–500 ) virus reflects low MV particle infectivity by counting how many virus particles are required to initiate a single infection event in HeLa cells , i . e . , to determine the particle-to-PFU ratio ( Table 1 ) . For the control WR-A26 MV particles , this ratio is ~43 , whereas for WR-A26 ( 76–500 ) it is ~88 , demonstrating that removal of aa 1–75 of A26 protein significantly reduced MV infectivity . Surprisingly , further deletion of A26 protein , aa 1–320 , recovered MV infectivity of WR-A26 ( 321–500 ) to a ratio of ~33 , i . e . , not statistically different from control WR-A26 MV infectivity ( an outcome we explain in the next section ) . Based on our model ( Fig 1A ) , A26 protein contains an acid-sensing or acid-sensitive region responsible for inducing acid-dependent conformational changes , as well as a fusion suppressor region to suppress fusion activity at neutral pH . To establish if these region functions are absent in our WR-A26 ( 76–500 ) and WR-A26 ( 321–500 ) protein constructs , we adopted a cell-cell fusion assay to investigate virus-cell membrane fusion ( Fig 2A , 2B and 2C ) . Mock-infected cells did not fuse at either neutral or acidic pH , so GFP- and RFP-expressing cells were well separated . Control WR-A26 virus needs a low pH endocytic environment to initiate fusion , so surface-bound MV did not induce cell-cell fusion at neutral pH . However , brief treatment of these surface-bound MV with low pH buffer created an acidic environment that mimicked endosomes , leading to conformational changes of the A26 protein that activated virus-mediated cell-cell fusion to produce double-fluorescent fused cells . In contrast , WR-ΔA26 MV lacked a fusion suppressor , so cell-cell fusion was triggered by virus infection at both neutral and acidic pH . Interestingly , WR-A26 ( 76–500 ) did not fuse at either neutral or acidic pH , thus acting like a pH-independent fusion suppressor and indicating that aa 1–75 represents the acid-sensitive region of A26 protein . Finally , WR-A26 ( 321–500 ) triggered robust membrane fusion at both neutral and acidic pH , i . e . , similar to WR-ΔA26 , suggesting that the fusion suppressor region is absent in WR-A26 ( 321–500 ) and that aa 76–320 represents the fusion suppressor region of A26 protein . Based on the fusion quantification data in Fig 2C , we divided the % fusion at low pH ( 4 . 7 ) by that at neutral pH ( 7 . 4 ) in order to obtain an “acid fusion index” that reflects the acid dependence of each MV construct ( Fig 2D ) . Only endocytic WR-A26 had a high acid fusion index , whereas all other viruses lost their acid dependence with WR-ΔA26 and WR-A26 ( 321–500 ) fusing well at both pH and WR-A26 ( 76–500 ) fusing poorly at both pH . Therefore , we conclude that the N-terminal region of aa 1–75 of A26 protein is important for acid-sensing or acid sensitivity and the middle region of aa 76–320 is required for fusion suppression ( Fig 2E ) . This conclusion fits well with the low infectivity of WR-A26 ( 76–500 ) MV and the normal infectivity of WR-A26 ( 321–500 ) MV ( described in the previous section ) , since this latter virus exhibited the ability to enter cells via plasma membrane fusion , just like WR-ΔA26 virus . The above-described analyses prompted us to investigate the N-terminal aa sequences of A26 protein . Since two-dimensional ( 2D ) 1H-15N heteronuclear single quantum coherence ( HSQC ) serves as a reliable measure of secondary structure and conformational change in solution , we then performed 2D HSQC experiment to examine the N-terminus of A26 . To achieve better protein solubility and stability critical for NMR study , we fused A26 ( aa 1–91 ) coding region with thioredoxin ( TRX ) , and purified the recombinant fusion protein TRX-A26 ( 1–91 ) . In addition , we also purified wild type TRX control protein and a mutant TRX-fused protein TRX-A26 ( 1–91 ) H48 , 53R , in which His48 and His53 were mutated to Arg . We then applied 2D HSQC experiment to TRX-A26 ( 1–91 ) , TRX-A26 ( 1–91 ) H48 , 53R and TRX respectively ( Fig 3B–3D ) . Notably , the 2D HSQC spectra of all three proteins—TRX , TRX-A26 ( 1–91 ) and TRX-A26 ( 1–91 ) H48 , H53R—at pH 8 are somewhat similar ( blue in Fig 3B–3D ) , suggesting that the 2D spectral signals are dominated by those of TRX protein . Furthermore , the spectral patterns of control TRX at pH 6 and pH 8 are nearly identical ( Fig 3C ) , demonstrating that TRX is pH-insensitive . However , recombinant TRX-A26 ( 1–91 ) at pH 6 exhibits a different 2D spectral pattern ( red in Fig 3B ) from that at pH 8 ( blue in Fig 3B ) . At pH 6 , the amide-1H signals gave rise to a narrow dispersion within 8–8 . 5 ppm , indicating a partially unfolded conformation [37]; in contrast , at pH 8 , the amide-1H signals displayed a much wider distribution 7–10 ppm , indicative of a structured conformation . The data thus suggested that , when fused with TRX , the A26 ( 1–91 ) fragment induced significant conformational changes in the TRX-A26 ( 1–91 ) fusion protein at low pH . However , recombinant TRX-A26 ( 1–91 ) H48 , H53R fusion protein exhibited similar 2D spectral patterning at pH 6 and pH 8 ( Fig 3D ) , confirming that H48 and H53 are responsible for pH sensitivity of TRX-A26 ( 1–91 ) in vitro . To further confirm this conclusion , we then removed the TRX tag from the above-mentioned fusion proteins and performed circular dichroism ( CD ) spectroscopy to analyze conformational changes of recombinant A26 ( 1–91 ) protein and A26 ( 1–91 ) H48 , H53R mutant protein at different pH , ranging from 5 . 1 to 8 . 5 ( Fig 3E and 3F ) . The CD spectrum of recombinant A26 ( 1–91 ) protein exhibited α-helical ellipticity at 208 and 222 nm ( Fig 3E ) . The ellipticity decreased as the pH value decreased , suggesting a transition from an α-helix to a random coil . In contrast , the A26 ( 1–91 ) H48 , H53R mutant protein was insensitive to pH alteration from 5 . 2 to 8 . 5 ( Fig 3F ) although the secondary structure of the mutant protein may appear similar to A26 ( 1–91 ) in α-helix content . Based on these data , we concluded that the N-terminal region ( 1–91 ) of A26 protein is sufficient for low pH-dependent conformational changes , and that His48 and His53 are essential for acid sensitivity of A26 ( 1–91 ) protein in vitro . To demonstrate that His48 and His53 of A26 protein are indeed involved in acid-dependent membrane fusion of MV in cells , we created a recombinant vaccinia virus ( WR-A26-H2R ) that expresses flag-tagged A26-H48 , H53R double mutant protein ( A26-H2R protein ) in the infected cells ( Fig 4A and 4B ) . Unlike wild-type A26 protein , A26-H2R mutant protein should fail to undergo conformational changes in response to low environmental pH . We also generated another recombinant virus A26-H3R that contains an extra H92R mutation in flag-tagged A26 protein , in addition to H48R and H53R ( Fig 4A and 4B ) . Immunoblot analyses revealed comparable levels of WR-A26 , A26-H2R and A26-H3R proteins in the infected cells ( Fig 4C ) and in purified MV particles ( Fig 4D ) . Although WR-A26-H2R and WR-A26-H3R mutant viruses exhibited normal MV assembly in the infected cells ( Fig 4E ) , MV growth was reduced ( Fig 4F ) and MV particles presented very low infectivity ( Table 1 ) , reminiscent of our observations of the WR-A26 ( 76–500 ) deletion virus . In cell-cell fusion assays , WR-A26-H2R and WR-A26-H3R mutant viruses did not trigger cell-cell fusion at either neutral or low pH ( Fig 4G , quantified in Fig 4H ) , similar to WR-A26 ( 76–500 ) virus , suggesting that the A26-H2R and A26-H3R proteins are constitutive fusion suppressors , as evidenced by their low acid fusion indexes ( Fig 4I ) . Taken together , these mutational studies show that H48 and H53 are required for the functioning of the acid-sensitive region of A26 protein during vaccinia MV-mediated membrane fusion . Additional mutation of His92 did not enhance the mutant virus phenotype . To further understand how His48 and His53 mediate A26 protein conformational change , we endeavored to obtain a crystal structure of A26 protein . We generated various A26 gene constructs for protein expression in E . coli and only A26 ( 1–420 ) and A26 ( 1–420 ) -C43C342A were successfully purified . However , we failed to obtain crystals from either proteins , suggesting that A26 ( 1–420 ) and A26 ( 1–420 ) -C43C342A still contain some disordered regions . Our limited trypsin digestion identified aa 395–420 as an unstable region so we generated recombinant A26 ( 1–397 ) protein and purified it through affinity and monoQ ion exchange chromatography before solving its crystal structure ( Table 2 and Fig 5 ) . The overall A26 ( 1–397 ) structure consists of 18 α-helices and 6 β-strands ( Fig 5A and 5B ) , with an N-terminal α-helical domain ( NTD; aa 17–228 ) and a C-terminal β-sheet domain ( CTD; aa 229–364 ) . The total solvent accessible surface area ( SA ) of A261-397 is 14606 Å2 , and the buried area between these two domains are 4252 . 3 Å2 the interface ( the contact areas on NTD and CTD are 2387 and 1865 . 3 Å2 , respectively ) . Additionally , an inter-domain disulfide bond is present between Cys43 and Cys342 , consistent with our previous mutational analyses [35] . To address the novel fold of this structure , we used the DALI server ( http://ekhidna2 . biocenter . helsinki . fi/dali/ ) to perform a structural homolog analysis . The results showed that the overall structure of A261-397 does not have any significant hit . The most similar protein to A261-397 is the C-terminal MIF4G domain in NOT1 ( PDB ID 6H3Z with RMSD above 11 . 4 Å ) . Moreover , the A261-397 NTD exhibits only minor similarity to importin protein ( PDB ID:3zkv; with RMSD above 6 Å ) . In comparison , the folding of A261-397 CTD is similar to gamma crystallin S ( PDB ID: 1m8u; RMSD = 2 . 2 Å ) . However , since sequence identity between the A261-397 CTD and gamma crystallin S is below 15% , it does not suggest a close relationship between these two proteins . Consequently , the A261-397 structure appears to present a novel fold with two distinct domains . Many viral fusion proteins exhibit pH-dependent conformational changes that are mainly controlled by electrostatic repulsion [39] . Although A26 protein is a fusion suppressor and not a viral fusion protein , its ability to respond to acidic environments suggests that electrostatic replusion may also contribute to its conformational changes at low pH . In general , two classes of paired amino acids are involved in pH-dependent electrostatic repulsions within a protein , i . e . , His-cation repulsion at acidic pH and anion-anion ( Ani-Ani ) repulsion at neutral pH [39] . For His-cation pairs , the histidine residues are usually close ( < 7 Å ) to other His or basic residues ( Arg or Lys ) . Therefore , we investigated whether any His-cation or Ani-Ani pairs are present in the A26 ( 1–397 ) structure . As shown in Fig 5C , most His-cation or Ani-Ani pairs are located around helix α2 that hosts His53 ( green arrow in Fig 5C ) , so upon encountering the acidic endosomal pH , charge repulsion produced by the His-cation pair between His53 and Arg57 destablizes the conformation of helix α2 . It is worth noting that His53 is also cation-paired with Arg312 and His314 , both of which are located in the CTD of A26 ( 1–397 ) , so electrostatic repulsions of His53 at low pH may also destabilize the interactions between helix α2 and the CTD . Furthermore , His48 ( green arrow in Fig 5C ) is His-cation-paired with Lys47 in helix α2 . We observed that an Asp308-Asp310 pair is also adjacent to this region . In another notable obervation , the predicted pKa of the residues that involve in His-cation or Ani-Ani pairs ( S1 Table ) are usually low ( below 5 ) and most residues with low pKa are found in the helix α2 region , indicating that these residues prefer proton release . Thus , our crystal structure of A26 ( 1–397 ) protein strongly supports that His-cation pairs involving both His48 and His53 within the N-terminal region most likely contribute to structural alterations by increasing electrostatic repulsions under acidic conditions . We performed in vitro mutagenesis to express an A26 mutant protein ( A26-H2-CAT ) that contains K47D , R57D , R312D and H314R mutations to reduce cation-mediated repulsion at low pH via His48 and His53 ( Fig 6A ) . As expected , yield of the recombinant WR-A26-H2-CAT virus ( Fig 6B ) at 24 hpi was significantly reduced ( Fig 6C ) . Purified WR-A26-H2-CAT MV particles contained mutant A26 protein of the correct size ( Fig 6D ) , but exhibited low infectivity with an increased particle-to-PFU ratio of ~147 ( Table 3 ) . Importantly , the WR-A26-H2-CAT mutant virus triggered less cell-cell fusion at both neutral and acidic pH ( Fig 6E and 6F ) and it presented a low acid fusion index ( Fig 6G ) . Thus , we conclude that electrostatic repulsion induced by the N-terminal-protonated His48 and His53 residues and their surrounding basic aa ( K47 , R57 , R312 and H314 ) is essential for conformational changes of A26 protein at low pH . Interference with these conformational changes will inhibit subsequent membrane fusion of an endocytic vaccinia virus . During our experiments on HeLa cells , we noticed that WR-A26 ( 76–500 ) recombinant virus formed plaques that were slightly smaller than those of control WR-A26 and recombinant WR-A26 ( 321–500 ) viruses ( Fig 7A ) . Interestingly , WR-A26-H2R , WR-A26-H3R and WR-A26-H2-CAT recombinant virus all formed tiny plaques on HeLa cells . These A26 mutant viruses appeared unstable , generating spontaneous “large plaque” revertants during early virus passaging and propagation ( red arrows in Fig 7A ) . Therefore , we isolated several of the large plaque revertants ( Rev ) from the WR-A26-H2R , WR-A26-H3R and WR-A26-H2-CAT mutant viruses and analyzed their protein expression in the infected HeLa cells . As shown in Fig 7B , the size of the A26 protein in all these large-plaque revertant viruses was either smaller relative to control or the protein was completely absent in the infected cells . We suspected that this outcome was due to second-site mutations within the A26 ORF so we purified viral genomic DNA from cells infected with the WR-A26-H2R-Rev1 , WR-A26-H3R-Rev1 and WR-A26-H2-CAT-Rev1 revertant viruses and subjected it to whole genome sequencing ( results summarized in Table 4 ) . All revertant viruses retained the designed A26 mutations present in the parental WR-A26-H2R , WR-A26-H3R and WR-A26-H2-CAT mutant strains , but they also all contained an extra intragenic deletion in the A26 ORF that resulted in a frame-shift and premature termination of A26 protein translation ( Fig 7C ) . Our sequencing results are consistent with the immunoblots ( Fig 7B ) , although some small A26 fragments were not detected in the lysates , probably due to rapid degradation . WR-A26-H2R-Rev1 and WR-A26-H2-CAT-Rev1 genomes contained no other gene mutations , whereas WR-A26-H3R-Rev1 contains a C-to-A mutation in a pseudogene B3R ORF ( a truncated ortholog of camelpox viral gene 176R ) . The camelpox 176R encodes a schlafen-like protein in virus-infected cells , but a screening of 16 vaccinia viruses revealed no evidence of B3R expression [40] . We conclude that all three revertant viruses host second-site mutations that only affect A26 protein function . Since the A26 fragments in all of these revertant viruses are much shorter and lack the C-terminal A27-interacting region ( Fig 7C ) , they are unlikely to be packaged into revertant MV particles . Accordingly , we anticipated that these three revertant viruses would exhibit a phenotype similar to that of WR-ΔA26 virus . Indeed , the WR-A26-H2R-Rev1 , WR-A26-H3R-Rev1 and WR-A26-H2-CAT-Rev1 viruses mediated clear cell-cell fusion under neutral pH , just like WR-ΔA26 ( Fig 7D ) , and presented acid-independent fusion activity ( Fig 7E ) . Therefore , by mutating A26 protein to eliminate His-cation-mediated repulsion at low pH , we created a constitutive suppressor for viral membrane fusion so mutant MV infectivity diminished significantly . However , second-site mutations in the A26 gene resulted in revertant viruses regaining MV infectivity ( Tables 1 and 3 ) and exhibiting normal virus yields ( Fig 7F ) through plasma membrane fusion . Successful selection of these revertant viruses provides strong evidence that vaccinia MV can switch between the endocytosis and plasma membrane fusion entry pathways , mediated by A26 protein on MV . Most importantly , we have uncovered the structure of the N-terminal region of A26 protein and provide mechanistic insights demonstrating that electrostatic repulsion of His48 and His53 is critical for controlling acid-dependent conformational change of A26 protein prior to virus-mediated endocytic membrane fusion .
Poxviruses are very large and are known to contain multiple proteins of overlapping or redundant functions . Vaccinia virus contains four envelope proteins for cell attachment [5–8 , 10] , whereas viral membrane fusion requires a separate fusion protein complex of 11 components that specifically performs membrane fusion ( reviewed in [3 , 4] ) . Therefore , vaccinia virus has evolved two separate sets of envelope proteins specialized for cell attachment and membrane fusion , respectively , during cell entry . Many studies have reported that vaccinia virus entry pathways vary depending on virus strains and cell types [17 , 31 , 41] . How can virus entry pathways be strain-dependent ? Using proteomics and genetic complementation analyses , we previously showed that A26 protein determines MV entry pathways in several cell lines [32] . A26+ strains , such as the WR and IHD-J strains , employ endocytosis to enter HeLa cells , whereas A26- strains , such as MVA and Copenhagen , employ a plasma membrane fusion pathway [32 , 33] . Viral endocytosis would appear to be an optimal mode of virus entry into cells since no envelope proteins or viral membranes remain on the host cell surface for host B and T cell detection . However , under certain conditions when endocytosis becomes a less optimal route for A26+ vaccinia virus to enter cells , deletion of the A26 ORF results in A26- MV progeny that can infect cells through plasma membrane fusion , thereby broadening the host range . Another advantage of having multiple entry pathways is to avoid innate immune sensing and antiviral signaling activation . We recently infected murine bone marrow-derived macrophages ( BMDM ) with WR or WR-ΔA26 virus and found that the IFNβ-Stat1 signaling pathway was preferentially induced by endocytic WR virus but not by WR-ΔA26 virus [36] . Consequently , WR-ΔA26 exhibited enhanced virulence in mice compared to WR vaccinia virus [36] . Here in this study , we have used genetic , biochemical and structure analyses to provide strong evidence supporting that vaccinia A26 viral protein functions as an acid-sensitive fusion suppressor of MV particles during virus endocytosis . To demonstrate the critical role of the pH-dependent conformational changes , we purposely generated His48R and His53R mutations ( A26-H2R ) in the N-terminal domain of A26 protein so that the acid-sensing region is rendered pH-independent . We assume that the N-terminal domain in the A26-H2R mutant may structurally mimic the low pH conformation because of constitutive repulsion forces even in neutral pH environments . However , this scenario does not necessarily mean that the conformation of the fusion suppressor domain also changes during assembly into MV particles . Based on the A26 crystal structure , we generated H2-CAT mutations such that the resulting N-terminal domain structure of the A26-H2-CAT protein also becomes pH-independent but , in this case , His-Cation repulsion was replaced by His-Anion attraction . Therefore , we anticipated that the N-terminal domain of A26-H2-CAT mimics the neutral pH conformation , even in acidic environments . Despite different structural mimics being generated in the N-terminal regions of A26-H2 and A26-H2-CAT , both proteins were acid-insensitive and constitutively suppressed fusion . These outcomes demonstrate that pH-dependent conformational changes via His-Cation repulsion , as opposed to a particular acid-stable N-terminal domain structure per se , are essential for regulating membrane fusion activation . The A26-H2R and A26-H2R-CAT mutations eliminated the acid-dependent response and these mutant proteins retain fusion-suppressing functions . Taken together , our deletion and mutagenesis data support that His48 and His53 in the N-terminal domain are protonated at low pH , creating electrostatic repulsion with surrounding residues ( K47 , R57 , R312 and H314 ) that results in conformational changes . Our model is also consistent with the data from the A26 ( 76–500 ) deletion protein , which behaves as a constitutive fusion suppressor upon deletion of the acid-sensing domain . Finally , although we do not have the crystal structure of the C-terminal region of A26 protein , we have generated other C-terminal His-to-Arg mutant viruses , such as A26H357R , A26H425 , 432 439R , A26H439 , 452 , 453R and A26H425 , 432 , 439 , 452 , 453R . All these A26 C-terminal mutant viruses expressed A26 mutant proteins of correct size , formed plaques of normal size , and grew to high titers , suggesting that , in contrast to His48 and His53 , these C-terminal His residues have a limited role in A26-mediated MV entry and fusion regulation . In our A26 crystal structure obtained at neutral pH , His48 and His53 are located in the helix α2 , which is strategically sandwiched between N-terminal helix clusters and C-terminal beta strands ( Fig 5C ) . This implies that the helix α2 is important for maintaining protein structure stability at neutral pH . The A26 protein structure revealed that these C-terminal beta sheets are distinct from the N-terminal helix cluster , with a sole intra-molecular cysteine disulfide bond formed between C43 and C342 , suggesting that the C-terminal domain may stabilize the helix-rich N-terminal domain or vice versa . Currently , we do not have an A26 crystal structure under conditions of low pH nor for A26-H2R mutant protein at neutral pH so we do not know how the N-terminal domain alters A26 protein structure under the acidic condition . To investigate pH-mediated changes of A26 by using the current results , we employed Discovery Studio [42] to produce an A261-397 model at pH 4 . 7 [A261-397 model ( pH 4 . 7 ) ] . We first compared the surface electrostatic potential between A261-397 and A261-397 ( pH 4 . 7 ) model ( S1 Fig ) . Our results show that the NTD of A261-397 is highly positively charged , and the CTD of A261-397 is relatively negatively charged . Furthermore , we identified a positively-charged cavity ( dotted green box in S1 Fig , panel A ) between two domains . The cavity is formed by the α2 helix region , which we propose plays an important role in the pH-dependent regulation of A26 . Next , we endeavored to address the issue of pH-mediated changes in A26 . Since the crystal structure of A261-397 was obtained from a neutral pH , we first used Discovery Studio to produce a model of A261-397 at pH 4 . 7 , as described in materials and methods . We used the final conformation for subsequent analysis ( e . g . to calculate surface electrostatic potential and solvent accessibility , etc . ) . We then compared the structures and surface charges of the A261-397 and the A261-397 ( pH 4 . 7 ) model . In the A261-397 ( pH 4 . 7 ) model , the low pH enriches the positive charges and reduces the negative charges on the protein surface ( S1 Fig , panel B ) . We also observed a significant difference in the low pH model in terms of the region comprising the α1 and α2 helices , both of which presented a loosely coiled structure . This outcome may be related to electrostatic repulsions caused by the enrichment of positive charges in this region . Although our low pH model seems to support our hypothesis , it will be necessary to resolve the actual structure of A261-397 at low pH for verification . The solvent accessibility ( SA ) of A261-397 and A261-397 ( pH 4 . 7 ) model were calculated using Discovery Studio [42] . However , the SA difference between overall protein of A261-397 and A261-397 model ( pH 4 . 7 ) is not much ( 14606 and 14357 Å2 , respectively , representing a difference of 1 . 7% ) . We also established the SA of each residue for both structures ( S2 Table ) . Using a threshold for individual residues of a 15% difference in SA between structures , we predicted the SA of 27 residues is reduced in the A261-397 ( pH 4 . 7 ) model , whereas it was increased for another 17 residues . Notably , although the SA of most residues that involve His–Cation and Ani-Ani pairs were not greatly altered , we observed pronounced SA changes adjacent to the α2 helix ( S2 Fig ) , suggesting that this region may undergo a conformational change at low pH . Again , this analysis was based on a model of A261-397 at low pH , it is necessary to resolve the actual structure of A26 at low pH to precisely elucidate the pH-dependent changes in A26 . It is worth noting that though A26 protein only exists on MV and not on EV , it has been hypothesized that A26 protein in cells can negatively regulate MV egress to Golgi to form EV [43 , 44] . Since all of our designed acid-insensitive A26 mutant viruses exhibit small plaque sizes , we rationalized that conformational changes of A26 protein may also control MV to EV egress at late phase in infected cells . Subsequent isolation of revertant viruses from the WR-A26-H2R , WR-A26-H3R and WR-A26-H2-CAT mutant viruses was unexpected . However , these revertant viruses clearly demonstrate how vaccinia mature virus can cope with detrimental mutations of A26 protein and how , through second-site mutations in A26 protein , the revertant mature virus regains host cell entry ability by switching from endocytosis to plasma membrane fusion ( Fig 8 ) . Apart from the three revertants reported in Fig 7 , we analyzed additional revertants with the large plaque phenotype—Rev2 and 3 from WR-A26-H2R; Rev2 , 3 and 4 from WR-A26-H3R; and Rev2 , 3 , and 4 from WR-A26-H2-CAT ( S3 Fig . ) —and immunoblots showed that all of these revertants contain a smaller form of A26 protein or no A26 protein at all ( S4 Fig ) . PCR amplification and sequencing of A26L genes from these viral genomes revealed additional second-site mutations within the A26L ORF ( S5 Fig . ) that resulted in a frame-shift and premature termination of A26 protein ( S6 Fig . ) , consistent with our immunoblot data and supporting our model shown in Fig 8 . As expected , all of these revertant viruses infected cells via plasma membrane fusion , and displayed robust cell-cell fusion at both neutral and acidic pH ( S7 Fig ) . Previous crystallization experiments on viral fusion proteins under different pH have provided strong evidence to support acid-dependent conformational changes of viral fusion proteins [23 , 45–51] . At low pH , Type I , II and III fusion proteins exposed an N-terminal fusion peptide or internal fusion loop for target membrane insertion , followed by fusion protein oligomerization , hemifusion and subsequent complete fusion between viral and host membrane [23 , 26 , 52 , 53] . Although A26 protein is not a viral fusion protein , its acid-dependent conformational changes provide a new paradigm of membrane fusion activation , i . e . activation of viral membrane fusion by “de-repression” of a viral fusion suppressor . In the report by Gershon et al . , the N-terminal region of A26 was proposed to interact with the N-terminal of G9 and the C-terminal of ATI based on crosslinking analysis [54] . However , the protein structures of G9 , A16 and ATI remain unresolved . Without this structural information , it is difficult to evaluate the interfaces between these proteins and A261-397 . We speculate that a conformational change at low pH may affect the G9 and ATI interfaces on A26 , resulting in disassociation of A26 from these two proteins , but the actual structures of A26 at low pH , as well as G9 , A16 and ATI will be necessary to verify this possibility . A recent crystal structure study of the Gn glycoprotein of Rift Valley Fever Virus ( RVFV ) also revealed that Gn protein shields the hydrophobic fusion loops of the Gc fusion protein to prevent premature fusion of RVFV [55] . Despite a lack of similarity between the RVFV Gn protein and vaccinia A26 protein , conformational changes of viral regulatory proteins may represent a new mechanism to activate viral fusion proteins for membrane fusion .
An African green monkey kidney cell line BSC40 ( provided by Dr . Sridhar Pennathur ) , a human cervical adenocarcinoma cell line HeLa ( Obtained from American Type Culture Collection ( ATCC CCL-2 ) and murine L ( ATCC CRL-2648 ) cells expressing GFP or RFP were cultured in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal bovine serum ( Invitrogen ) . The wild-type Western Reserve ( WR ) strain of vaccinia virus , the A26L deletion virus ( WR-ΔA26 ) , and a revertant WR-Flag-A26 virus ( WR-A26 ) in which an N-terminal flag-tagged A26L ORF was reinserted back into the genome of WR-ΔA26 were all described previously [33] . Viruses were propagated in BSC40 cells and purified through a 36% sucrose cushion and a 25–40% sucrose gradient , followed by CsCl gradient centrifugation as previously described [56 , 57] . Anti-A27 [5] and anti-D8 [7] antibodies were described previously . Anti-flag monoclonal antibody was purchased from Sigma Inc . Two A26L N-terminal deletion ORFs , A26 ( aa 76–500 ) and A26 ( aa 321–500 ) , were generated by PCR using the WR-A26L ORF as template . PCR fragments containing N-terminal flag sequences were individually cloned into pMJ601 plasmid so that each flag-A26L deletion ORF was expressed from a synthetic late promoter and flanked by the left and right arm viral tk sequences . In addition , we performed in vitro mutagenesis ( QuickChange Lightning site-directed mutagenesis kit; Agilent Tech . Inc . ) on the pMJ601-flag-A26L ORF plasmid to generate His-to-Arg mutations at His48 and His53 ( A26-H2R ) , as well as three histidine residues at His48 , His53 and His92 ( A26-H3R ) . We also performed in vitro mutagenesis on the pMJ601-flag-A26L ORF plasmid to generate an A26 mutant protein ( A26-H2-CAT ) that contains K47D , R57D , R312D and H314R mutations to reduce cation-mediated repulsion at low pH via His48 and His53 . All mutant A26L plasmids were sequenced to confirm accuracy . To generate N-terminal A26 deletion viruses and the other A26 mutant viruses , CV-1 cells ( A kidney cell line of Cercopithecus aethiops purchased from ATCC CCL-70 ) were infected with WR-ΔA26 at an MOI of 1 PFU/cell , subsequently transfected with each plasmid and cultured for another 3 days . Lysates were subsequently harvested for recombinant virus isolation on BSC40 cells via three rounds of plaque purification in agar containing 150 μg/ml 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside ( X-Gal ) , as described previously [58] . CsCl purified vaccinia mature virions ( 1μg ) were loaded on SDS-PAGE gels for immunoblot analyses as previously described [32] . Alternatively , BSC40 or HeLa cells were infected with various vaccinia viruses at an MOI of 5 PFU per cell for 1 h at 37°C and harvested at 24 hpi . Cell lysates were separated on SDS-PAGE gels and transferred onto nitrocellulose membranes for immunoblot analysis with anti-flag , anti-A27 , and anti-D8 antibodies as previously described [32] . EM analyses of virus-infected cells were performed as previously described but with minor modifications [59] . In brief , BSC40 and HeLa cells were infected with each virus at an MOI of 5 PFU per cell and harvested at 24 hpi . After embedding and sectioning , samples in thin sections were stained with 1% uranyl acetate in H2O and Reynold’s lead citrate solution . The samples were subsequently examined under a Tecnai G2 Spirit TWIN electron microscope ( FEI Company , The Netherlands ) and photographed using a CCD camera ( 4*3 model 832 , Orius SC1000B ) . EM analyses of purified vaccinia MV were performed as previously described [8] . In brief , CsCl-purified vaccinia MV samples were serially diluted , and then half of the samples were stained with 1% Sodium phosphotungstate ( PTA ) and loaded onto 400-mesh , 10 nm Formvar and 1 nm carbon-coated grids to count MV particle numbers under a Tecnai G2 Spirit TWIN electron microscope ( FEI Company , The Netherlands ) . The other half of the serially-diluted MV samples was used to infect BSC40 cells for plaque assays to determine virus titers . Vaccinia MV infectivity is calculated as the Particle-to-PFU ratio = ( Particle number per ml ) / ( PFU per ml ) as previously described [8] . The infectivity assays for each virus were repeated three times and statistical analyses were performed using Student's t test in Prism ( version 5 ) software ( GraphPad ) . Statistical significance is represented as * , P value <0 . 05; ** <0 . 01; and *** <0 . 001 . Cell-cell fusion assays induced by vaccinia MV infections were performed as previously described [32] . In brief , L cells expressing EGFP or RFP were mixed at a 1:1 ratio and seeded in 96-well plates . The next day , cells were pretreated with 40 μg/ml cordycepin ( Sigma ) for 60 min and subsequently infected with each vaccinia virus at an MOI of 50 PFU per cell in triplicate . Cordycepin was present in the medium throughout the experiments . After infection at 37°C for 30 min , cells were treated with PBS at either pH 7 . 4 or pH 4 . 7 at 37°C for 3 min , washed with growth medium , further incubated at 37°C , and then photographed at 2 hpi using a Zeiss Axiovert fluorescence microscope . Five images for each virus were recorded and the % fusion was calculated using the image area of GFP+RFP+ double-fluorescent cells divided by that of single-fluorescent cells . “Acid Fusion Index” was calculated to represent the acid-dependence of each A26 deletion protein , i . e . , the occurrence of A26 protein conformational change . The index for each virus was obtained by dividing the percentage of cell fusion at low pH with that recorded for neutral pH . To quantify fusion activity of each virus as described above the fusion assays were repeated three times and statistical analyses were performed using Student's t test in Prism ( version 5 ) software ( GraphPad ) . Statistical significance is represented as * , P value <0 . 05 , ** <0 . 01 , and *** <0 . 001 . An NdeI-EcoRI DNA fragment containing a thioredoxin ( TRX ) -hexahistidine-Xa cutting site was synthesized and cloned into the bacterial expression vector pET21a , resulting in pET21a-TRX . Subsequently , an EcoRI-XhoI fragment containing the A26L ORF encoding aa 1–91 was synthesized and cloned into pET21a-TRX , resulting in pET21a-TRX-A26 ( 1–91 ) that expresses a bacterial fusion protein containing N-terminal TRX fused with A26 ( aa 1–91 ) ( Yao-Hong Biotechnology Inc . , Taiwan ) . Next , in vitro mutagenesis was performed using pET21a-TRX-A26 ( 1–91 ) plasmid as template to generate the pET-21a-TRX-A26 ( 1–91 ) H48 , 53R mutant plasmid . To generate a control TRX expression plasmid , we inserted a stop codon immediately before the A26 ( 1–91 ) sequence in pET-21a-TRX-A26 ( 1–91 ) to generate a control plasmid that only expresses TRX protein ( XL Site-Directed Mutagenesis Kit , Agilent Technologies , Santa Clara , CA ) . Each plasmid was transformed into BL21 ( DE3 ) and recombinant proteins were expressed via 0 . 2 mM isopropyl 1-thio-β-d-galactopyranoside ( IPTG ) induction for 4 h , before harvesting for protein purification by nickel column chromatography as suggested by the manufacturer . All of the recombinant A26 proteins used in our NMR analyses contained the TRX fusion tag . To render recombinant TRX fusion proteins suitable for heteronuclear NMR studies , bacterial cultures were incubated at 37°C in M9 medium supplemented with [15N]ammonium chloride ( 1 g/liter ) ( Sigma-Aldrich Co . , St . Louis , MO ) to an absorbance at 600 nm of 0 . 8 , induced for 4 h at 37°C with 0 . 2 mM IPTG , and harvested for protein purification through nickel-nitrilotriacetic acid affinity column chromatography . The bound recombinant TRX-fusion proteins were eluted with 0 . 3 M imidazole and dialyzed overnight against 0 . 1 M MES buffer pH 6 . 0 at 4°C before use . For NMR measurement , all 1H-15N HSQC spectra were recorded at pH 6 . 0 or 8 . 0 and at 25°C on a Bruker Avance 600 MHz spectrometer equipped with a 5 mm QXI ( 1H/13C/15N ) z-axis gradient probe . For 15N-labeled proteins ( 0 . 8–1 . 0 mM ) , Shigemi NMR tubes ( 5 mm outer diameter ) were used . The pH values were measured at 25°C with a Suntex TS-100 pH meter . Proteins started to aggregate under acidic conditions ( pH<6 . 0 ) . All spectra were processed using Topspin version 3 . 2 ( Bruker , Karlsruhe , Germany ) . For CD measurements , the TRX tag was removed from the fusion A26 proteins using ~2 μg Factor Xa ( Sigma Aldrich ) in 20 mM Tris·HCl , pH 6 . 5 with 50 mM NaCl and 1 mM CaCl2 at 4°C . After cleaving , purification was carried out using Ni-NTA resin to separate the cleaved His-TRX tag from the A26 protein samples . The CD spectra were recorded on a Jasco J-815 spectrometer equipped with a water bath for temperature control . All CD spectra were collected at 25°C using a quartz cuvette with a 1 mm path length and a protein concentration of 15 . 4 μM . The step size was 0 . 2 nm with a 1 . 0 nm bandwidth at a scan speed of 50 nm/min . Each spectrum represents the average of three measurements . All spectra were collected in 20 mM potassium phosphate buffer with background buffer correction . Four A26L DNA fragments coding for ( 1 ) full-length A26 protein , ( 2 ) residues 1–420 ( A26 ( 1–420 ) ) , ( 3 ) residues 1–420 with Cys43/Cys342 mutations ( A26 ( 1–420 ) C43C342A ) , and ( 4 ) residues 1–110 ( A261-110 ) were each ligated into pET-16 vector ( Novagen ) . Each construct was transformed into Escherichia coli BL21 ( DE3 ) . After induction with 1 mM IPTG , each recombinant protein was expressed at 16°C for 16 hours . Each soluble A26 protein was purified by immobilized metal-ion chromatography with a Ni-NTA column ( GE Healthcare ) . Since we failed to crystallize A26 ( 1–420 ) and A26 ( 1–420 ) C43C342A , we used a limited trypsin digestion assay to determine the core structure of A26 protein . Briefly , 500 μg purified A26 ( 1–420 ) C43C342A was incubated in 30 μl reaction buffer ( 30 mM Tris pH 8 . 0 , 100 mM NaCl , 1 mM dithiothreitol and 5% glycerol ) either alone or in the presence of trypsin at a 1:500 ratio ( w/w; trypsin:protein ) . Digestion was carried out at 4°C for 16 hours . The reaction products were analyzed by 12% Bis-tris SDS-PAGE and stained with coomassie blue . The bands containing A26 fragments were excised from the SDS PAGE gel and then subjected to in-gel digestion with trypsin . The digested peptide mixtures were then subjected to a NanoLC−nanoESI-MS/MS analysis . MS data were analyzed using the MASCOT server ( http://www . matrixscience . com/search_form_select . html ) . Based on the above-described digestion analysis , we decided to use A26 ( 1–397 ) for further crystallization experiments using the ( smt3/Ulp ) system provided by Dr . C . D . Lima for recombinant protein expression and purification as previously described [60] . The pET-His10-SUMO-A261-397 DNA construct ( codon-optimized in bacteria ) was transformed into the E . coli BL21 ( DE3 ) strain ( Novagen ) , and the cells were cultured in LB broth containing 50·μg/ml Kanamycin until the optical density at 600 nm ( OD600 ) reached 0 . 6–0 . 8 at 37°C . A final concentration of 0 . 1 mM isopropyl-®-thiogalactopyranoside ( IPTG ) was added to induce expression and cultured overnight at 17°C for 20 h until the OD600 reached 1 . 20 . Bacterial pellets were harvested by centrifugation at 6 , 000×g for 30 min at 4°C and then disrupted by sonication in lysis buffer [20 mM Tris pH 8 . 0 , 20 mM imidazole , 0 . 5 M NaCl , 10% ( w/v ) glycerol , 1 mM PMSF , 1 mg/ml lysozyme , 0 . 1 mg/ml DNase I , 1 mM benzamidine ( Novagen ) , and EDTA-free protease inhibitor cocktail ( Roche ) ] . SUMO-A26 ( 1–397 ) protein was loaded onto a Ni-NTA affinity chromatography column ( GE Healthcare ) , washed first with 40 volumes of binding buffer 1 [20 mM Tris pH 8 . 0 , 20 mM imidazole , 0 . 5 M NaCl , 10% ( w/v ) glycerol] , then with 40 volumes of binding buffer 2 [20 mM Tris pH 8 . 0 , 100 mM imidazole , 0 . 5 M NaCl , 10% ( w/v ) glycerol] , before elution with a linear gradient of up to 100% ( v/v ) elution buffer [20 mM Tris pH 8 . 0 , 0 . 5 M imidazole , 0 . 5 M NaCl , 10% ( w/v ) glycerol] . The eluted SUMO-A26 ( 1–397 ) protein was dialyzed three times against 7 . 5 liters of buffer [20 mM Tris pH 8 . 0 , 0 . 3 M NaCl , 10% ( w/v ) glycerol] and then subjected to Ubiquitin-like-specific protease 1 ( Ulp1 ) treatment to remove the histidine-tagged SUMO fusion protein . The histidine-tagged SUMO fusion protein was cleaved using Ulp1 at a ratio of 1:500 ( w/w; Ulp1:protein ) that was later removed with a Ni-NTA affinity chromatography column ( GE Healthcare ) . The untagged A26 ( 1–397 ) proteins were purified using another HiPrep Q FF 16/10 column ( GE Healthcare ) . These untagged A26 ( 1–397 ) proteins were purified through a HiPrep Q FF 16/10 column ( GE Healthcare ) , the column was washed with 10 volumes of Q binding buffer [20 mM Tris pH 8 . 0 , 1 mM DTT , 50 mM NaCl] , and eluted with a linear gradient of up to 100% ( v/v ) elution buffer [20 mM Tris pH 8 . 0 , 1 mM DTT , 0 . 5 M NaCl] . The A26 ( 1–397 ) proteins were stored in a buffer containing 20 mM Tris pH 8 . 0 , 1 mM DTT , and 100 mM NaCl at 4°C . We used a 12% SDS PAGE gel to confirm the purity of A26 ( 1–397 ) proteins as being above 99% . Selenomethionine-labeled A26 ( 1–397 ) protein ( SeMet A26 ( 1–397 ) ) was labeled using a SelenoMethionine medium complete kit ( Molecular Dimensions ) and purified according to the same procedures . Since A26 shows no sequence homology to any reported protein structure , we produced SeMet A26 ( 1–397 ) for X-ray analysis . SeMet A26 ( 1–397 ) was crystallized by the sitting drop method , in which 2 μl of the purified protein ( 15 mg/ml ) was mixed with 2 μl of a reservoir containing 0 . 2 M sodium acetate trihydrate , 0 . 1 M Tris pH 8 . 5 , 30% w/v PEG 4000 , and equilibrated with 200 μl of the reservoir at 25°C . For X-ray data collection , 15% ethylene glycol was used as a cryoprotectant . A single-wavelength anomalous dispersion ( SAD ) X-ray diffraction dataset was collected from Taiwan Photon Source ( TPS ) beamline 05A at the National Synchrotron Radiation Research Center ( NSRRC ) in Hsinchu , Taiwan . The X-ray data were processed by using HKL2000 [61] . The space group of the SeMet A26 ( 1–397 ) crystal is P21 , with unit cell dimensions of a = 45 . 38 Å , b = 80 . 72 Å , c = 53 . 81 Å and β = 113 . 8° ( Table 2 ) . The initial electron density map of SeMet A26 ( 1–397 ) was calculated by using the peak dataset collected at wavelength 0 . 97907 Å and the program Shelix CDE [62] . The program BUCCANEER [63] was then used to produce the initial model . Only one SeMet A26 ( 1–397 ) monomer was found in each asymmetric unit . We used the programs COOT [64] and Refmac [65] for model refinement . Finally , residues 17–364 of SeMet A26 ( 1–397 ) were successfully built . In addition , resdiues 382–385 of SeMet A26 ( 1–397 ) ( Thr-Pro-Ile-Pro ) were also built as a separate fragment . Data collection and refinement statistics are shown in Table 2 . The completeness of 95 . 2% is actually for the outermost shell and this is now clarified in Table 2 . We also analyzed our SeMET A26 structure by MolProbity ( http://molprobity . biochem . duke . edu/ ) [38] . The resulting MolProbity score and Clashscore are 1 . 10 and 2 . 68 , confirming the good quality of this structure . We used the CCP4 package [66] , Chimera program [67] , Areaimol [68] , PROPKA3 [69] and Discovery studio [42] for structural analyses and to generate figures . The corresponding positions of the regions of constructs used in this study are highlighted in S8 Fig . Discovery studio was also used to build a A261-397 model at pH4 . 7 [A261-397 model ( pH4 . 7 ) ] . Then , the A26 1-397model ( pH4 . 7 ) was subjected to molecular dynamic simulations following the Standard Dynamics Cascade protocol . The dynamic simulations were performed for a production time of 1 , 000 ps using default parameters . The final conformation of A26 1–397 model ( pH4 . 7 ) was then used in subsequent analysis ( e . g . calculate Surface electrostatic potential and solvent accessibility , etc ) . WR-A26-H2R , WR-A26-H3R and WR-A26-H2-CAT mutant viruses formed tiny plaques of HeLa cells . Revertant ( Rev ) viruses displaying a large plaque phenotype were spontaneously derived at an initial rate of ~0 . 1% during early expansion of WR-A26-H2R , WR-A26-H3R and WR-A26-H2-CAT viruses . We independently isolated three revertant viruses ( Rev1-3 ) from WR-A26-H2R , four revertant viruses ( Rev1-4 ) from WR-A26-H3R , and four revertant viruses ( Rev1-4 ) from WR-A26-H2-CAT , respectively . All of the revertant viruses were subsequently purified to 100% purity . Viral genomic DNA was purified from all the revertant viruses but only WR-A26-H2R-Rev1 , WR-A26-H3R-Rev1 and WR-A26-H2-CAT-Rev1 viruses were sent for whole genome sequencing . Viral genomic DNA of all other revertant viruses were used in PCR amplification and sequencing to determine the location of the second-site mutations in A26L ORF . Vaccinia viral genomic DNA was extracted as previously described [70] . All genomic DNA was quantified by Qubit ds DNA BR assays using a Qubit 3 . 0 fluorometer ( Life Technologies , Carlsbad , US-CA ) . Genomic DNA ( 2 μg ) was sheared to an average length of 550 bp , end-repaired and A-tailed , and then ligated with indexed adapters for PCR-free library preparation [71] based on the manufacturer’s protocols ( Illumina TruSeq DNA PCR-Free Library Preparation kit protocol 15036187 Rev . B ) ( Illumina Inc . , San Diego , CA , USA ) . The quality and size distribution of the genomic libraries was verified using an Agilent DNA High Sensitivity kit ( 5067–4626 ) and Agilent 2100 Bioanalyzer . Viral genomic sequencing was performed using an Illumina Miseq 2x300 cycle or NextSeq500 2x150 cycle paired-end run at the Genomics Core facility of the Institute of Molecular Biology , Academia Sinica . The sequence data were performed using CLC Genomics Workbench 11 . 0 . 1 ( Qiagen , Aarhus , Denmark ) for raw sequencing trimming , sequence mapping , and variant detection . Raw sequencing reads were trimmed by removing adapter sequences , low-quality sequences ( Phred quality score of less than Q20 ) and sequencing fragments of shorter than 30 nucleotides . Sequencing reads were mapped to the human genome ( GRCh38 , from ftp . ensembl . org/pub/release-82/fasta/homo_sapiens/dna/ ) with the following parameters: mismatches cost = 2 , insertion cost = 3 , deletion cost = 3 , minimum fraction length = 0 . 8 , minimum fraction similarity = 0 . 8 . All host genome sequences that met the above parameters were removed , as were duplicate reads , before mapping the remaining paired-end reads to the vaccinia virus WR genome ( GenBank NC_006998 ) [72] with much more stringent parameters ( mismatches cost = 2 , insertion cost = 3 , deletion cost = 3 , minimum fraction length = 0 . 9 , minimum fraction similarity = 0 . 9 ) . We used the Basic Variant Detection tool in CLC Genomics Workbench 11 . 0 . 1 to call single nucleotide polymorphisms ( SNPs ) and insertions/deletions ( InDels ) with customized parameters to identify mutation positions: ( 1 ) minimum frequency of 10% and minimum coverage 10 reads; and ( 2 ) minimum quality of SNPs/InDels should be larger than Q25 and the neighborhood quality ( upstream/downstream five bases ) should be larger than Q20 . We also used the paired-end reads after removing host genome sequences to generate mutant and revertant viral genomes by de novo genome assembly but excluding the terminal repeat sequences of vaccinia virus . Using the alignment program MAFFT version 7[73 , 74] , we aligned all viral genome sequences with the reference WR strain ( GenBank NC_006998 ) to identify the second-site mutations in revertant viruses . The multiple alignments of viral genomic sequences of WR-A26 , WR-A26-H2R-Rev1 , WR-A26-H3R-Rev1 and WR-H2-CAT-Rev1 are included in Supplemental S1 Appendix . Coordinates and structure factors of A26 have been deposited in the Protein Data Bank with 6A9S accession number ( PDB ID: 6A9S ) .
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Vaccinia virus is a complex large DNA virus with a large number of viral membrane proteins to facilitate cell entry . Although it is well established that vaccinia mature virus uses endocytosis to enter cells , it remains unclear how it triggers membrane fusion in the acidic environment of endosomes . Recently , we hypothesized that A26 protein in vaccinia mature virus functions as an acid-sensitive membrane fusion suppressor , which suggests a novel viral regulation not present in other enveloped viruses . We postulated that conformational changes of A26 protein at low pH result in de-repression of viral fusion complex activity to trigger viral and endosomal membrane fusion . Here , we provide structural , biochemical and biological evidence demonstrating that vaccinia A26 protein does indeed function as an acid-sensitive fusion suppressor protein to regulate vaccinia mature virus membrane fusion during endocytosis . Our data reveal an important and unique “checkpoint” for vaccinia mature virus endocytosis that has not been described for other viruses . Furthermore , by isolating adaptive vaccinia mutants that escaped endocytic blockage , we discovered that mutations within the A26L gene serve as an effective strategy for switching the viral infection route from endocytosis to plasma membrane fusion , expanding viral host range .
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2019
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Vaccinia viral A26 protein is a fusion suppressor of mature virus and triggers membrane fusion through conformational change at low pH
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Morphological changes are critical for host colonisation in plant pathogenic fungi . These changes occur at specific stages of their pathogenic cycle in response to environmental signals and are mediated by transcription factors , which act as master regulators . Histone deacetylases ( HDACs ) play crucial roles in regulating gene expression , for example by locally modulating the accessibility of chromatin to transcriptional regulators . It has been reported that HDACs play important roles in the virulence of plant fungi . However , the specific environment-sensing pathways that control fungal virulence via HDACs remain poorly characterised . Here we address this question using the maize pathogen Ustilago maydis . We find that the HDAC Hos2 is required for the dimorphic switch and pathogenic development in U . maydis . The deletion of hos2 abolishes the cAMP-dependent expression of mating type genes . Moreover , ChIP experiments detect Hos2 binding to the gene bodies of mating-type genes , which increases in proportion to their expression level following cAMP addition . These observations suggest that Hos2 acts as a downstream component of the cAMP-PKA pathway to control the expression of mating-type genes . Interestingly , we found that Clr3 , another HDAC present in U . maydis , also contributes to the cAMP-dependent regulation of mating-type gene expression , demonstrating that Hos2 is not the only HDAC involved in this control system . Overall , our results provide new insights into the role of HDACs in fungal phytopathogenesis .
The switch between yeast and hypha stages , or dimorphism , is a key morphological conversion required for the virulence of several animal and plant pathogenic fungi [1–4] . This process occurs at specific stages of fungal infection and is tightly controlled . The two best studied signalling pathways regulating gene expression during dimorphism are the mitogen activated protein ( MAP ) kinase cascade and the cyclic-AMP protein kinase A ( cAMP-PKA ) pathway . Their activation is typically controlled by specific environmental stimuli and results in the induction of master regulatory genes [5–8] . Ustilago maydis is a well-established model organism for the study of the yeast-hypha dimorphic switch , a key stage of its pathogenic cycle [6 , 8 , 9] . The U . maydis pathogenic cycle starts when two mating compatible haploid yeast cells recognise each other via a pheromone-receptor system . Mating leads to the formation of a dikaryon filament , whose apical tip differentiates into a specialised structure for plant penetration known as the appressorium [10–12] . Once inside the plant , U . maydis proliferates , inducing the formation of tumours and eventually develops into diploid spores [13] . Thus , the transition from the yeast form to the infective filamentous one is crucial for U . maydis pathogenicity . The control of this process relies on a tetrapolar system consisting of the biallelic a and multiallelic b loci . The a locus encodes components of the pheromone-receptor system , allowing cells from opposite mating types to recognise each other , form conjugation tubes and fuse [14 , 15] . The fate of the resulting dikaryon is then controlled by the b locus , which encodes two transcription factors , bE and bW . When different mating type-specific alleles of bE and bW are expressed , they form a compatible heterodimer that activates the filamentation and virulence programs [16 , 17] . MAP kinase and cAMP-PKA pathways are necessary for sensing pheromone and environmental signals . Both pathways lead to the transcriptional and post-translational activation of the transcription factor Prf1 ( Pheromone-responsive factor 1 ) . Once activated , Prf1 binds to the promoters of mating-type gene loci and activates their expression ( S1 Fig ) [18–23] . Prf1 is a central regulatory factor during mating and virulence , and is tightly controlled at the transcriptional level [19 , 20 , 22 , 24–27] . Chromatin structure and modifications are central to gene regulation , however , little is known about their contribution to the control of prf1 and of Prf1-dependent gene expression . Histone deacetylases ( HDACs ) are important regulators of gene expression and are grouped into different classes . Class I ( Saccharomyces cerevisiae RPD3-like ) and class II ( HDA1-like ) HDACs are conserved from yeast to humans . Typically , nuclear histone deacetylases inhibit transcription through the deacetylation of promoter-bound histones . However , several groups have reported HDACs with both activating and repressing functions [28–31] . For example , the S . cerevisiae Hos2/Set3 HDAC complex represses specific meiotic genes while activating others [28 , 29] . HDACs have been implicated in many physiological processes , with their roles varying between different biological contexts . This is exemplified by the HDACs of plant and animal pathogenic fungi . There are important differences in the strategy employed by a fungus to colonise an animal or plant , and this seems to be reflected in the virulence-related process controlled by HDACs in each case . For example , Hos2 regulates the dimorphic switch of Candida albicans during animal infections [32] , whereas it controls plant tissue penetration and post-penetration stages of plant pathogens [33 , 34] . Finally , very little is known about the downstream HDAC gene targets required for plant pathogen virulence , or how HDAC activity integrates with the upstream signalling pathways that control pathogenic development . Here , we report a comprehensive characterisation of class I and II HDAC homologues in the plant pathogenic fungus U . maydis . Our results demonstrate that Hos2 is important for the yeast-to-hypha transition and fungal virulence . Analysis of deletion mutants and ChIP experiments strongly suggest that Hos2 directly regulates the expression of U . maydis mating-type genes downstream of the cAMP-PKA pathway . Lastly , we present evidence indicating that another HDAC , Clr3 , functionally interacts with Hos2 in the regulation of mating-type genes through the cAMP-PKA pathway .
A BLAST search and phylogenetic analysis revealed that Ustilago maydis genome harbours six putative class I and II HDACs , a putative orthologue of each HDAC found in Saccharomyces cerevisiae , Candida albicans , and Schizosaccharomyces pombe , except for the Rpd3/Clr6 HDAC , for which we found two distinct homologues in U . maydis ( Fig 1A ) . Two of the six U . maydis HDACs were already named: Hda1 for Um02065 [35] and Hda2 for Um11308 [36] . The other four proteins were named according to their closest relative in S . cerevisiae or S . pombe . Thus , we named Um04234 Hos1 , Um11828 Hos2 ( annotated in the MIPS Ustilago maydis database , MUMDB ) , Um10914 Hos3 and Um02102 Clr3 . All four proteins possess the domains characteristic of their type as shown in S2 Fig . To test the requirement of each HDAC for U . maydis virulence , we generated deletion mutants for class I and II HDACs in FB1 and FB2 mating-compatible strains . We then infected seven day-old maize seedlings with all the HDAC mutant and wild-type FB1 and FB2 strains . Symptoms were scored 14 days post infection ( dpi ) . As shown in Fig 1B , most HDAC mutants showed wild-type pathogenic infection rates . However , Δhos2 mutants showed a strong reduction in pathogenesis . The absence of Hos2 results in an approximate 3-fold increase in the number of tumour-free plants , a 6-fold decrease in plant death and a decrease in the size of the developed tumours ( Fig 1B and 1C ) , indicating that Hos2 is required for full U . maydis virulence . During this analysis , we also noticed a modest decrease in virulence when clr3 was deleted , especially regarding the number of dead plants ( Fig 1B ) . Since U . maydis Hda1 is essential for teliospore development [35] , we investigated whether other HDAC mutants were required for spore formation in this fungus . To this end , we analysed spore production inside maize tumours 21 days post infection . This analysis revealed that , besides Hda1 , no other HDAC is required for spore formation ( Fig 1D ) . To conclude , Hos2 and Hda1 control important yet distinct stages of the pathogenic cycle in U . maydis . To confirm our sequence alignment analysis ( Fig 1A ) , we sought to experimentally confirm that U . maydis Hos2 is indeed an HDAC . With this aim , we compared H4K16 acetylation , the canonical histone target of Hos2 [29 , 37] , in U . maydis Δhos2 mutant relative to the wild-type control . As can be observed in S3A Fig , the absence of Hos2 increased H4K16 acetylation levels confirming its HDAC activity . Additionally , we observed that a Δhos2 mutant shows a hypersensitivity response to Trichostatin A ( TSA ) , as previously described in S . pombe [38 , 39] , ( S3B Fig ) . These results confirm that we have identified the functional U . maydis Hos2 HDAC . To identify the cause of the reduced virulence shown by Δhos2 cells , we investigated the role of Hos2 at different stages of the U . maydis life cycle , comparing its behaviour to the other HDAC mutants . Δhda1 cells have previously been shown to have no phenotype except during spore formation , and were therefore not analysed further [35] . Light microscopy analysis of cells grown in liquid culture revealed no major anomalies , although we noticed that in an FB1 background 14% of Δhos2 and 8 . 3% of Δclr3 mutant cells were multi-budded , without significantly affecting their doubling time or cell shape during exponential growth ( Figs 2A , 2B and S4A–S4C ) . Similar results were observed in an FB2 background ( S4A , S4D and S4E Fig ) . Mating between two compatible haploid partners is the first step of the U . maydis pathogenic cycle . To check whether Δhos2 mutants are able to mate normally , we used charcoal-containing plates , which mimic the plant leaf surface and induce the mating process . Mating between compatible partners and post-fusion filamentation can be visualised by the appearance of white and fuzzy colonies on charcoal plates . As shown in Fig 2C , mating between FB1Δhos2 and FB2Δhos2 mutants resulted in reduced white colony fuzziness relative to a cross of wild-type cells . Comparing rich ( PD ) and complete ( CM ) charcoal plates , we observed that the degree of the mating defect of Δhos2 mutant might be dependent on nutritional conditions . This result will be further discussed in the manuscript ( see Discussion ) . The other HDAC mutants mated normally ( S5 Fig and [35] ) . To determine whether Hos2 contributes to cell fusion , we also analysed the filamentation of wild-type and Δhos2 mutants in the solopathogenic strain SG200 . This strain contains genes encoding a compatible bE1/bW2 heterodimer , as well as a gene constitutively expressing the opposite mating type pheromone ( mfa2 ) [40] . This allows haploid SG200 cells to form filaments and infect maize plants without needing to mate with a compatible partner . We found that the deletion of hos2 in this background severely impaired the formation of white , fuzzy colonies ( Fig 2D ) , indicating that Hos2 has a post-fusion mating role . We then verified the virulence capacity of the SG200Δhos2 mutant and observed that Hos2 is also required for full pathogenicity in this genetic background ( Fig 2E and 2F ) . Significantly , the virulence defects shown by SG200Δhos2 were comparable to those seen in FB1Δhos2 FB2Δhos2 mutant crosses , indicating that the post-fusion role of Hos2 is probably responsible for the reduced pathogenicity of Δhos2 mutant cells . In U . maydis , post-fusion filament formation is controlled by b mating-type genes , which are necessary and sufficient to induce filamentation and pathogenicity [40] . Upon formation of the bE/bW heterodimer , several transcription factors are activated , some of which are known to be essential for filament formation and virulence [41–43] . To check for a genetic interaction between hos2 and b genes , we took advantage of the AB31 strain , which contains a compatible b heterodimer under the control of the arabinose inducible promoter , crg1 [41] . This strain can either divide by budding , when grown on complete media containing glucose as the sole carbon source , or switch to filamentous growth on complete media containing arabinose . Deletion of hos2 in the AB31 background did not affect filamentation , as compared to a wild-type control ( Fig 3A and 3B ) . We verified that b gene expression was comparable between Δhos2 and the wild-type strains ( Fig 3C ) . Similar results were obtained when hos2 was deleted in a HA103 genetic background ( Fig 3D ) , which allows the constitutive expression of a compatible b heterodimer [18] . Thus , the constitutive expression of b genes restores the reduced filamentation observed in the SG200Δhos2 strain . These results suggest that Hos2 controls post-fusion filamentation upstream of the b locus . We hypothesised that either Hos2 is required for b gene expression , or that it controls filament formation via an independent pathway that can be compensated for by the induction of b genes . To check the expression level of b genes in Δhos2 mutants , we grew cells on complete charcoal-containing medium , and analysed b gene expression levels by RT-qPCR . As shown in Fig 3E , bE1 mRNA levels were reduced in Δhos2 mutants , suggesting that Hos2 is required for the expression of b genes . We have shown that Hos2 plays an important role in post-fusion filamentation upstream of b genes . Considering that the direct regulator of b genes , Prf1 , is also responsible for the expression of the pheromone and receptor genes , we wondered whether Hos2 could also be affecting pre-fusion events during mating . To do this , we performed a pheromone stimulation assay by exposing wild-type and Δhos2 cells to synthetic a2 pheromone , then checking conjugation tube formation 5 hours post-pheromone addition . As shown in Fig 4A and 4B , the hos2 mutant showed a 50% reduction in conjugation tube formation upon pheromone stimulation . This result indicates that Hos2 has a dual function in U . maydis dimorphism by controlling pre- and post-fusion mating events . Accordingly , the expression of the gene encoding the Mfa1 pheromone , mfa1 , was reduced in hos2 mutants compared to wild-type cells ( Fig 4C ) . Interestingly , addition of the HDAC inhibitor Trichostatin A ( TSA ) , phenocopied the Δhos2 mutant conjugation tube formation defect . Incubation of wild-type FB1 cells with TSA prior to pheromone addition led to a decrease in the number of cells able to develop conjugation tubes ( S6A Fig ) , without affecting cell viability ( S6B Fig ) . These results suggest that the chemical inhibition of Hos2 activity recapitulates the mating phenotypes observed in Δhos2 strains . Furthermore , overexpression of hos2 under the otef promoter at the ip locus , restored the filamentation capacity of SG200Δhos2 ( S7A and S7B Fig ) . We verified that overexpression of hos2 did not cause any mating , filamentation or virulence phenotype , by integrating the same construct in the FB1 or SG200 wild-type strains ( S7B–S7D Fig ) . Altogether , these results suggest that the observed phenotypes are a consequence of the loss of hos2 . The role of Hos2 in pre- and post-fusion mating events , as well as in the expression of a and b genes , prompted us to examine whether Hos2 controls these processes in response to the pheromone responsive MAP kinase cascade . To test this possibility , we used the FB1Pcrg1fuz7DD strain which contains a constitutively active Fuz7 MAPK kinase allele under the control of the crg1 promoter [22] . Expression of the constitutively active fuz7DD allele , by switching from glucose to arabinose containing media , induced the expression of both prf1 , the a and b genes , and conjugation tube formation through an unknown pathway ( Fig 5A; see also S1 Fig and [8] for details ) . As shown in Fig 5B and 5C , expression of the fuz7DD allele in a Δhos2 background promotes conjugation tube formation to a similar level to that observed in the wild-type control strain , indicating that Hos2 does not control conjugation tube formation downstream of the Fuz7 MAPK kinase . Moreover , the arabinose-dependent induction of prf1 , mfa1 , pra1 and bE1 was comparable between Δhos2 mutants and wild-type controls ( Fig 5D–5G ) . fuz7DD-induced conjugation tube formation and mating type gene expression both require MAPK Kpp2 activity ( Fig 5A and [22] ) ; thus , our results strongly suggest that Hos2 does not function downstream of the MAPK cascade in the control of pre- and post-fusion events during mating . If Hos2 is not downstream of the MAPK cascade , it could act in either an upstream or a parallel pathway . To address this question we turned to our previous work , which showed that Tup1 controls dimorphism and virulence in U . maydis [27] , with many similarities to what we describe here for Hos2 . Both proteins are involved in virulence by controlling pre- and post-fusion events during mating and , interestingly , Tup1 is known to control gene expression through interaction with histone deacetylases [44 , 45] . However , unlike Hos2 , Tup1 is required for fuz7DD-induced prf1 and a and b gene expression ( Fig 5 , and [27] ) . Therefore , it is unlikely that Tup1 functionally interacts with Hos2 to control dimorphism and virulence . To confirm this , we decided to examine how Tup1 and Hos2 interact genetically . We constructed a double mutant SG200Δtup1Δhos2 strain and analysed its virulence in maize plants . If Hos2 acts exclusively upstream of the MAPK pathway , we would expect an epistatic relationship with Tup1 . As expected , double Δtup1Δhos2 mutants were unable to form filaments on charcoal-containing plates , as is the case for either single mutant ( Fig 6A ) . Interestingly , pathogenicity was severely reduced in Δtup1Δhos2 mutants , compared to the wild-type or either of the single mutant strains ( Fig 6B and 6C ) . These results indicate that both genes have independent regulatory roles during pathogenic development rather than acting together in the control of virulence genes . This interpretation is consistent with a putative role for Hos2 in the control of the dimorphic switch and virulence that is independent of the MAPK cascade . In addition to the MAPK cascade , U . maydis dimorphism and mating-type gene expression are also controlled by the cAMP-PKA pathway ( S1 Fig ) . Addition of exogenous cAMP to wild-type U . maydis cell cultures induces the expression of mating-type genes and cAMP pathway mutants affect this induction [19 , 21 , 23 , 46–49] . Therefore , we looked at whether Hos2 might control the expression of mating-type genes in response to activation of the cAMP pathway . To do this , we measured the expression of prf1 , mfa1 and pra1 in FB1Δhos2 cells growing in rich liquid medium ( PD broth ) with or without the addition of exogenous cAMP . As shown in Fig 7 , the addition of cAMP was unable to induce the expression of mating- type genes in Δhos2 mutants . These results strengthen the hypothesis that Hos2 functions independently of the MAP kinase pathway to control mating-type gene induction . Additionally , even in the absence of cAMP , the expression of mating-type genes was reduced in Δhos2 mutants , suggesting that Hos2 is important for maintaining basal mating-type gene expression levels under these conditions . In order to determine whether the effect of Hos2 on mating-type gene expression is direct , we performed chromatin immunoprecipitation ( ChIP ) assays followed by qPCR . For this purpose we constructed the strain FB1Hos2-HA3 , in which the endogenous Hos2 protein is tagged with three copies of the HA epitope . This strain was able to form conjugation tubes upon pheromone stimulation in a comparable way to the wild-type control ( S8 Fig ) , indicating that the Hos2-HA3 allele is functional . In S . cerevisiae and C . albicans Hos2 is recruited to gene bodies in a transcription dependent manner [29 , 50] . Thus , we decided to check whether U . maydis Hos2 binds to the gene bodies of mating-type genes . As shown in Fig 8A , there was a significant enrichment of Hos2-HA3 within the prf1 , mfa1 and pra1 open reading frames ( ORFs ) , as compared to the HA ChIP signal obtained in an untagged strain . As a negative control , we analysed the enrichment of Hos2 at the appressorium specific um01779 gene , which is expressed only upon induction of appressoria formation [12] . At this gene , Hos2-HA3 binding was not significant compared to the untagged control . A similar result was obtained for Hos2-HA3 associated with the bE1 ORF . In conclusion , our ChIP experiments reveal that Hos2 directly binds to prf1 , mfa1 and pra1 mating-type gene sequences , where it most likely acts to control their expression . ChIP-seq experiments in S . cerevisiae and C . albicans , clearly show that Hos2 occupancy positively correlates with gene expression levels [29 , 50] . Therefore , we analysed how Hos2 binding to mating-type genes changes upon cAMP addition , which induces their expression ( Fig 7 ) . We found that the addition of cAMP caused a significant increase in Hos2 binding to mfa1 and pra1 ORFs ( Fig 8B ) . Increased Hos2 binding to mfa1 and pra1 correlated well with their increased expression upon cAMP induction ( Fig 7 ) . Hos2 prf1 binding was unaffected by cAMP addition ( Fig 8B ) , consistent with its modest induction ( Fig 7 ) . As a control , we then tested the cAMP-induced recruitment of Hos2 to three different loci . Hos2 binding remained not significant at the bE1 gene , whose expression does not respond to cAMP addition [21] . A similar result was observed at um01779 , whose expression is also cAMP independent [12] . Finally , as a positive control , we detected significant enrichment of Hos2 at the strongly constitutively-expressed ppi gene , irrespective of cAMP addition . Thus , we conclude that Hos2 directly regulates the expression of mating-type genes in U . maydis in response to cAMP signalling . This is likely to account for the expression and mating defects observed in hos2 mutants . HDACs have redundant roles in several organisms [51–53] . As described in Fig 1A , U . maydis has two putative S . cerevisiae Rpd3 orthologues . Examination of the double Δhda1Δhda2 mutant phenotypes in a SG200 background , revealed that neither maize plant virulence nor filament formation on charcoal plates were significantly affected ( Fig 9 ) . This indicates that the two putative U . maydis Rpd3 orthologues do not act redundantly in these processes . In S . cerevisiae , ScRpd3 and ScHos2 act redundantly to control FLO11 , a gene involved in budding yeast dimorphism [53] . Therefore , we tested whether a similar genetic interaction might control pathogenicity in U . maydis . We constructed all possible double and triple mutant combinations in a SG200 genetic background and examined their virulence and filamentation phenotypes . All combinations showed phenotypes comparable to those observed in single Δhos2 mutants ( Fig 9 ) . These observations confirm that Hda1 and Hda2 do not control corn smut fungus pathogenic development . Furthermore , our data suggest that there are no significant functional redundancies between the ScRpd3 homologs , Hda1 and Hda2 , and Hos2 in the control of dimorphism and virulence in U . maydis . In the human pathogen C . albicans , CaHos2 and CaHda1 have been shown to play opposing roles in the control of morphological changes [54] . Thus , we wondered whether an analogous genetic interaction might occur during the U . maydis yeast to hypha transition . To test this , we deleted the U . maydis clr3 gene ( Cahda1 ) in the SG200 background , and examined its filamentation capacity alone or in combination with Δhos2 . As shown in Fig 10A , clr3 deletion did not rescue the filamentation defects of Δhos2 mutants , suggesting that Clr3 does not antagonise Hos2 control of the U . maydis dimorphic switch . As with FB1Δclr3 FB2Δclr3 mutant crosses ( Fig 1B ) , we detected a decrease in SG200Δclr3 mutant virulence capacity ( Fig 10B and 10C ) . Furthermore , we observed an even stronger virulence reduction in the SG200Δhos2Δclr3 double mutants , versus either single mutant . Altogether , these results show that Clr3 contributes to U . maydis pathogenicity , particularly when Hos2 is absent , and indicate that Hos2 and Clr3 play either redundant or independent functions during fungal virulence . To further characterise this genetic interaction , we measured the expression levels of b mating-type genes in Δclr3 single and Δhos2Δclr3 double mutants during filamentation on charcoal-containing medium . As shown in Fig 10D , Δclr3 mutants showed a mild reduction in b gene expression , but retaining sufficient expression to promote filamentation ( Fig 10A ) . Interestingly , Δhos2Δclr3 double mutant showed lower b gene expression than either Δclr3 or Δhos2 single mutants . We also measured the expression of a mating-type genes and prf1 in response to addition of exogenous cAMP . Similarly to what we found for bE1 gene , we observed a mild reduction of mfa1 expression in clr3 mutants upon cAMP addition ( Fig 10E–10G ) . However , we did not observe a synergistic reduction in the expression of this gene in Δhos2Δclr3 double mutant . Overall , these observations indicate that Hos2 and Clr3 participate in the control of mating-type gene expression through the cAMP pathway . Our results also suggest some redundancy between Hos2 and Clr3 , at least in the regulation of b gene expression .
Morphological changes are critical for pathogenic fungi to be able to infect their hosts and , consequently , are tightly controlled . Here , we report a comprehensive characterisation of the roles of HDACs over the life cycle of the plant pathogenic fungus U . maydis . We observed that of all the HDACs only Hos2 and , to a lesser extent , Clr3 , have significant roles in cell growth , morphology , dimorphism or virulence . Moreover , we identified the signalling pathways that regulate Hos2 and Clr3 to control the yeast-hypha transition in this fungus . Finally , we described putative functional redundancies between Hos2 and Clr3 in the control of U . maydis pathogenicity . Hos2 plays crucial roles during fungal pathogenesis [32–34] . In the plant pathogenic fungi Magnaporthe oryzae , Cochliobolus carbonum and Fusarium graminearum , loss of Hos2 compromises appressorium biology during the plant penetration and post-penetration stages of pathogenic development [33 , 34 , 55 , 56] . Our results reveal another fungus in which Hos2 is required for virulence . However , our study suggests that Hos2 controls pathogenicity by a different mechanism to the ones described for other plant pathogenic fungi . Indeed , the reduced virulence of U . maydis Δhos2 mutants appears to be due to their inability to shift from yeast-like to polarised growth , which is needed to form infective filaments . Interestingly , yeast-to-hypha transition defects have also been noticed in Δhos2 mutants of the human pathogen C . albicans . However , in this fungus , hos2 deletion results in constitutive filamentation , which is the opposite phenotype to the one we have observed in U . maydis [33] . Strikingly , the roles we have identified for Hos2 in this work have a number of parallels with our previous findings , including opposing regulatory roles between U . maydis and C . albicans , for the general transcription repressor Tup1 . Our results show that Δhos2 mutants are impaired at pre- and post-fusion events during mating , again resembling what we had previously observed for Tup1 [27] . Interestingly , one of the mechanisms through which Tup1 controls gene expression is via interaction with histone deacetylases [44 , 45] . One possibility is that Hos2 could be the HDAC recruited and controlled by Tup1 in its regulation of mating-type gene expression . However , a double Δtup1Δhos2 mutant strain was almost non-virulent , showing a much more severe phenotype than the single mutants ( Fig 6 ) , suggesting independent roles for the two proteins . Consistent with this idea , Tup1 controls the expression of mating-type genes downstream of the pheromone responsive MAP kinase cascade [27] , whereas we did not observe such an effect in Δhos2 mutants . Although the contribution of HDACs to Tup1 function in transcription initiation is well established , there is also evidence to suggest that Tup1 can regulate gene expression by HDAC-independent mechanisms . For example , Wong and Struhl , as well as Parnell and Stillman , showed that the repressive role of Tup1 in certain promoters relies mainly on its interaction with histone acetyl-transferase ( HAT ) complexes [57 , 58] . Interestingly , it has recently been shown that deletion of the Gcn5 histone acetyl-transferase causes a constitutive filamentous phenotype in U . maydis [59 , 60] . From this evidence we could propose a model whereby Tup1 controls the dimorphic switch by regulating the Gcn5 HAT , with the Hos2 HDAC regulated by another parallel mechanism . By which mechanism does Hos2 control the dimorphic switch ? One possibility is that hos2 expression is controlled by the environmental signals that ultimately activate this transition . As shown in S9 Fig , hos2 expression did not vary substantially in different media , upon activation of the cAMP or MAPK signalling pathways , or during filamentous growth in charcoal-containing media . These observations suggest that external cues regulate mating-type genes via changes to Hos2 activity rather than expression . Supporting this conclusion , we observed that the mating-type defects of Δhos2 mutants were more pronounced on charcoal-containing CM plates than on PD ones ( Fig 2C ) . Thus , Hos2 might control mating in response to the nutrients present in the medium . Our functional analyses of the MAPK cascade and of the cAMP-PKA pathway , reveal that Hos2 controls mating-type gene expression downstream of cAMP signalling . The simplest and most obvious explanation for this observation is that Hos2 directly targets mating-type genes in response to cAMP signalling . Our ChIP analysis confirms that Hos2 directly binds the prf1 ORF , as well as some of its target genes . Furthermore , Hos2 recruitment responds to cAMP and positively correlates with the expression level of mfa1 and pra1 mating-type genes . Interestingly , Hos2 binding at prf1 ORF does not respond to cAMP addition , suggesting that there might be differences in how Hos2 is recruited at mfa1 and pra1 on one hand or at prf1 on the other hand . Altogether , it is likely that Hos2-mediated deacetylation of these genes promotes transcriptional elongation , as has been proposed for S . pombe , S . cerevisiae and C . albicans Hos2 target genes [37 , 50 , 61] . Strikingly , we detected even stronger Hos2 binding to the prf1 promoter than along its ORF ( S10A Fig ) . Interestingly , the prf1 upstream regulatory region is particularly long , extending over 2 Kb . The strongest region of Hos2 binding overlapped with a cAMP- and nutrient-responsive upstream activating sequence ( UAS ) [19] . Upon cAMP addition , Hos2 binding slightly decreased specifically at this UAS ( S10B Fig ) . This strong binding of Hos2 upstream of the prf1 ORF was unexpected because S . cerevisiae and C . albicans Hos2 have been shown to mainly bind gene bodies [29 , 31 , 50] . An exciting possibility is that U . maydis Hos2 binds to this UAS to control the transcription of a putative long non-coding RNA , similar to what has been observed upstream of the S . cerevisiae IME1 gene [62] . This could open up an exciting novel line of research toward understanding prf1 expression regulation , which is critical for the dimorphic switch and U . maydis virulence . Another non-mutually exclusive possibility is that Hos2 controls the activity of PKA , or its regulatory subunits , or affect the amount of intracellular cAMP . However , Δhos2 mutants showed sensitivity to TSA , accompanied by increased H4K16 acetylation ( S3 Fig ) . Additionally , our ChIP experiments show that mating-type genes are direct targets of Hos2 ( Figs 8 and S10 ) . Altogether , our data strongly suggest that Hos2 directly regulates mating-type gene expression to control mating and virulence in U . maydis . Interestingly , a previous study has indeed reported that the Hos2/Set3 complex functions in the C . albicans cAMP pathway . C . albicans mutants lacking the Hos2/Set3 complex are hyper-sensitive to filamentation-inducing signals [32] . This is in marked contrast with the role of U . maydis Hos2 that we have reported here . Interestingly , the effect of cAMP-PKA signalling is also different between C . albicans and U . maydis . Hyperactivation of this pathway in U . maydis causes a multiple budding phenotype , while its inhibition causes filamentation , as observed in mutations affecting the regulatory and catalytic subunits of PKA [47 , 48] . In contrast , hyperactivation of the same pathway in C . albicans leads to filamentation . To conclude , the role of Hos2 in the cAMP pathway may be conserved between C . albicans and U . maydis , even though the pathway has opposite effects on dimorphism in the two organisms . Although Δhos2 mutants clearly showed the most prominent virulence phenotype , we also noticed a modest infection defect in Δclr3 mutants . It is known that HDACs can compensate for each other in the regulation of gene expression [51–53] . Therefore , we wanted to check for functional redundancies between HDACs in the control of fungal virulence . In C . albicans , CaHos2 and CaHda1 ( Clr3 in U . maydis and S . pombe ) play opposite roles in the control of morphological changes [54]; however , how these two HDACs interact with each other to control dimorphism is still unknown . Interestingly , in U . maydis , Δhos2Δclr3 double mutants exhibited much more severe pathogenesis defects than either of the single mutants . From these data we can conclude that Clr3 participates in U . maydis virulence , particularly when Hos2 is absent , revealing a genetic interaction between both genes during U . maydis infection . Like Hos2 , we found that Clr3 is required for normal expression of a and b mating-type genes . Our analysis of filamentation , virulence and mating-type gene expression in single and double mutants suggests that Hos2 can compensate for Clr3 in U . maydis , in contrast to their opposing roles in C . albicans [54] . Nevertheless , a common role for Hos2 and Clr3 in the control of C . albicans dimorphism might be taking place too . Recently , CaHda1 has been shown to be required for the maintenance of filamentation in nutrient-poor media that lacks the preferred nitrogen source glutamine [63] . Interestingly , hyperfilamentation of C . albicans Δhos2 mutants occurs virtually in all conditions except for nitrogen starvation conditions , where they are unable to form filaments , while the wild-type can [54] . Thus , Hos2 and Clr3 might have a common role in the control of C . albicans dimorphism with respect to certain nutritional conditions . In summary , we have shown that the class I histone deacetylase Hos2 has a crucial role in the control of dimorphism and virulence in U . maydis . Our data demonstrate that Hos2 binds to and is required for the expression of mating-type genes upon activation of the cAMP pathway . We believe that our results contribute to a better understanding of how HDACs regulate morphological changes and fungal pathogenic development . In the future , it will be interesting to study other HDAC complex components and which histone acetyltransferase is involved in these processes . Our results also provide interesting insights into the regulatory mechanisms governing the expression of virulence genes via conserved signalling pathways , which may be highly relevant to other fungal pathogens .
Escherichia coli DH5α was used for cloning purposes . Growth conditions for E . coli [64] and U . maydis [16 , 65] have been described previously . U . maydis strains relevant to this study are listed in S1 Table . For cell width and length analyses , exponentially growing cultures were generated by growing cells in YEPSL liquid media for 12 hours , diluting them in the same media to OD600 = 0 . 05 and then allowing them to reach OD600 = 0 . 8–1 prior to light microscopy examination . For the estimation of growth rates , U . maydis cells were grown until exponential phase in YEPSL at 28°C , diluted in the same media to OD600 = 0 . 05 and the number of cells counted . After eight hours of incubation at 28°C , cells were counted again and growth rates calculated . For cell viability assays , a total of 200 cells , grown in the conditions indicated in each case , were plated on YPD plates and incubated at 28°C for 3 days prior to colony counting . For charcoal mating and filamentation assays , cells were grown on YEPSL until exponential phase , washed twice with sterile distilled water , spotted onto PD-charcoal plates and grown for 24–48 hours at 25°C . For RNA extractions , exponentially-growing cultured cells were spread out on CM charcoal plates at a concentration of OD600 = 0 . 1 per cm2 . Cells used to analyse the expression of hos2 in different media were grown to OD600 = 0 . 5–0 . 8 for early exponential phase cultures ( E ) and to OD600 = 4 for late exponential phase cultures ( L ) . Induction of the crg1 promoter in AB31 [41] or FB1Pcrg1fuz7DD [22] strains , and their derivatives , were done as previously described . Mating [16] and pheromone stimulation [22] assays were performed as previously described . To determine the effect of Trichostatin A ( TSA; Sigma T8552 ) on conjugation tube formation , cells were grown in CMD until exponential phase ( OD600~0 . 5–0 . 8 ) ; then 1 μg/ml of TSA was added to 1 ml of cell culture and incubated for 2 hours at room temperature prior to pheromone addition . Control cultures were treated only with DMSO or pheromone . In all cases , conjugation tube formation was quantified 5 hours after pheromone addition . For pathogenicity assays , U . maydis strains were grown to exponential phase , concentrated to OD600 = 3 , washed twice with sterile distilled water , and injected into 7 day old maize ( Zea mays ) seedlings ( Early Golden Bantam ) . Tumour formation was quantified 14 days post infection . For the cAMP-PKA pathway induction assays , cells were cultured to saturation in rich PD-broth media without cAMP , diluted in the same media and grown to exponential phase . Cells were then washed with sterile distilled water , diluted to OD600 = 0 . 2 and grown for 8 hours in PD-broth with 6 mM cAMP or the same media without cAMP . Cells were then recovered by centrifugation for 4’ at 4500 rpm and 4°C , washed twice with sterile distilled water and frozen in liquid nitrogen . RNA extraction was performed as described below for liquid cultured cells . RT-qPCR was performed as described below , using the corresponding primers ( see S2 Table for sequences of primers ) . Molecular biology techniques were used as previously described [64] . U . maydis DNA isolation and transformation procedures were carried out following the protocol of [1] . Deletion constructs were generated according to [66] . To generate single deletion U . maydis mutants for hos1 ( um04234 ) , hos2 ( um11828 ) , hos3 ( um10914 ) , hda1 ( um02065 ) , hda2 ( um11308 ) and clr3 ( um2102 ) , fragments of the 5’ and 3’ flanks of their open reading frames were generated by PCR on U . maydis FB1 genomic DNA with the following primer combinations: UmHOS1KO5–1/UmHOS1KO5–2 and UmHOS1KO3–1/UmHOS1KO3–2; UmHOS2KO5–1/UmHOS2KO5–2 and UmHOS2KO3–1/UmHOS2KO3–2; UmHOS3KO5–1/UmHOS3KO5–2 and UmHOS3KO3–1/UmHOS3KO3–2; UmHDA1KO5–1/UmHDA1KO5–2 and UmHDA1KO3–1/UmHDA1KO3–2; UmHDA2KO5–1/UmHDA2KO5–2 and UmHDA2KO3–1/UmHDA2KO3–2; UmCLR3KO5–1/UmCLR3KO5–2 and UmCLR3KO3–1/UmCLR3KO3–2 ( Sequences in S2 Table ) . These fragments were digested with SfiI and ligated with the 1 . 9 Kb SfiI carboxin resistance cassette , 2 . 7 Kb SfiI hygromycin resistance cassette , or 1 . 5 Kb SfiI neourseotricin resistance cassette as described previously [67] . Ligation products were then cloned into pGEM-T-EASY vector ( Promega ) . Linear PCR-generated DNA was used for U . maydis transformation of each construct . For hos2 overexpression , the p123-hos2 plasmid was generated . p123-hos2 is a p123 [68] derivative whose eGFP fragment has been substituted with the hos2 ORF . For this purpose , the hos2 open reading frame was amplified by PCR with the oligonucleotides Umhos2ATGXmaISmaI and Umhos2StopNotI , containing XmaI and NotI restriction sites , respectively . The Phusion high fidelity DNA polymerase ( Invitrogen ) was used . The PCR product was digested with XmaI and NotI , purified , and cloned into the p123 vector digested with the same restriction enzymes . To generate SG200Potefhos2 and SG200Δhos2Potefhos2 strains , p123-hos2 was linearized with SspI and integrated into the SG200 or SG200Δhos2 ip locus by homologous recombination . For HA3 tagging of endogenous Hos2 , the plasmid pUMa792Hos2 was generated . pUMa792Hos2 is a pUMa792 derivative ( P . Müller and R . Kahmann , http://www . mikrobiologie . hhu . de/ustilago-community . html ) in which a 1 Kb PCR-generated DNA fragment corresponding to the C-terminal part of the Hos2 open reading frame , has been cloned in frame with the three HA epitope repeats present in the plasmid . Additionally , a 1kb PCR-generated DNA fragment of the 3’UTR of Hos2 has also been cloned into the same plasmid , downstream of the hygromycin resistance cassette . To clone these fragments , the Gibson Assembly Cloning Kit ( New England Biolabs ) was used . The DHO915-DHO916 primer pair was used to amplify the 1 Kb DNA fragment of the C-terminal part of the Hos2 open reading frame . The DHO917-DHO918 primer pair was used to amplify the 1 Kb DNA fragment corresponding to the Hos2 3’UTR . Oligonucleotide design and enzymatic reactions were performed according to the manufacturer’s instructions . We used 0 . 1 pmol of each of the above mentioned DNA fragments together with 0 . 1 pmol of the DNA fragments resulting from the digestion of pUMA792 with SfiI . To generate the FB1Hos2HA3 strain , the corresponding 4694 bp DNA fragment , containing the HA3 tagged Hos2 C-terminal fragment , the hygromycin resistance cassette and the 1 Kb DNA fragment corresponding to the 3’UTR of Hos2 , were amplified from pUMa792Hos2 with the oligonucleotides DHO919-DHO920 and transformed into U . maydis FB1 protoplasts . The high-fidelity Phusion DNA polymerase was used . Sequences of all these primers can be found in S2 Table . Successful cloning was verified by PCR , sequencing and western blot . For RNA extractions of AB31 and FB1Pcrg1fuz7DD , cells were grown until OD600 = 0 . 5–0 . 8 and the nar and crg promoters induced as described above . 25 ml of AB31 or FB1Pcrg1fuz7DD induced cells ( 5 hours and 30 minutes induction ) were pelleted in 50 ml tubes , washed once with sterile distilled water and frozen in liquid nitrogen . Samples were stored at-80°C . Cells were thawed on ice for 10 minutes and suspended in 2 ml TRIzol Reagent ( Invitrogen ) . Cells were lysed by the addition of a 0 . 5 ml volume of acid-washed glass beads , vortexed 10 times for 20 seconds each and incubated at room temperature for 5 minutes . Each sample was divided into two 1 . 5 ml tubes , each containing 1 ml of the sample . 200 μl of chloroform was added to each tube , which were then inverted during 15 seconds and incubated at room temperature for 3 minutes . Samples were centrifuged at 11500 rpm for 15 minutes in a bench-top centrifuge at 4°C . 500 μl of the supernatant was transferred to new RNase-free 1 . 5 ml tubes . 500 μl of 2-propanol was added followed by incubation at room temperature for 10 minutes . Samples were centrifuged at 11500 rpm for 10 minutes . The 2-propanol was removed and 100 μl of 70% ethanol added . After 5 minutes of centrifugation at 11500 rpm and 4°C , the ethanol was removed and samples dried at 37°C for 10 minutes . Finally , samples were suspended in 50 μl of RNase-free distilled water and RNA concentration quantified using a Nanodrop spectrophotometer ( ThermoFisher Scientific ) . For RNA extractions of strains grown on charcoal plates , biomass was recovered , transferred to liquid nitrogen pre-chilled mortars and crushed to a powder . The processed material was then transferred into 50 ml tubes containing 3 ml of TRIzol Reagent ( Invitrogen ) and a 1 ml volume of acid-washed glass beads . RNA extraction was performed as described above . 10 μg of each RNA sample was treated with rDNase I according to the manufacturer’s protocol in a final volume of 30 μl . Samples were incubated for 30 minutes at 37°C . 3 μl of ribonuclease inactivation reagent was then added to each sample and incubated for 2 minutes at room temperature with constant shaking . Samples were then centrifuged at 13000 rpm for 2 minutes . 15 μL of the supernatant were taken for further steps . 3 μl of each DNA-free RNA sample were used for retrotranscription by first mixing them with 1 μl of 10 mM dNTPs , 0 . 17 μl of random oligonucleotide and 8 . 83 μl of RNase-free water . Next , samples were heated to 65°C for 5 minutes and then cooled to 4°C for 1 minute in a thermocycler . Immediately , 4 μl of 5X first strand cDNA buffer , 1 μl of 0 . 1 M DTT , 1 μl of RNase inhibitor ( Rnasin ) and 1 μl of Superscript III reverse transcriptase ( Invitrogen ) were added to each sample . Samples were then heated to 65°C 1 h to allow retrotranscription to take place and finally held at 12°C . After retrotranscription , 90 μl of RNase-free water was added to each sample , to reach a final volume of 110 μl . For qPCR analysis , samples were diluted 200 times ( 4 μl of sample in 796 μl of milliQ water ) . Samples of non-retrotranscribed RNA were used as controls for genomic DNA contamination . For standard curve serial dilutions of wild-type cDNA in the corresponding inducing conditions were used . The constitutively expressed ppi gene was used as an internal control . The SYBR green method was used . bE1 , mfa1 , pra1 , prf1 and ppi oligonucleotide sequences for RT-qPCR have been described previously [69–71] . Samples were loaded in triplicate and three independent experiments were performed in all cases . For normalisation , the lowest expression value was set to 1 . Logarithmic-transformed expression values were used to allow visualisation of pairwise comparisons that include highly divergent values , which were not clearly visible in raw data plots . For homogeneity logarithmic transformation was applied to every gene expression analysis . To extract proteins from U . maydis , 25 ml of exponentially growing U . maydis cells were washed once with water and frozen in liquid nitrogen . The frozen pellet was resuspended in 300 μl of Workman Extract Buffer ( 40mM HEPES-NaOH pH7 . 4 , 350 mM NaCl , 0 . 1% NP40 , 10% glycerol , 1mM PMSF , 1 μg/ml Pepstatin A , 1 μg/ml Bestatin , EDTA-free protease inhibitor tablets ( Roche ) ) and transferred into 1 . 5 ml screw-cap tubes containing acid washed glass beads . Cells were fragmented using a FastPrep homogeniser ( MP Biomedicals ) with power set to 6 . 0 using 4 x 40” pulses at maximum speed with 3 minutes rest between each pulse . Dry-ice was used to cool down the machine at the beginning of the process . To recover the liquid fraction , the bottom of the 1 . 5 ml tube was drilled with a needle , placed into a 5 ml tube and centrifuged at 1000 rpm for 1 minute . Samples ( supernatant and any pellet formed ) were then transferred to a new 1 . 5 ml tube and centrifuged for 10 minutes at 13000 rpm and 4°C . The supernatant , containing total protein extract , was finally transferred into a new 1 . 5 ml tube . For extraction of total protein with the aim of detecting histone modifications , a workman extract buffer with a higher salt concentration ( 600 mM NaCl ) was used . Protein concentration was measured using the Bradford protein assay . 60 μg of total protein was used for classical western blots . For western blots used to detect histone modifications , 20 μg of total protein was loaded . To detect histone acetylation , an H4K16Ac ( Cell Signalling; E2B8W; Rabbit; 1:1000 in TBST 5% BSA ) specific antibody was used . Total histone H3 was detected using an Anti-Histone H3 antibody ( Merk-Millipore; clone AS3; 1:2500 in TBST 5% fat-free milk ) . α-tubulin was detected using a Monoclonal Anti-α-Tubulin antibody ( Sigma; clone B-5–1–2; Mouse; 1:10000 in TBST 5% fat-free milk ) . For the histone assays , proteins were transferred from 15% acrylamide gels to PVDF membranes for 45 minutes at 90 V . Other western blots were carried out using 10% acrylamide gels , with proteins transferred to nitrocellulose membranes for 90 minutes at 90 V . 50 ml of exponentially growing U . maydis cells ( OD600 = 0 . 5–0 . 8 ) were cross-linked by adding 1% formaldehyde and incubated for 30 minutes with constant shaking at room temperature . Glycine was added to a final concentration of 250 mM and cultures incubated at room temperature for 10 minutes . Samples were then centrifuged for 4 minutes at 4500 rpm and 4°C and resuspended in 10 ml of ice-cold PBS 1X . Centrifugation and resuspension in PBS 1X was repeated once . Samples were then centrifuged for 4 minutes at 4500 rpm and 4°C , resuspended in 800 μl of PBS 1X and transferred to 2 ml screw-cap tubes . Samples were centrifuged at 6000 rpm for 2 minutes at room temperature , and the pellet frozen in liquid nitrogen and stored at-80°C . Pellets were thawed on ice for 5 minutes and resuspended in 800 μl of ice-cold LB140 ( 50 mM HEPES-KOH pH7 . 4 , 140 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% sodium deoxycholate , supplemented with 1 mM PMSF , 1 μg/ml of pepstatin A , 1 μg/ml of bestatin , and 1X EDTA-free protease inhibitor tablets ( Roche ) ) . Acid-washed glass beads were added up to the meniscus of the liquid and cells were fragmented using the FastPrep homogeniser as described above but with 6 x 40” pulses at maximum speed with 3 minutes rest between each pulse . Dry-ice was used to cool down the machine both at the beginning and during the process ) . Samples were recovered by drilling a hole into the 2 ml screw-cap tube , which was introduced into a 5 ml tube , and centrifuged for 1 minute at 1000 rpm . Liquid and any pellet formed were transferred from the 5 ml tube into a new 1 . 5 ml tube . Samples were then centrifuged at maximum speed for 5 minutes at 4°C . Pellets , containing the chromatin insoluble fraction , were resuspended in 800 μl of LB140 , and centrifuged at maximum speed for 5 minutes at 4°C . Supernatant was discarded , the pellet resuspended in 1 . 2 ml of LB140 and PMSF re-added . Samples were then sonicated in a Brandson sonicator , using an amplitude of 20% with 12 x 10” pulses separated by 50” rest periods . Under these conditions chromatin fragments had an average size of about 500 bp . 5 μl of the sonicated chromatin extract was used for determining protein concentration using the Bradford protein assay . 2% of the chromatin extract was kept as input , and frozen at-20°C . A total of 1 mg of chromatin extract was used for IP in a final volume of 500 μl . Volume was adjusted using LB140 . For IP , to each 500 μl sample , 3 μg of anti-HA anti-body ( abcam , ab9110 ) were used . Samples were incubated at 4°C for 16 hours on a rotating platform . Protein G sepharose beads ( GE Healthcare ) were pre-washed twice with distilled sterile water , twice with LB140 and resuspended in LB140 . 50 μl of pre-washed beads were added to each sample , which were then incubated for 5 h at 4°C on a rotating platform . Samples were then centrifuged at 5000 rpm for 1 minute at room temperature . Supernatant was removed by aspiration and beads washed twice with 800 μl of each of the following buffers: ( i ) WB140 ( 50 mM HEPES-KOH pH7 . 4 , 140 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% sodium deoxycholate ) ; ( ii ) WB500 ( 50 mM HEPES-KOH pH7 . 4 , 500 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% sodium deoxycholate ) ; ( iii ) WBLiCl ( 10 mM Tris-HCl pH 7 . 4 , 250 mM LiCl , 1 mM EDTA , 0 . 5% NP40 , 0 . 5% sodium deoxycholate ) . The first wash was performed by adding 800 μl of the buffer , mixing by inverting the tube and centrifuging the cells at 5000 rpm for 1 minute at room temperature using a bench-top centrifuge . For the second wash , samples were incubated in the corresponding buffer for 5 minutes at room temperature with constant shaking , prior to centrifugation . Finally , cells were washed with TE10:1 ( 10mM Tris-HCl pH 7 . 4 , 1mM EDTA ) with a 5 minute incubation at room temperature . After centrifugation for 1 minute at 5000 rpm , supernatant was removed , the centrifugation repeated and the remaining TE10:1 supernatant completely removed . All buffers were ice cold . For elution , 105 μl of TES ( 50 mM Tris-HCl pH 7 . 4 , 10 mM EDTA , 1%SDS ) was added to each sample , incubated for 30 minutes at 65°C with occasional mixing . Samples were centrifuged at maximum speed for 1 minute at room temperature and 100 μl of supernatant were transferred into a new 1 . 5 ml tube . Another 105 μl of TES were added to the beads , incubated for 15 minutes at 65°C with occasional mixing . After centrifuging at maximum speed for 1 minute at room temperature , another 100 μl of the supernatant was transferred to the corresponding previous 1 . 5 ml tube , resulting in a final volume of 200 μl for each sample . To reverse the crosslinking , input samples were thawed at room temperature , the volume was adjusted to 50 μl with LB140 , and 150 μl of TES added to each sample . Next , both input and IP chromatin extracts were incubated at 65°C for 16 hours . 250 μl of TE10:1 , 1 μl of glycogen ( 20 mg/ml ) and 7 μl of Proteinase K ( 20 mg/ml ) were added to each sample and incubated for 2 h at 37°C . To extract and precipitate the DNA , 450 μl of phenol:chloroform:isoamyl alcohol ( 25:24:1 ) was added to each sample , followed by vortexing for 20 seconds and centrifugation at full speed for 5 minutes at room temperature . Supernatant was transferred to a new 1 . 5 ml tube . 450 μl of chloroform:isoamyl alcohol ( 24:1 ) was added to each sample , vortexed for 5 seconds and centrifuged at full speed for 2 minutes at room temperature . Supernatant was transferred into a new 1 . 5 ml tube . 12 . 5 μl of 5 M NaCl and 1 ml of ice-cold 100% ethanol were then added . Samples were incubated at-80°C for 2 h . A maximum of 4 samples were centrifuged at a time for 10 minutes at full speed and 4°C . Supernatant was removed and pellet washed with 1 ml of 70% ethanol . After removing the ethanol , pellets were dried at room temperature for 15 minutes . Finally , 100 μl of TE10:1 was added to each sample and incubated for 10 minutes at room temperature . For qPCR a 3 fold dilution of each immunoprecipitated sample and a 200-fold dilution of input ( 2% ) samples were used . The oligonucleotides used for each amplicon were: ( i ) DHO1024 and DHO1025 for prf1–3 . 5 kb; ( ii ) DHO981 and DHO982 for prf1–2kb; ( iii ) DHO1022 and DHO1023 for prf1–1 . 5 kb ( UAS ) ; ( iv ) DHO983 and DHO984 for prf1–0 . 5 kb; ( v ) DHO985 and DHO986 for prf1 ORF ( 5’ ) ; ( vi ) DHO991 and DHO992 for bE1; and ( vii ) DHO1001 and DHO1002 for um01779 . Oligonucleotide sequences can be found in S2 Table . The oligonucleotide sequences for mfa1 , pra1 , prf1 ORF ( 3’ ) and ppi amplicons have previously been described in [69 , 70] . U . maydis Hos1 , Hos2 , Hos3 , Hda1 , Hda2 and Clr3 sequences were obtained from the MIPS U . maydis database ( http://mips . gsf . de/genre/proj/ustilago/ ) . S . cerevisiae , S . pombe and C . albicans HDAC sequences were obtained from SGD ( http://www . yeastgenome . org/ ) , PomBase ( http://www . pombase . org/ ) and CGD ( http://www . candidagenome . org ) databases , respectively . Multiple sequence alignments and phylogenetic analyses were performed using MEGA5 [72] . Domain structure analysis was performed using the InterProScan Sequence Search tool from the European Bioinformatics Institute ( http://www . ebi . ac . uk/ ) . Pfam retrieved domains were used . Schematic representation of the retrieved domains was performed maintaining the proportions of each domain with respect to the whole protein sequence length . Images showing cell morphology , conjugation tube formation or b-dependent filamentation were taken using bright field Zeiss Apotome microscopes from the Centro Andaluz de Biología del Desarrollo ( CABD ) and from the Montpellier RIO Imaging ( MRI ) platform at the Centre de Recherche de Biochimie Macromoléculaire ( CRBM ) . 40X and 63X objectives were used . Measurements of cell length and width were performed using ImageJ . Image acquisition , analysis and processing was carried out using Metamorph and Adobe Photoshop CS2 . Data are expressed as means ±SD of at least triplicate samples . Statistical analysis and significance was assessed using GraphPad Prism 5 and considered significant if p-values were <0 . 05 . t-test was used when comparing two means for differences . Fisher’s exact test was used to compare two or more groups with a categorical variable as the outcome . For multiple comparisons , one-way or two way ANOVA followed by Duncan’s new multiple range test was used . We performed Mann-Whitney tests ( also known as Wilcoxon rank-sum test ) to compare the distributions of disease symptoms induced by U . maydis strains . U . maydis sequence data can be found in the NCBI protein libraries under accession numbers , XP_760381 . 1 for Hos1 , XP_756808 . 1 for Hos2 , XP_761874 . 1 for Hos3 , XP_758212 . 1 for Hda1 , XP_757499 . 1 for Hda2 and XP_758249 . 1 for Clr3 . Other sequences used in this study have the following accession numbers: S . cerevisiae Hos1 , NP_015393 . 1; Hos2 , NP_011321 . 1; Hos3 , NP_015209 . 1; Hda1 , NP_014377 . 1; Rpd3 , NP_014069 . 1; S . pombe Hos2 , NP_594079 . 1; Clr3 , NP_595104 . 1; Clr6 , NP_595333 . 1; C . albicans Hos1 , XP_723599 . 1; Hos2 , XP_717660 . 1; Hos3 , XP_720292 . 1; Hda1 , XP_718271 . 1; Rpd3 , XP_715765 . 1 .
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Many pathogenic fungi need to undergo morphological changes in order to infect their hosts . Typically , pathogenic fungi switch from a non-pathogenic yeast-like form to a polarised pathogenic filament . This morphological switch is regulated genetically and is triggered by specific environmental conditions . Histone deacetylases ( HDACs ) are important regulators of chromatin structure and gene expression . In this study , we investigate the role of HDACs as targets of the signalling pathways that activate fungal virulence programs in response to specific external signals . We identify two specific HDACs , Hos2 and Clr3 , that are required for the virulence of the corn smut fungus , Ustilago maydis . Our results reveal that Hos2 and Clr3 function in the cAMP-PKA cascade , a nutrient-sensing pathway conserved between all eukaryotes .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2015
|
The Hos2 Histone Deacetylase Controls Ustilago maydis Virulence through Direct Regulation of Mating-Type Genes
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Gene expression differences between divergent lineages caused by modification of cis regulatory elements are thought to be important in evolution . We assayed genome-wide cis and trans regulatory differences between maize and its wild progenitor , teosinte , using deep RNA sequencing in F1 hybrid and parent inbred lines for three tissue types ( ear , leaf and stem ) . Pervasive regulatory variation was observed with approximately 70% of ∼17 , 000 genes showing evidence of regulatory divergence between maize and teosinte . However , many fewer genes ( 1 , 079 genes ) show consistent cis differences with all sampled maize and teosinte lines . For ∼70% of these 1 , 079 genes , the cis differences are specific to a single tissue . The number of genes with cis regulatory differences is greatest for ear tissue , which underwent a drastic transformation in form during domestication . As expected from the domestication bottleneck , maize possesses less cis regulatory variation than teosinte with this deficit greatest for genes showing maize-teosinte cis regulatory divergence , suggesting selection on cis regulatory differences during domestication . Consistent with selection on cis regulatory elements , genes with cis effects correlated strongly with genes under positive selection during maize domestication and improvement , while genes with trans regulatory effects did not . We observed a directional bias such that genes with cis differences showed higher expression of the maize allele more often than the teosinte allele , suggesting domestication favored up-regulation of gene expression . Finally , this work documents the cis and trans regulatory changes between maize and teosinte in over 17 , 000 genes for three tissues .
Changes in the cis regulatory elements ( CREs ) of genes with functionally conserved proteins have been considered a key mechanism , if not the primary mechanism , by which the diverse forms of multicellular eukaryotic organisms evolved [1]–[3] . Variation in CREs allows for the deployment of tissue specific patterning of gene expression , differences in developmental timing of expression , and variation in the quantitative levels of gene expression . Furthermore , modification of CREs , as opposed to coding sequence changes , are assumed to have less pleiotropy and consequently have a lower risk of unintended deleterious effects in secondary tissues . The importance of CREs for the development of novel morphologies is supported by the growing catalog of examples for which differences in gene specific CREs between closely related species contributed to the evolution of diversity in form [4] . While compelling evidence for the importance of CREs in evolution has come from mapping causative variants to CREs , additional evidence has emerged from genomic analyses that show abundant cis regulatory variation both within [5]–[8] and between species [9]–[11] . Some studies have reported a bias such that genes with cis differences between species or ecotypes show preferential up-regulation of the alleles of one parent , possibly as a result of natural selection [7] , [11] , [12] . Consistent with the hypothesis that cis differences are a key element of adaptive evolution , divergence for cis regulation between yeast species is more often associated with positive selection than trans divergence [10] , [13] . Crop plants offer a powerful system for the investigation of evolutionary mechanisms because they display considerable divergence in form from their wild progenitors , yet exhibit complete cross-fertility with these progenitors [14]–[16] . QTL fine-mapping experiments have provided multiple examples of modified CREs that underlie trait divergence between crops and their ancestors . These studies include examples in which cis changes confer the up-regulation of a gene during domestication [17] , the down-regulation of a gene [18] , [19] , the loss of a tissue specific expression pattern [20] , the gain of a tissue specific expression pattern [21] , and a heterochronic shift in the expression profile [22] . These diverse modifications suggest that changes in CREs offer a powerful means to fine-tune gene expression to generate new plant morphologies . Several transcriptional profiles contrasting crops and their ancestors have been performed , although the experimental designs used did not allow separation of cis and trans effects . These studies have shown that hundreds or even thousands of genes have altered expression in crops as compared to their progenitors and that genes with altered expression often show evidence for selection [23]–[25] . The data suggest massive alterations in gene expression profiles accompanied domestication . Work in cotton and maize shows a more frequent up-regulation of genes in the crop as compared to the wild ancestor , however whether this was due to cis or trans effects was not discernible [23] , [24] . In this study , we used RNAseq to parse genome-wide expression differences between maize and its progenitor , teosinte ( Zea mays ssp . parviglumis ) , into cis and trans effects . Three tissue types were assayed: immature ear , seedling leaf , and seedling stem . Approximately 70% of the 17 , 000 genes assayed show evidence of regulatory divergence between maize and teosinte . Over 1 , 000 genes show cis divergence that is highly consistent across our sampled maize and teosinte lines . For ∼70% of genes with consistent cis effects , the cis effects are specific to a single tissue type . The number of genes with cis differences is greatest for the ear , which underwent a profound transformation in form during domestication . Genes with cis regulatory differences between maize and teosinte are correlated with genes that show evidence for positive selection during domestication while trans genes are not . Maize also possesses less cis regulatory variation than teosinte over all genes and this deficit in maize is greatest for genes with cis regulatory divergence from teosinte . We observed a directional bias in genes with cis differences such that maize alleles were more frequently up-regulated than teosinte . Finally , our data provide a catalog of cis and trans regulatory variation for over 17 , 000 genes in three tissue types for 15 maize and teosinte inbred lines .
RNAseq data for seedling leaf , seedling stem ( including the shoot apical meristem ) , and immature ear from six maize inbreds , nine teosinte inbreds , and 29 of their F1 hybrids were used to examine variation in gene expression on a genome-wide scale . In total , 259 RNAseq libraries were constructed from an average of 1 . 96 biological replicates for each parent inbred and F1 . Overall , 996 million , 1 . 13 billion , and 1 . 21 billion F1 hybrid and 286 million , 283 million , and 276 million parent RNAseq reads were collected for ear , leaf , and stem tissue types , respectively ( Table 1 ) . These reads were aligned to custom-made parent specific pseudo-transcriptomes containing an average of 54 , 000 segregating sites ( SNPs or small indels ) in each of the 29 maize-teosinte contrasts . For F1 hybrid reads , 556 million , 672 million , and 716 million reads mapped to pseudo-transcriptomes in ear , leaf , and stem tissue , respectively . In parent inbred lines , 171 million , 170 million , and 163 million reads mapped to the pseudo-transcriptomes ( Table 1 ) . Thus , approximately the same percentage of reads ( 58 . 2% and 59 . 6% ) mapped to pseudo-transcriptomes in both the F1 hybrids and parent datasets with about 7 . 15% of the total reads mapping to segregating sites in the individual F1 hybrids and their parents . RNAseq reads for all 29 F1 hybrids and 15 parents that aligned to segregating sites in the transcriptomes represent 23 , 816 , 24 , 055 , and 24 , 643 genes for ear , leaf and stem tissues , respectively ( Table 2 ) . The union of these three groups is 25 , 619 genes , which is 65% of the 39 , 423 genes from the maize filtered gene set ( version 5b ) . We applied a filter to this list , requiring a read depth of 100 in both the parent inbreds and F1 hybrids . This filter reduced the lists to 15 , 939 , 15 , 931 , and 16 , 018 genes in ear , leaf , and stem tissues , respectively . The union of these three groups is 17 , 579 genes or ∼45% of the filtered gene set . There is a large degree of overlap among the genes expressed in the three tissues with 14 , 421 of 17 , 579 genes ( 82% ) seen in all three tissues . Of the remaining genes , 1 , 467 are in two tissues and 1 , 691 are in only a single tissue ( Figure 1 ) . We measured cis regulatory effects as log2 of the ratio of maize to teosinte read counts in F1 hybrids , and the trans effects as the difference between the F1 and parent log2 ratios . Binomial and Fisher's exact tests were used to determine whether these ratios deviated from 1∶1 and to assign genes to one of seven regulatory categories ( Table 3; see Materials and Methods ) . In overall maize versus teosinte comparisons , about 70% of genes ( 69 . 27% ear , 74 . 21% leaf , and 63 . 82% stem genes ) from the three tissues were classified as having significant cis and/or trans regulatory effects ( Figure 2 , Figure S1 ) . The remaining genes were classified as having conserved ( 18 . 6% , 15 . 5% , and 20 . 7% ) or ambiguous ( 12 . 1% , 10 . 2% , and 15 . 5% ) expression patterns . All three tissues had similar proportions of genes falling into the different regulatory categories in the overall maize-teosinte comparison ( Figure 2 , Figure S1 ) . We asked what proportion of regulatory divergence between maize and teosinte was due to cis effects by calculating the ratio: |cis|/ ( |cis|+|trans| ) [11] . Overall , cis effects account for 45% , 42% and 47% of regulatory divergence for ear , leaf and stem tissue , respectively ( Table S1 ) . We further asked the relative contribution of cis and trans in generating large expression differences by binning genes based on the magnitude of overall expression difference ( |log2 parent ratio| ) . This analysis shows that the proportion of divergence due to cis is greater with greater total divergence in expression ( Figure 3 ) . At high degrees of expression divergence between maize and teosinte ( log2 change of 5 or more ) , over 75% of divergence is due to cis . Thus , large expression differences appear to be caused primarily through difference in cis regulation as opposed to trans . A primary goal in this study was to identify genes with cis regulatory differences between maize and teosinte . Such genes in the cis only or cis plus trans regulatory categories ( CCT genes ) are candidates for direct targets of selection during maize domestication or improvement for altered gene expression . We identified 5 , 618 ear , 5 , 398 leaf and 5 , 435 stem CCT genes in the overall analysis ( Table 2 ) . To exclude CCT genes with little data , the list was filtered to include only genes assayed in at least 15 maize-teosinte F1s involving at least three maize and five teosinte inbred lines . This filtering resulted in 4 , 770 ear , 4 , 494 leaf , and 4 , 601 stem CCT genes ( union of 8 , 396 genes ) . Next , we asked if genes on the filtered CCT list consistently favor expression of maize or teosinte alleles in the individual F1 hybrids . The goal was to exclude CCT genes for which the significant overall cis effect was caused by a large expression bias in one or a few F1 crosses . We defined three levels of consistency: groups A , B and C for which 100% , 90% and 80% of F1s showed the same directionality , respectively . Groups A , B , and C genes combined across tissues contained 69 , 1 , 042 , and 2 , 326 genes respectively ( Table 2 ) . Thus , relatively few of the 8 , 396 filtered CCT genes show a significant overall cis effect that is highly consistent among 15 or more F1 hybrids . Visual examination of Figure 1 shows a greater density of cis genes ( black points ) with positive log2 hybrid expression ratios than with negative ratios , suggesting cis evolution during domestication more often favored alleles with increased expression in maize relative to teosinte . Consistent with this visual observation , the number of CCT ( ABC list ) genes with a positive ( maize biased ) versus negative ( teosinte biased ) log2 hybrid expression ratio are 947∶598 , 844∶483 and 826∶545 for ear , leaf and stem , respectively ( Table S2 ) . All of these ratios are significantly different from a 50∶50 unbiased expectation ( binomial test , p<0 . 001 ) . Additionally , a plot of the distribution of log2 hybrid expression ratio for CCT genes shows a greater density of genes with positive values for all three tissues ( Figure 4 , Figure S2 ) . The apparent bias in directionality of cis evolution could be the result of error in our bioinformatics pipeline . One potential error is preferential alignment of maize RNAseq reads due to greater sequence divergence of teosinte lines from the reference transcriptome ( B73 ) as compared to maize inbred lines . If such systematic error exists , the observed bias in directionality of cis evolution would be expected to be greatest for F1s involving the reference B73 ( zero alignment bias of maize reads and high bias for teosinte ) and less extreme for crosses between teosinte and non-reference maize lines ( moderate bias for non-reference maize and high bias for teosinte ) . To test this expectation , we calculated the number of CCT ( ABC list ) genes with positive versus negative log2 hybrid expression ratios for F1s involving B73 and non-B73 maize parents separately . For ear tissue , there are 569 teosinte-biased and 975 maize-biased genes for B73 F1s and 606 teosinte-biased and 939 maize-biased genes for non-B73 F1s . A Fisher's exact test fails to reject the null hypothesis of equivalent ratios ( p = 0 . 18 ) . There was also no evidence for non-equivalent ratios with the other two tissue types ( Table S3 ) . Thus , we see no evidence for significantly greater bias for maize alleles in crosses involving B73 versus the non-reference maize parents , suggesting alignment bias does not explain the excess of CCT genes with the maize allele expressed higher than the teosinte allele . Both the domestication bottleneck and selection during domestication are expected to reduce variation in maize as compared to teosinte . We asked if these reductions in variation are apparent in our gene expression data . To quantify whether variation in maize or teosinte was the source of the variation in expression ratios among F1 hybrids , a linear model was fitted on a gene-by-gene basis with maize and teosinte inbred parent as explanatory factors for hybrid expression ratio . Among ∼13 , 000 genes included in this analysis , the maize parent explains only 85% as much variation as the teosinte parent ( Figure 5 , Table S4 ) . The reduction is consistent across all seven regulatory categories ( Figure S3 ) . This represents the general reduction in diversity of maize as compared to teosinte , presumably a result of the domestication bottleneck . While the bottleneck predicts reduced expression variation in maize overall , genes that were targets of selection for regulatory differences should have an even greater reduction in expression variation . Consistent with this expectation , we observed a greater reduction in variation in maize as compared to teosinte for CCT genes than the full set of ∼13 , 000 genes ( Figure 5 , Table S4 ) . For the CCT-ABC genes , maize contributes 78% of teosinte variation , for the AB group ∼73% , and for the A group only 54% of teosinte variation . Thus , among our strongest cis regulatory candidate genes ( A group ) , the data indicate that maize explains only about half as much of the cis regulatory variation as teosinte . The reduction in gene expression variation in maize versus teosinte is also seen in the number of individual genes with significant effects due to the maize and/or teosinte parent on the F1 expression ratio ( Table S5 ) . In terms of numbers of genes on the AB list CCT genes , there were 2 . 0 to 2 . 5 fold more genes for which only the teosinte parent contributed significant variation to the F1 expression ratios than genes for which only the maize parent contributed . This difference is 5-fold in favor of teosinte for CCT genes on the A list . We compared CCT genes to putative targets of selection during maize domestication and improvement from a recent study [26] . There is significant enrichment for CCT genes among selection candidate genes for all three tissues ( Table 4 ) . The strength of the association with selected genes is strongest for the union of CCT genes from all three tissues . For example , there are 139 CCT-AB genes among the selected genes , while 87 . 7 would be expected by chance . Also , there were 10 CCT ( A-list ) genes from stem tissue among selected genes when only 2 . 16 were expected , a nearly 5-fold enrichment . XPCLR scores ( cross population composite likelihood ratios ) quantify the degree of support for positive selection on a genomic region . A comparison of the distributions of ln ( XPCLR ) scores at the transcriptional start site ( calculated by Hufford et al . [26] ) for the union of CCT genes ( A , AB , and ABC ) versus genes with conserved expression between maize and teosinte shows that CCT genes having a higher mean XPCLR than conserved genes ( Figure 6 ) . The distribution for conserved genes is significantly different than all three CCT gene lists in terms of shape ( Kolmogorov-Smirnov test , p<1 . 59e-5 ) and overall mean ( t-test , p<5 . 00e-5 ) . This pattern was also observed for tissue specific comparisons ( Figure S4 ) . A goal of this study was to explore the relative importance of cis versus trans regulatory divergence during maize domestication . To address this question , we examined the evidence for selection on genes with cis only effects in comparison to genes that have trans only effects . Fisher's exact tests on 2×2 contingency tables tabulating cis and trans genes with selection feature genes from Hufford et al . [26] show cis only genes are significantly enriched ( p-value<0 . 05 ) for selection in 6 of 9 comparisons , while trans only genes are never enriched and are actually significantly underrepresented among selected genes in two cases ( Table 5 ) . We assessed the degree of correspondence between our CCT genes and 612 differentially expressed genes identified by a recent microarray study in maize [24] . We constructed 2×2 contingency tables for differentially expressed ( DE ) and non-differentially expressed ( NDE ) genes from the two studies . A Fisher's exact test shows a highly significant degree of correspondence between the two studies for all three tissues ( Table 6 ) . Using our CCT-AB lists , ∼24 genes are identified as DE in both studies per tissue while about 7 are expected by chance . However , the absolute level of correspondence between the two studies is rather low . For example , of the 350 leaf genes identified as DE by RNAseq , only 24 ( 7% ) were also identified by the microarray study ( Table S6 ) . Thus , while the overlap between our two studies is statistically significant , the two methodologies resulted in largely different lists of DE genes . The largely different lists of DE genes identified by microarray and RNAseq analysis could be due in part to the fact that the microarray analysis includes genes with trans and cis×trans differences . To assess the proportion of the 612 DE genes from the microarray study that have trans versus cis effects , we examined the RNAseq-based regulatory categories of the ∼250 DE genes ( 241 , 262 , 259; ear , leaf , and stem ) for which there is both microarray and RNAseq data ( Table S7 ) . About 20% of these genes are classified as trans only or cis×trans by RNAseq , while 55% are classified as either cis only or cis+trans . The remaining 25% are classified as conserved , ambiguous or compensatory . These results suggests the very different lists of DE genes from the two technologies can only be explain in part by inclusion of genes with trans only effects in the list of DE genes from the microarray study . Most of the difference between the lists is likely due to differences in tissue , germplasm , environment , sampling error , or other sources of error . In a recent study , Eichten et al . [27] identified differentially methylated regions ( DMRs ) in maize and teosinte . We compiled a list of the nearest genes both upstream and downstream of each DMR , which gave a list of 332 genes . Of these genes , we have RNAseq data from 115 , 116 , and 121 for the ear , leaf , and stem tissues , respectively . Of these genes , 19 , 13 , and 17 genes were on the CCT-ABC gene lists ( Table S8 ) . We asked if CCT-ABC list genes are over-represented among the DMR associated genes as compared to random expectation and found that they are not ( Fisher's exact test , p>0 . 10 ) . Finally , DMR methylation does not correspond with the allele specific expression of CCT-ABC list genes with only ∼50% agreement between methylation status and allele expression ( Table S9 ) . We compared the proportions of genes showing dominant versus additive gene action in the cis only and trans only ABC lists . Dominant gene action of trans only genes will occur when there are haplo-sufficient loss of function ( LOF ) alleles at their trans regulators . In contrast , the effects of cis regulatory elements are expected to be purely additive in absence of transvection or similar mechanism [28] . Cis only genes classified as having dominant gene action may indicate error in classification due to trans effects below the level of statistical detection . Consistent with the expectation that dominance is more likely for trans only genes , the proportion of genes classified as dominant is higher for trans only genes in all three tissue types ( Figure 7 , Table S10 ) . It has been proposed that the allelic variants responsible for evolution during domestication are primarily recessive LOF alleles [29] . Under this model , a non-domesticated allele would be dominant to the recessive LOF domesticated allele . Among cis only genes with dominant gene action , dominance of the maize versus teosinte allele does not differ from the 50∶50 expectation ( Figure 7 , Table S10 ) . Among trans only genes with dominant gene action , the maize allele is dominant to the teosinte allele more often than expected by chance . These results are counter to the proposal that domestication favored recessive LOF alleles . We examined CCT-ABC genes for enrichment of several functional classes of genes including transcription factors , genes in known metabolic pathways , genes underlying QTL , and gene ontology ( GO ) groups . First , a list of maize transcription factors and their corresponding families were compiled from the transcription factor database [30] . Although CCT genes ( AB-list ) were found to be slightly enriched for several transcription factor families ( ARF and MADS-MIKC ) by Fisher's exact tests , these results do not stand up to Bonferroni multiple test correction ( Table S11 ) . We conclude that there is no compelling evidence that CCT genes are enriched for transcription factors . CCT ( AB list ) genes were also compared with results from a recent QTL mapping experiment for a number of domestication and improvement traits [31] . We compared observed versus expected overlap between CCT genes from the three tissues to the genes located within 1 . 5 LOD QTL support intervals for 11 traits . Testing was done on a trait by trait basis and restricted to 1 . 5 LOD QTL intervals containing 20 or fewer genes . After correction for multiple testing ( Bonferroni ) , no significant enrichment for CCT-AB genes in domestication QTL was observed ( Table S12 ) . The greatest enrichment was seen with the trait ear diameter for which there were four CCT genes assayed in ear tissue within the QTL intervals when only 1 . 22 were expected by chance ( Fisher's exact test , p = 0 . 03 ) . A test for enrichment of CCT genes and trans only genes in 15 different metabolic pathways defined in the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) was done using Fisher's exact test on 2×2 contingency tables . There was no compelling evidence for enrichment/depletion of either groups of genes in any of the 15 pathways tested ( Table S13 ) . The smallest p-value identified was for the fatty acid degradation pathway in leaf tissue for CCT ABC genes ( p = 0 . 008 ) , however this result does not remain significant after Bonferroni multiple test correction . We tested for GO term enrichment and depletion in the CCT and trans only gene lists . These analyses found significant GO term associations in the leaf CCT-ABC gene list for depletion for DNA binding ( Table S14 ) . For trans only genes , significant enrichment for a number of GO terms in the ear tissue was detected for transcription factor and photosynthesis related terms with additional enrichment for ribosomal GO terms found in the leaf tissue ( Table S14 ) .
Approximately 70% of the ∼17 , 000 assayed genes exhibit some form of regulatory difference between maize and teosinte , suggesting considerable regulatory divergence has occurred during maize domestication and improvement ( Figure 2 , Figure S1 ) . Similar proportions of gene regulatory differences were observed in recent studies of Drosophila [11] and yeast [13] species . The high amount of expression divergence between maize and teosinte is not surprising given the incredible divergence in their morphology and the exceptional expression variation existing within maize itself . For example , a recent study found 27 . 9% of maize genes are only expressed in a subset of maize inbred lines with over a thousand genes absent from the reference B73 genome [32] . The high fraction of genes exhibiting regulatory divergence between maize and teosinte should be viewed with this perspective . It includes all genes with specific combinations of significant binomial and Fisher's exact tests as outlined in Table 3 . Given the massive amount of data analyzed , statistically significant cis and trans effects were detected for as small as 1 . 02 fold expression differences , which seem unlikely to have biological significance . Moreover , many genes with significant regulatory differences between maize and teosinte “on average” show diversity in expression within maize and within teosinte such that the favored allele can change depending on the specific pair of maize-teosinte inbred lines . Our primary focus was on genes with cis regulatory differences between maize and teosinte as this class of genes should include many direct targets of selection for domestication traits . The overall analysis classifies 8 , 396 genes ( 47 . 8% ) as CCT genes that show cis divergence between maize and teosinte . This is a remarkably high proportion that reflects the considerable statistical power to detect small differences , likely with minimal biological importance , and does not consider whether the expression difference is consistent across maize-teosinte F1 comparisons . Thus , we focused our analysis on CCT A , B , and C gene lists showing consistent directionality of expression bias in 100% , 90% and 80% ( respectively ) of maize-teosinte comparisons . This approach narrows the list to 1 , 079 genes ( A and B lists ) that we consider our most robust candidates for genes with cis regulatory differences between maize and teosinte . CCT candidate genes from the three tissues were largely different . Among the 1 , 079 CCT genes on A and B lists , ∼73% were identified in only a single tissue . This includes 336 ear , 257 leaf , and 198 stem specific genes . In contrast , only 77 of the 1 , 079 CCT genes were classified as CCT candidates in all three tissues . These results are consistent with previous studies that examined cis regulatory divergence between taxa in multiple tissues [8] , [12] . These results highlight the importance of assaying multiple tissues and developmental stages . It also exposes a major weakness of genomic scale expression assays such as ours . Given the complex ways in which gene expression is regulated across different tissue types , genomic-scale assays in one or even multiple tissues are a very blunt instrument for exploring the evolution of gene expression . While CCT genes are mostly tissue-specific , genes overall are not . Of the 17 , 579 genes assayed with at least 100 read depth in both the parents and F1 hybrids , 14 , 421 were expressed in all three tissues . This high proportion of genes ( 82% ) expressed in all tissues creates a false impression that sampling one tissue type at one point in developmental time provides a reasonable assay of all tissues at all developmental times . The discordant observations that CCT genes are mostly tissue specific , while genes overall are expressed in all tissues can be explained by tissue specific enhancers or repressors . Among the sampled tissues , it is notable that ear has the largest number of overall ( 555 ) and tissue specific ( 336 ) CCT genes ( Figure 1 ) . The greater number of ear CCT genes identified may be related to the profound morphological changes that differentiate the maize and teosinte ear . By comparison , the sampled leaf and stem tissues differ only by size between maize and teosinte and not morphological structure . Similar to our results , an imbalance in number of DE genes in different tissues was also observed in a recent study in Arabidopsis [12] , where tissues differed by up to 80% in number of DE genes . In the F1 hybrid analysis , ∼55% of genes have higher maize expression than teosinte . Higher expression of the maize allele also occurs in the parent inbred lines , except for leaf , where an equal number of genes favor maize and teosinte alleles . This trend of up-regulated maize expression extends to the CCT gene lists , where ∼60% of genes favor the maize allele ( Figure 4 ) . Preferential expression for one of the parents ( maize ) is consistent with several previous studies in multiple organisms including maize [24] , cotton [23] , Arabidopsis [12] , Cirsium [7] , and fruit fly [11] . While our study mitigates alignment bias with parent specific pseudo-transcriptomes and perfect alignment to segregating sites , this method is unlikely to fully eliminate this bias . Consequently , we cannot exclude the possibility that bias for maize expression is an artifactual result . The mechanism for a general up-regulation of maize alleles across many genes is unclear . One possibility would be a remodeling of the epigenetic landscape during domestication . This study shows cis and trans regulatory differences account for ∼45% and ∼55% of regulatory divergence between maize and teosinte , respectively ( Table S1 ) . These values suggest relatively equal contributions of these two mechanisms to regulatory divergence . However , this ignores the contribution of cis effects to large expression differences where cis accounts for nearly 80% of the expression divergence ( Figure 3 ) . The observation that cis effects account for the majority of expression divergence of genes with large expression differences was also seen in Drosophila [11] . The prominence of cis effect among genes with the largest divergence in expression may indicate cis regulation is a more effective mechanism than trans for producing large changes in gene expression . A recent study using microarrays [24] showed greater overlap than expected by chance with CCT candidate genes ( Table 6 ) . However , the two studies produced largely different lists of DE genes . One difference between the RNAseq and microarray study is that the latter includes DE of genes with trans only and cis×trans regulatory regimes , which are excluded from RNAseq based CCT lists . This difference offers only a partial explanation for the differences between the two studies . Of 262 microarray DE genes assayed by RNAseq in leaf tissue , RNAseq classifies 31 as trans only and 16 as cis×trans ( Table S7 ) . Another 153 genes on the microarray DE list are classified as cis only or cis+trans by RNAseq , leaving 62 genes ( 24% ) for which the two studies disagree . Although much of this disagreement can likely be attributed to factors such as differences in sampling or other sources of error , it reminds us of the imprecision in these types of data . During domestication , maize experienced both a population bottleneck that caused a general reduction in genetic diversity as well as selection that further reduced diversity in targeted regions of the genome [33] , [34] . A recent genome-wide analysis estimated that the maize genome possess approximately 81% of the nucleotide diversity found in teosinte [26] . Our data allows us to ask whether maize domestication has caused a parallel reduction in cis regulatory variation . Overall , maize possesses only ∼85% of the cis regulatory variation seen in teosinte ( Figure 5 , Table S4 ) , a value very close to the reduction in nucleotide diversity . Moreover , the observed reduction in cis regulatory variation is greatest for genes that show evidence for cis differences between maize and teosinte . The loss of cis regulatory variation increases over C , B and A lists of CCT genes with CCT A-list genes possessing only ∼50% of the cis regulatory variation seen in teosinte . This trend suggests selection during the domestication process for cis regulatory variation . The high level of expression variation still present in teosinte also represents an untapped source of diversity for maize breeders , which could be explored with transcriptome profiling . Genomic scans for evidence of selection during adaptive transitions have become a powerful tool in evolutionary biology [35] . Such scans provide both a measure of the prevalence of selection and a list of candidate genes for further study . In comparisons of RNAseq data with a recent genomic selection scan in maize [26] , we sought to determine the specific target of selection in terms of cis and trans . If cis regulatory evolution was an important mechanism during maize domestication , then CCT genes should be enriched for selection candidates . In contrast , genes whose expression divergence between maize and teosinte are governed by trans effects should not be enriched for selection candidates , given that the trans regulators and not the trans responsive genes were the putative targets of selection . Consistent with these expectations , we observed a highly significant enrichment for selection candidate genes among CCT and cis only genes with no enrichment among trans only genes ( Tables 4 , 5 ) . Although greater than expected overlap was observed between selection candidates and CCT genes , the degree of correspondence is far from perfect . For example , 25 of 36 CCT A-list genes assessed in the genome selection scan from ear tissue do not show evidence for selection . The misalignment between the selection candidate and CCT lists is likely due to both biological factors and artifacts . Domestication genes such as tga1 for which the causative change appears to have been an amino acid change will not appear on CCT gene lists [36] . Similarly , genes with cis differences that were the target of soft sweeps or for which the signature of selection is weak for other reasons are expected to be missed in selection scans . The candidate for the major gene ( ZmSh5 . 1 ) responsible for the loss of seed shattering during maize domestication is not on the list of selection candidates [26] , [37] . These properties of genomic scans remind us of their limits and the probabilistic arguments on which they are based . It has been proposed that the allelic variants responsible for evolution during domestication are often recessive LOF alleles such that the wild progenitor allele would be dominant to the domesticated allele [29] . While there is some support for this hypothesis from rice in the form of increased frequency of deleterious amino acid changes [38] , recent reviews of QTL studies found no compelling evidence for dominance of the progenitor alleles and few LOF alleles among positionally cloned domestication genes [39] , [40] . There is some support that LOFs are relatively common among genes contributing to varietal differences within crops [39] , [40] . Dominance is expected to be uncommon for gene expression of cis only genes , since dominance at cis only genes requires a mechanism such as transvection [28] and this is unknown in maize . In absence of mechanisms such as transvection , if a CRE in the progenitor allele of a cis only gene produced 5 RNAseq reads and disruption of the CRE reduced expression to 1 read per allele in the crop , then the diploid progenitor would have 10 reads , the crop would have 2 reads , and their F1 hybrid would have 6 ( 5+1 ) reads . The gene would be classified as purely additive . However , for trans only genes , if haplo-sufficient , dominance of the maize or teosinte allele could be observed . Consistent with the first expectation , we found greater dominance among trans only genes versus cis only genes ( Figure 7 ) . The observed dominance effects among the cis only genes may be due to statistical error , trans effects that are below the level of statistical significance , or a molecular mechanism such as transvection . Among trans only genes with dominance , the maize allele is dominant more often than the teosinte allele ( Figure 7 ) . This observation fails to support the hypothesis that recessive LOFs were favored during domestication . GO term analysis showed genes involved in sequence-specific DNA binding transcription factor activity were enriched in the trans only class ( Table S14 ) . These trans only genes are responding to unknown upstream regulators that differ between maize and teosinte . It is the putative upstream regulators and not the trans only genes themselves that are the potential targets of selection during domestication/improvement . This result suggests that transcription factors are frequently downstream in regulatory cascades that were targets of selection during maize domestication and improvement . The identity of the upstream regulators of our trans only genes are unknown but they likely include genes involved in signal transduction , hormonal regulation of gene expression , and other transcription factors . The CCT gene lists are candidates for these unknown regulators of the trans only genes . A product of this study is a resource for researchers looking for preliminary data on the expression patterns of specific genes . To facilitate this use , two supplemental datasets have been made available from the Dryad Digital Repository: http://dx . doi . org/10 . 5061/dryad . 4kh67 . Supplemental dataset 1 contains overall data for the complete set of 25 , 619 genes including regulatory classification , summed RNAseq read counts , expression ratios , and other summary information for each gene . Supplemental dataset 2 contains read counts for F1 hybrid and parent contrasts on a cross-by-cross basis . An example of the value in these supplemental datasets is barren stalk1 ( ba1 ) , a known maize single gene mutant that causes a defect in branch initiation for both the whole plant and tassel [41] . In our study , ba1 was one of our strongest candidates with all assayed crosses showing higher expression of the maize allele in the ear . The overall shift in expression was substantial ( ∼4-fold ) and this shift was caused solely by cis regulatory differences . ba1 was also found to be under selection during maize domestication in two independent studies [26] , [41] . These combined observations suggest that selection for a CRE drove up-regulation of ba1 in the ear , perhaps resulting in a greater number of rows ( branches ) of kernels in the maize ear as compared to teosinte . Compelling evidence for this hypothesis could be obtained by fine-mapping and identifying the hypothesized CRE and demonstrating with expression assays that the maize and teosinte alleles of the CRE have the imagined effects on gene expression during ear development and on phenotype ( kernel row number ) in the adult ear . ba1 illustrates the power of genomic scans to identify strong candidates for future study that can inform us about the fine details of evolution under domestication .
Six maize inbred lines , nine teosinte inbred lines , and 29 of their 54 possible maize-teosinte F1 hybrids were used in this experiment ( Table S15 ) . An average of 1 . 96 biological replicates of each genotype was used . Plants were grown in growth chambers with a 12 hour dark-light cycle for up to 6 weeks , after which they were moved to a greenhouse . Fifty to 100 milligram samples of the immature ear , leaf , and seedling stem were harvested for RNA extraction during this time . Leaf and seedling stem ( including the shoot apical meristem ) tissue was collected at the v4 leaf stage . Single ears from maize and F1 hybrid plants were collected when the ears weighed 50 to 100 milligrams with silks just beginning to be visible . Teosinte ears were also collected when silks just started to appear , however , due to the small size of teosinte ears 7 to 16 ears ( average of 11 . 27 ) from each plant were pooled to obtain ∼50 milligrams of tissue . These three tissue types are referred to as the ear , leaf , and stem tissues . Total RNA was extracted from the plant tissues using a standard TRIzol protocol , quantified by spectrophotometer , and normalized to 1 µg/µL in nuclease free water . Starting with 5 µg total RNA , we generated polyA selected , strand specific , barcoded RNAseq libraries with a previously published protocol using a five minute fragmentation time and 12 PCR amplification cycles [42] . Library adapters used barcode sequences of four and five base pairs ( Table S16 ) designed to balance percent nucleotide composition within the first five base pairs of sequence reads and to have at least two base pair differences from any other barcode . RNAseq libraries were then pooled in groups of 14 ( F1s ) or 15 ( parents ) , and the pooled libraries sequenced on one lane ( parents ) or two lanes ( F1s ) of an Illumina HiSeq2000 sequencer . The raw sequence data has been deposited in NCBI Sequence Read Archive with accessions SRX710894-711341 and the Gene Expression Omnibus ( GEO ) Series with accession number GSE61810 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE61810 ) . Mitigating mapping bias through use of multiple references or enhancing the reference with segregating sites is critical for allele specific studies [43] , [44] . We investigated parent specific de novo transcriptome assemblies using Trinity [45] , but ultimately pursued an enhanced reference approach due to poor Trinity assembly qualities ( Text S1 ) . The pipeline developed in this study , based on the work by Wang et al . [46] , accounts for mapping bias through parent specific pseudo-transcriptomes generated by incorporating polymorphisms derived from non-B73 genomic paired-end reads into the B73 reference followed by alignment and evaluation of RNAseq read depth at segregating sites . Pseudo-transcriptomes were constructed using the B73 reference genome ( version AGPv2 ) and transcriptome ( version ZmB73_5a_WGS ) plus ∼403 . 1 million ( 17 . 5× coverage ) paired-end genomic sequencing reads from each of the other 14 inbred lines ( Table S17 ) . For each of the 14 non-B73 inbreds , paired-end genomic sequencing reads were aligned to the reference genome with the BWA aligner ( version 0 . 5 . 9 ) [47] . Only uniquely mapping reads with up to two mismatches were used to limit false polymorphism detection due to paralogous read alignment . Single nucleotide polymorphisms ( SNPs ) and small insertions and deletions ( indels ) with respect to B73 were called using the GATK package ( version 1 . 0 . 5588 ) [48] , [49] and filtered ( Text S1 ) to include only polymorphisms that were homozygous in the inbred with read depth of at least 4× . A strand bias filter was also applied to ensure that the polymorphism was detected on both the plus and minus strand . Polymorphisms surviving these filters were then inserted into the reference B73 transcriptome to make a pseudo-transcriptome for each parent . For each of the 29 maize-teosinte pairs , a robust set of segregating sites was determined by comparing the pseudo-transcriptomes of the two parents and taking the sites where: the two parental alleles differed , coverage in genomic read alignment was at least four for both parents within the read length ( 88 bp ) of the site , and no heterozygous polymorphisms were detected in genomic read alignments of the two parents within the read length of the site . RNAseq reads from each F1 hybrid and each corresponding pair of inbred parents were then aligned to the combined pseudo-transcriptomes of the two parents ( B73 reference transcriptome used for the B73 parent ) using the Bowtie aligner ( version 0 . 12 . 7 ) [50] . We assessed allele specific expression by counting read depth from each parent at segregating sites ( Table S18 ) . Since only perfect alignments were allowed , assignment of reads to parents was straightforward ( a read from a given parent could only align to this parent's allele at a segregating site ) . We calculated F1 hybrid and parent maize∶teosinte expression ratios for each gene of the 29 individual F1 hybrid comparisons . Total depth at segregating sites summed over genes was highly correlated between biological replicates ( average of 95% , Table S19 ) and consequently read depth was pooled for the various genotypes . Gene expression ratios for F1s ( e . g . B73×TIL01 ) were then calculated by dividing total maize read depth by total teosinte read depth summed over all segregating sites in the gene . The parent expression ratios for individual maize-teosinte comparisons were calculated the same way from parental RNAseq reads , except total parental read depths at segregating sites were corrected for differences in total number of reads between the two parent lines ( Text S1 ) . The result of these calculations was a set of 29 matched F1 and parent expression ratios consisting of maize∶teosinte read counts for each gene . We produced overall maize-teosinte expression ratios for each gene by summing read depth over all maize-teosinte hybrid comparisons . To calculate the overall F1 expression ratio , the maize and teosinte read depths from the F1 hybrids were simply summed over all segregating sites in a gene and across all hybrids . The calculation of the overall parent expression ratio required weighting to avoid counting the parent reads multiple times for each of the F1 comparisons in which it was a parent and to compensate for the fact that different parents had variable total numbers of reads ( Text S1 ) . Only genes with a read depth of at least 100 in both the F1 and its parent comparison were included . The result of these calculations was an overall F1 and parent ratio of read counts for each gene . To check whether single F1s caused aberrant estimates of the cis effect with these overall ratios , we performed a drop1 analysis and found that inclusion/excluding of single F1s had on negligible effects ( Text S1 and Figure S5 ) . Finally , as a exercise in proof of concept , we compared allele specific expression results for several specific genes of known importance in domestication with expectations from the literature and found a good fit between our data and published results ( Text S1 and Table S20 ) . The combination of F1 hybrid and parent inbred expression data allows estimation of both the cis and trans effects on gene expression . For the F1 hybrids , the maize and teosinte alleles at each gene are in a common trans cellular environment , and thus any deviation of the maize∶teosinte F1 expression ratio from 1∶1 represents purely cis effects . By contrast , the maize∶teosinte parent expression ratio is a combination of the cis and trans effects and any deviation of this ratio from 1∶1 reflects the combined cis plus trans effects . Therefore , the trans effects can be estimated by subtracting the F1 hybrid ratio ( cis ) from the parent ratio ( cis plus trans ) . Maize and teosinte gene expression as measured by the read depth counts at genes was used for statistical testing of cis and trans effects . Significant cis and trans effects were determined using binomial and Fisher's exact tests as described in McManus et al . [11] . In brief , two binomial tests were used to identify genes with maize∶teosinte expression ratios significantly different from 1∶1 in the F1 hybrid and parent comparisons . Genes with an expression ratio significantly different from 1∶1 for the F1 hybrid and/or parent comparison were then subjected to a Fisher's exact test to determine if the parent and F1 hybrid maize∶teosinte expression ratios were different from one another . An FDR rate of 0 . 5% using Storey's q-value [51] was used to compensate for the large number of statistical tests being performed . We investigated a higher FDR cutoff of 5% to include additional genes in downstream analyses , but only observed a minor increase in the number of candidate genes ( Text S1 , Table S21 ) . The combination of the two binomial tests and Fisher's exact test allowed us to classify each gene into one of seven different regulatory categories ( Table 3 ) as described in McManus et al . [11] . Genes under selection for expression during maize domestication are expected to show a maize∶teosinte cis expression ratio that is significantly different from 1∶1 . These genes can fall into the cis only ( C ) or cis plus trans ( CT ) groups on Table 3 . We call this combined group CCT genes and these differential expression candidates are the focus of many of our analyses . The list of CCT genes from the overall test was large ( 5 , 618 ear; 5 , 398 leaf; 5 , 435 stem ) and reflects the considerable statistical power to detect slight overall expression biases for genes with thousands of reads aligning to segregating sites . We observed significant maize∶teosinte expression biases as small as 1 . 02-fold in the overall tests . Such small differences seem unlikely to have biological importance and genes showing these small differences are weak candidates for genes with cis expression variation that is causal in maize domestication and improvement . Therefore , we applied filters to identify candidates with the strongest and most consistent regulatory differences . To narrow down the CCT gene list to candidate genes that show the strongest evidence for differential cis regulation between maize and teosinte , we applied two filters . ( 1 ) Genes with the strongest evidence for cis differences will fall in the CCT group and have data from a majority of sampled maize and teosinte parents . Thus , we filtered the initial list of CCT genes for those with data from at least fifteen F1 hybrids that include at least three different maize inbreds and five different teosinte inbreds . ( 2 ) For genes with cis differences that contributed to maize domestication/improvement , the direction of the expression bias should be highly consistent among the individual F1 hybrids . Consequently , CCT genes were classified for consistency of directionality of expression bias among the F1s with several levels of candidate genes partitioned at 100% , 90% and 80% of F1s showing the same directionality . In calculating these percentages , we used read depth for each F1 at the gene to weight the contribution of the F1s to the overall percentage . We refer to the CCT genes with 100% , 90% and 80% consistent directionality among the F1s as the A-list , B-list and C-list , respectively . For comparative purposes , we made similar A , B and C lists of genes for the cis only or trans only classes . These CCT and cis only gene lists ( A , AB , and ABC ) were used in downstream analyses in comparison with conserved or trans only genes to explore the role of cis regulatory variation in maize domestication . CRE diversity within maize and teosinte is expected to display as variation in the F1 hybrid expression ratios . We asked whether cis expression variation among F1 hybrid ratios was more heavily influenced by maize or teosinte inbred parent . Since three teosinte inbreds ( TIL05 , TIL10 , and TIL15 ) were involved in only a single F1 each , the three F1s involving these inbreds were removed in order to balance the number of maize and teosinte inbred parents for this analysis . Genes were tested for variation among the F1 expression ratios ( cis variation ) using a linear model on a gene-by-gene basis that fitted the log2 ( maize∶teosinte ) F1 expression ratio to the maize and teosinte parents as independent variables . Significant maize and teosinte parent terms were identified with an F-test ( p<0 . 05 ) using the R drop1 function . The data for each F1 was weighted by total depth at the gene to account for variable read depth in the F1 hybrids . Our gene expression dataset consisting of parent inbred and hybrid expression ratios gives the opportunity to address dominant and additive gene expression on a genome-wide scale . We calculated the additive effect , dominant effect , and dominant/additive ( D/A ) ratio for each gene and maize-teosinte F1 hybrid comparison . The overall maize-teosinte average D/A ratio was then calculated after exclusion of outlier D/A ratios ( Figure S6 ) by iteratively applying the Dixon method [52] . Genes were classified as additive if |D/A|<0 . 25 and as having dominant gene action if 0 . 75<|D/A|<1 . 25 . Following calculation of D/A ratios , we examined ratios for cis only and trans only genes for altered degrees of dominance . We assessed whether genes in different expression classes ( CCT , cis only and trans only ) are over or under represented in several functional categories as compared to all genes or genes with conserved expression levels between maize and teosinte . Generally , we tested all CCT gene lists ( A , AB , and ABC ) with the most weight given to the CCT-AB gene lists , which we consider our best candidates genes . The categories we tested include transcription factors , several metabolic pathways , gene ontology ( GO ) categories , selection candidates , and domestication QTL . A list of maize transcription factors and their associated families was obtained from the plant transcription factor database [30] . Metabolic enzyme cDNA sequences for starch and lipid metabolism pathways in maize were downloaded from the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) [53] , [54] and matched with genes from the maize filtered gene set ( version 5b ) by BLAST . Matches ( single gene hit with percent identity greater than 95% ) were found for 370 out of 379 genes and used to test for enrichment of genes in the various metabolic pathways . Genes under positive selection during maize domestication and improvement were taken from a recent genomic scan for selection [26] . We obtained a list of QTL associated with maize domestication and improvement traits from Table A . 1 of a recent QTL analysis [31] . In general , enrichment or depletion of genes in expression classes among various functional categories was tested with Fisher's exact tests on 2×2 contingency tables . For QTL , enrichment of CCT genes among the genes within QTL 1 . 5 LOD support intervals were tested separately for each trait and only included QTL whose 1 . 5 LOD support intervals included 20 or fewer genes . For genes under positive selection during domestication and improvement [26] , we also tested for a significant difference in the cross population composite likelihood ratio ( XPCLR ) at the transcription start site between CCT genes ( A , AB , and ABC ) versus genes with conserved expression using the Kolmogorov-Smirnov and simple t-tests . Finally , GO term enrichment and depletion was tested using the goseq package [55] in R [56] using median gene length to adjust the GO term reference . The base background GO term reference consisted of genes assessed in 15 crosses , three unique maize , and five unique teosinte inbred lines with a cumulative depth of 100 at segregating sites in F1 and parent comparisons . GO terms occurring at least five times in the background reference were tested with p-values corrected for multiple testing using the Benjamini-Hochberg method [57] .
|
Modification of cis regulatory elements to produce differences in gene expression level , localization , and timing is an important mechanism by which organisms evolve divergent adaptations . To examine gene regulatory change during the domestication of maize from its wild progenitor , teosinte , we assessed allele-specific expression in a collection of maize and teosinte inbreds and their F1 hybrids using three tissues from different developmental stages . Our use of F1 hybrids represents the first study in a domesticated crop and wild progenitor that dissects cis and trans regulatory effects to examine characteristics of genes under various cis and trans regulatory regimes . We find evidence for consistent cis regulatory divergence that differentiates maize from teosinte in approximately 4% of genes . These genes are significantly correlated with genes under selection during domestication and crop improvement , suggesting an important role for cis regulatory elements in maize evolution . This work provides valuable insight into the evolution of gene regulatory elements during the domestication of an important crop plant .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"gene",
"expression",
"genetics",
"plant",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"evolutionary",
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] |
2014
|
The Role of cis Regulatory Evolution in Maize Domestication
|
The trematode Fasciola hepatica is responsible for chronic zoonotic infection globally . Despite causing a potent T-helper 2 response , it is believed that potent immunomodulation is responsible for rendering this host reactive non-protective host response thereby allowing the parasite to remain long-lived . We have previously identified a growth factor , FhTLM , belonging to the TGF superfamily can have developmental effects on the parasite . Herein we demonstrate that FhTLM can exert influence over host immune functions in a host receptor specific fashion . FhTLM can bind to receptor members of the Transforming Growth Factor ( TGF ) superfamily , with a greater affinity for TGF-β RII . Upon ligation FhTLM initiates the Smad2/3 pathway resulting in phenotypic changes in both fibroblasts and macrophages . The formation of fibroblast CFUs is reduced when cells are cultured with FhTLM , as a result of TGF-β RI kinase activity . In parallel the wound closure response of fibroblasts is also delayed in the presence of FhTLM . When stimulated with FhTLM blood monocyte derived macrophages adopt an alternative or regulatory phenotype . They express high levels interleukin ( IL ) -10 and arginase-1 while displaying low levels of IL-12 and nitric oxide . Moreover they also undergo significant upregulation of the inhibitory receptor PD-L1 and the mannose receptor . Use of RNAi demonstrates that this effect is dependent on TGF-β RII and mRNA knock-down leads to a loss of IL-10 and PD-L1 . Finally , we demonstrate that FhTLM aids newly excysted juveniles ( NEJs ) in their evasion of antibody-dependent cell cytotoxicity ( ADCC ) by reducing the NO response of macrophages—again dependent on TGF-β RI kinase . FhTLM displays restricted expression to the F . hepatica gut resident NEJ stages . The altered fibroblast responses would suggest a role for dampened tissue repair responses in facilitating parasite migration . Furthermore , the adoption of a regulatory macrophage phenotype would allow for a reduced effector response targeting juvenile parasites which we demonstrate extends to an abrogation of the ADCC response . Thus suggesting that FhTLM is a stage specific evasion molecule that utilises host cytokine receptors . These findings are the first to clearly demonstrate the interaction of a helminth cytokine with a host receptor complex resulting in immune modifications that facilitate the non-protective chronic immune response which is characteristic of F . hepatica infection .
The trematode Fasciola hepatica is capable of establishing chronic infection in multiple hosts that can last for many years . In terms of animal infections F . hepatica is highly prevalent within sheep , cattle and goats throughout temperate regions of the globe with varying levels of infection reported from 30%-70%[1] . This problem is compounded by a growing degree of resistance against what has been the drug of choice for combating infection , triclabendazole [2] . F . hepatica however is not solely an animal problem as it also has growing implications for human health with large endemic foci of infection within South America and the Middle East [3] . Crucially , there has now been a reported case of triclabendazole resistant parasites causing human infection [4] . As such F . hepatica has been added to the list of emerging zoonotic diseases [5] . In response to infection an extremely polarised T-helper ( Th ) 2 response characterised by high levels of IgG1 , IL-4 and eosinophilia . Despite the magnitude of this response naturally infected animals fail to develop immunity [6] and efforts at experimental vaccination have thus far demonstrated that a mixed Th2/Th1 profile is required to achieve a reduction in parasite burdens , egg outputs and liver damage [7] . As infection progresses the Th2 response switches to a characteristic regulatory response—this is denoted by high levels IL-10 and TGF-β and suppression of antigen specific T-cells [8] . Much research has identified the nature and mode of action of parasite derived immunomodulators . Chief amongst these is the aforementioned cathepsin L1 which has also been shown to suppress Th1 cytokine secretion . Cathepsin L1 has also been shown to cleave CD4 from the surface of lymphocytes [9] and prevent antibody-dependent cell cytotoxicity ( ADCC ) from killing newly excysted juvenile ( NEJ ) parasites [10]; one of the few mechanisms known to kill NEJs . More recently Donnelly and colleagues have shown multiple modes of immunosuppression involving a family of helminth defence molecules , similar to host defence peptides that are capable of suppressing macrophage activation and B-cell cytokine responses [11]; providing evidence parasite for mimicry . Another family of proteins which has demonstrated conservation between host and multiple parasites is the Transforming Growth Factor ( TGF ) superfamily [12] . Members of this protein superfamily have been shown to predominantly play roles in body patterning , optimal sexual development and reproductive success . Members of this family have been found in free-living and parasitic worms including Schistosoma mansoni [13] , S . japonicium [14] , Ancylostoma caninum [15] and Caenorhabditis elegans [16] . We have recently demonstrated that the F . hepatica contains a TGF-like molecule which we termed FhTLM [17] . Previously we have shown that the expression of FhTLM was restricted to the NEJ stage . Further to this we have provided evidence that recombinant FhTLM could enhance motility and survival of NEJs while increasing the rate at which eggs underwent embryonation . Members of the TGF superfamily have been ascribed many roles including within leukocytes . TGF-β is a requirement for the development of both Th17 and Treg cell subsets with the levels IL-6 playing a crucial role in dictating the fate of these cells . TGF-β secreted from Treg cells or other sources can have an anti-proliferative effect on T-cells after TCR stimulation . Macrophage responses to TGF-β are broadly known to result in anti-tumouricidal [18] and anti-inflammatory [19] macrophages . Recently , the effect of TGF-β has been shown to direct the effects of myeloid suppressor cells in Nippostrongylus brasiliensis infection thus controlling Th2 immunity within the lung [20] . TGF-β is also known to be crucial to development of fibrosis , this response can be serve to promote pathology via hypertension during S . mansoni infection [21] . Studies on Heligmosomoides polygyrus suggests that there is a protein ( s ) which can bind the mammalian TGF-receptor complex and initiate a Smad2/3 signalling program [22] . This H . polygyrus derived antigen was found to upregulate FoxP3 expression within naïve T-cells , directly generating Tregs; a finding that explains the protective effect of H . polygyrus in lung inflammation [23] . Given the above and our own findings with regard to FhTLM we sought to determine if FhTLM could directly interact with the native receptor complex and initiate a phenotype . To begin this process we determined if FhTLM could generate a response signal using a luciferase reporter cell line and physically bind host TGF-β RI and RII . FhTLM initiates a Smad3 signal and can alter the responses of fibroblasts in a TGF-β RI kinase dependent fashion . Moreover when used to activate macrophages the response to FhTLM and the resultant phenotype resembled a regulatory macrophage rather than the helminth-linked alternatively activated macrophage; with high levels of IL-10 and PD-L1 and moderate arginase-1 activity . These processes occurred in a tgf-βRII dependent fashion as demonstrated by siRNA knockdown . Finally , the FhTLM macrophage phenotype was incapable of killing NEJ parasites by ADCC demonstrating that this stage specific parasite protein might elicit non-protective responses from resident cells within the intestinal phase of infection .
We have recently shown that the trematode parasite Fasciola hepatica contains three ligand members of the transforming growth factor superfamily [17] . These include two bone morphogenic proteins ( BMPs ) and an activin-like molecule which we have terms F . hepatica TGF-like molecule , FhTLM . We have demonstrated a restricted pattern of expression within the parasite with the highest level of expression within the newly excysted juvenile that emerges within the intestine of hosts . To determine if FhTLM is a bioactive molecule similar to TGF-β we assessed its activity in a reporter assay [24] . A dose response analysis suggests that FhTLM can indeed generate a positive luciferase signal and when compared to a TGF-β1 standard curve it would suggest that FhTLM has a lower degree of bioactivity in this assay when compared with mammalian equivalents ( Fig 1A ) . We were also able to demonstrate a similar activity within crude parasite homogenate ( LFH ) which required higher concentrations to induce comparable responses ( S1 Fig ) . Initial attempts to use a monoclonal antibody to inhibit FhTLM activity did not demonstrate inhibitory capacity . However , a polyclonal anti-sera raised with broad specificity was found to reduce the activity of FhTLM in the same bioassay in a dose dependent manner ( Fig 1B ) . To ensure the effects of FhTLM were dependent on ligand-receptor based interactions we sought to determine if FhTLM could bind either of the bovine TGF-β RI or RII . We cloned and expressed fusion proteins comprised of the bovine TGF-β RI and RII extracellular domain fused to the human IgG1 Fc domain ( S1 Table ) . Using these proteins we performed a comparison between the binding of these fusion proteins to FhTLM and human TGF-β1 ( Fig 1C & 1D –note differences in Y-axis scale ) . Using both fusion proteins we could confirm that human TGF-β1 could bind the bovine receptors RI and RII . More interestingly we also confirmed that FhTLM could cause the binding of both fusion proteins with a greater affinity for TGF-β RII-Fc , which is similar to the reported affinity of TGF-β RII with TGF-β1 elsewhere [25 , 26] . Final confirmation of the greater affinity of TGF-β RII with FhTLM was confirmed by repeating the above assays but with the inclusion of increasing concentrations of KSCN after initial addition of fusion proteins to the plate . A greater concentration of KSCN was required to disassociate the interaction between FhTLM with TGF-β RII-Fc when compared with TGF-β RI-Fc ( Fig 1E ) . Moreover when we tested the affinity of FhTLM for either TGF-β RII-Fc or TGF-β RI-Fc in a competition assay we were able to demonstrate that FhTLM was only moderately able to reduce the binding of free TGF-β RII-Fc or TGF-β RI-Fc to immobilised TGF-β1 reducing binding to TGF-β RII-Fc and TGF-β RI-Fc by 46% and 42% , respectively . In comparison TGF-β was able to reduce binding of FhTLM to TGF-β RII-Fc and TGF-β RII-Fc by 50% and 22% , respectively , as comparable doses ( Fig 1F & 1G ) . The TGF-β receptors are G-protein coupled receptors and after ligand binding heterodimers of TGF-β RI and RII are formed . The resultant phosphorylation of this receptor complex triggers movement of the signalling proteins phosphorylated ( p ) Smad2/3 into the nucleus where gene transcription is initiated [27] . To determine if FhTLM was capable of driving Smad2/3 signalling after engagement with the receptor complex we used primary bovine peripheral blood mononuclear cells ( PBMCs ) in a stimulation assay to measure the extent of co-localisation of pSmad2/3 with the nucleus . Given the differences we noted above between activity of recombinant FhTLM in our luciferase assay and the binding data determined from our receptor fusion protein assays we conducted a dose response curve for bovine macrophages using IL-10 as our readout to determine the optimal working concentration ( S2 Fig ) . PBMCs were stimulated for between 3 and 4hrs , fixed and stained with DAPI ( Fig 2A top row ) , anti-pSmad2/3-FITC ( Fig 2A middle row ) and the percentage of co-localisation ( or pSmad2/3 positive cells ) was determined ( Fig 2A bottom row ) . TGFβ clearly drives pSmad2/3 signalling in this setting with 28% ( ±4 . 1% ) of cells being pSmad2/3 positive at 3hrs post-stimulation with a small , but not significant decrease , at 4hrs to 22 . 4% ( ±5 . 3% ) . Interestingly when we initially examined cells stimulated for 3hrs with FhTLM we found only 10 . 7% ( ±1 . 9% ) of cells positive which was not significantly increased when compared to our controls [6 . 8% ( ±2 . 5% ) ] . However , when we extended our analysis to 4hrs we found that FhTLM induced pSmad2/3 in 15 . 8% ( ±2 . 9% ) of cells which was significantly different when compared to controls [5 . 4% ( ±3 . 4% ) ] . A recent analysis of the bovine il10 promoter within our lab has indicated a role for GATA1 in driving il10 expression . Furthermore GATA1 has been in implicated in anti-helminth immunity in previous studies using Nippostrongylus brasiliensis [28] and S . mansoni [29] . We subjected PBMCs to a 4hr stimulation as above and then determined the levels of GATA1 co-localisation and found that both TGF-β ( 11%±1 . 3% ) and FhTLM ( 6 . 1%±1 . 4 ) could induce significantly more GATA1 co-localisation in PBMCs when compared to controls ( 4 . 9%±1 . 1 ) . Our results clearly demonstrate that FhTLM can act to induce both direct and indirect transcription factors in primary host PBMCs . Characterisation of TGFβ demonstrated a profound an anti-proliferative and developmental effect on multiple cell types [30–35] . In an effort to ascribe a phenotype to the effects of FhTLM we performed CFU assays using the NIH 3T3 fibroblast line . Cells were seeded at a density of 6 cells/petri dish and incubated for 10 days . This was done in the presence of TGF-β or increasing concentrations of FhTLM ( 2 . 5 , 25 , 200 ng/mL ) . CFUs that formed were counted and our results clearly show a significant decrease in number of CFUs that formed when higher doses of FhTLM were used ( Fig 3A ) . 25ng/mL of FhTLM was sufficient to reduce the number of CFUs to comparable level as seen on those incubated with TGF-β ( 128 . 3±6 . 173 vs . 129 . 7 ±19 . 10 ) . To further determine the effects of FhTLM on fibroblast activity we performed in vitro scratch assay/wound closure experiments . Confluent cells were scratched and imaged before incubation for 24hrs with TGF-β or FhTLM . After 24hrs the wounds were imaged and total area determined ( Fig 3B ) . Expressing this area as % wound closure we found that both TGF-β and FhTLM significantly reduced wound closure ( P<0 . 01 ) [Ctrl = 67 . 77%±12 . 17 vs . TGF = 47 . 95±4 . 906 vs . FhTLM = 44 . 47±7 . 235] . Decreased arginase-1 has been previously shown to correlate with delayed wound resolution [36] . We determined the levels of arginase-1 in wounded cultures 24hr after incubation ( Fig 3C ) . We confirmed that in cultures treated with TGFβ or FhTLM the levels of arginase-1 were decreased suggesting an ability of FhTLM in altering arginase-1 activity . We next using chemical inhibition to block the kinase activity of TGF-β RI to determine if the effects of FhTLM are indeed TGF-β dependent and specific . As can be seen in Fig 3B when cells were co-cultured with the inhibitor SB- 431542 [37] during formation of fibroblast CFUs the effect of both TGF-β and FhTLM were abolished . These results suggest that FhTLM can both bind and initiate a specific signal via the TGF-β receptor complex . Having demonstrated a role for FhTLM in modulating cell growth and regulating the arginase levels of these cells we sought to determine if the effects of FhTLM on arginase levels could be extended to macrophages . The classical and alternative pathways for macrophage activity can be broadly defined in terms metabolism of L-arginine either using iNOS or arginase following stimulation with LPS/IFN-γ or IL-4 , respectively [38] . We produced bovine macrophages using purified blood derived CD14+ monocytes; these were then stimulated with IL-4 , LPS , TGF-β or FhTLM . Our initial analysis confirmed that IL-4 and LPS act to induce arginase or NO , respectively ( Fig 4A & 4B ) . While FhTLM can induce a slight increase , similar to that seen in response to induced TGF-β , in the levels of arginase-1 this was not significant when compared with IL-4 but was significantly different when compared with the control . Similarly LPS induced NO but IL-4 , TGF-β or FhTLM induced marginal levels in comparison . To further determine if FhTLM could alter the phenotype of macrophages we measured IL-10 and IL-12 . Only LPS stimulation induced significantly more IL-12 when compared to controls ( Fig 4D ) . However , both IL-4 , TGF-β and FhTLM induced IL-10 , raising levels significantly above controls ( Fig 4C ) . While TGF-β tended towards higher levels of IL-10 induction when compared with L-4 this was not significant . Reports of a regulatory macrophage phenotype in helminth infection or in response to helminth products suggest that this cell population is distinct from alternatively activated macrophages [39 , 40] and in some cases it would appear to be independent of arginase-1 [39] . These reports suggest that upregulation of mannose receptor ( MR ) and PD-L1 serve as proxy markers for these cells . We determined MR levels in stimulated cells by immunofluorescence or PD-L1 levels of qPCR ( Fig 4E & 4F ) . FhTLM significantly upregulated MR expression compared to controls and the number of cells becoming MR+ after stimulation was comparable to IL-4 treatment . However TGF-β , in comparison to both IL-4 and FhTLM , was induced a higher number of MR+ cells [~60% vs 20%] ( Fig 4E ) . When we examined PD-L1 expression only FhTLM and TGF-β were able to induced PD-L1 above levels seen in controls , again with TGF-β inducing higher levels of PD-L1 compared to FhTLM [30 fold change vs 10 fold change] ( Fig 4F ) . To determine what host factor confers specificity on interaction of FhTLM with bovine macrophages we employed siRNA directed against the tgf-βRII . Primary bovine macrophages were as standard , however at point prior to normal cytokine stimulation cells were transfected with target siRNA or with scrambled siRNA , thereafter we measured changes in tgf-βRII levels over a 72hr time period . Our results show we could reliably suppress tgf-βRII mRNA levels up to 24hrs post transfection ( Fig 5A ) . We then proceeded to stimulate macrophages 6 hours after knock-down , with FhTLM or TGF-β . Our results show that absence of tgf-βRII mRNA at the time of cytokine treatment results in a reduced levels of PD-L1 being upregulated in response to both FhTLM and TGF-β with knock-down reducing PD-L1 upregulation by 47% and 90% , respectively ( Fig 5B ) . While in the case of IL-10 induction , measured 54 hours post-transfection and 48hrs post-stimulation , we same similar reductions in the levels of IL-10 in response to FhTLM and TGF-β ( Fig 5C ) . These results strongly support the conclusion that the effects of FhTLM are dependent on the host cytokine receptor complex TGF-β RI and RII , especially when taken in together with our findings above showing that ALK inhibition also negated the effects of FhTLM . We have previously shown that FhTLM is selectively expressed within the newly excysted juvenile ( NEJs ) stage of F . hepatica infection [17] . NEJs are thought to be resident within the intestine for only a number of hours before entering the peritoneal cavity and continuing on their migration to the liver . Within the intestine , multiple type-2 effector responses could be active including antibody-dependent cell cytotoxicity ( ADCC ) . ADCC is one of the few mechanisms shown to actively kill F . hepatica and has previously been shown to target NEJs , both in vitro and in vivo; moreover it has previously been shown to be a target of parasite immune evasion mechanisms [10 , 41 , 42] . We sought to determine if FhTLM altered the macrophage component of this process to benefit parasite survival . Using naïve donor macrophages we incubated cells and NEJs in the presence of either immune or naïve sera . Thereafter viable parasites were counted by visual inspection , as can be seen in Fig 6A the presence of macrophages plus immune sera , but not non-immune sera , resulted in the death of NEJs and was accompanied by induction of NO ( Fig 6B ) . When macrophages were incubated with TGF-β or FhTLM prior to addition of NEJs and sera a different outcome was recorded . As can be seen in Fig 6A both TGF-β and FhTLM reduced the capacity of immune sera to induce ADCC-mediated death of NEJs , this was also accompanied by a loss of NO production ( Fig 6B ) . We next determined if the effect of FhTLM was dependent on the TGF-β RI , via kinase activity , pre-incubation of cells with the inhibitor SB-431542 . Pre-incubation of macrophages with inhibitor prior to TGF-β or FhTLM reversed the negative effect on cells and rescued the ADCC response to NEJs in the presence of immune sera in comparison to cells pre-incubated with vehicle only ( Fig 6C ) . To accompany this we also found that the NO response was restored in both TGF-β and FhTLM cultures in cells pre-incubated with inhibitor but not vehicle only ( Fig 6D ) . Thus our findings demonstrate that FhTLM alters the host macrophage phenotype , via TGF-β RI and RII , to evade ADCC killing of NEJs .
Multiple studies have shown that chronic infection with F . hepatica can be long lived and accompanied by parasite-specific and non-specific immunosuppression [8 , 43 , 44] . As the host progresses from a Th2 type response to a more regulatory response it is assumed that secretion of IL-10 and TGF-β increases as a result of either T-cell phenotypic changes or the expansion of T-regulatory cells . The parallel expansion of both Th2 and Treg populations has previously been demonstrated in S . mansoni [45] and recent work in murine models of F . hepatica have also demonstrated that infected mice generate a FoxP3+ population of cells as infection progresses [46]; however the relevance of this to ruminant immune responses remains to be determined . All of these mechanisms would appear to be driven , or at executed , by the host as a balance to minimise immunopathology . Here we demonstrate that F . hepatica can utilise a host-exogenous cytokine/growth factor , FhTLM—previously shown to have developmental functions , to direct the host immune system . Indeed this mechanism fits with previous patterns identified whereby for optimal host and parasite survival a balance of immune effector mechanisms must be maintained , allowing parasite survival while avoiding immunopathology [47] . Our data demonstrates that FhTLM is capable of directly engaging TGF-β RI and RII in an ELISA format which makes use of fusion proteins of RI and RII . This data helps to explain the activation by FhTLM of the luciferase reporter . Indeed there are prior reports of parasite derived molecules driving activation in this assay system previously [22] . What is apparent from these data is that FhTLM has a ) a higher affinity for TGF-β RII over RI and b ) has a lower binding capacity for either receptor when compared with human TGF-β1; this is further evidenced by our competition data . This effect is seen again in the results of our luciferase assay whereby FhTLM was needed in the ng/mL range to generate signals equivalent to those seen in TGF-β in the pg/mL range . The higher affinity of TGF-β for TGF-β RI and RII is also seen in mammalian systems [25 , 26] and highlights the conservation between the two ligands despite being phylogenetically distinct , demonstrating the close association amongst the parasite and host . To ensure that the immunomodulatory capacity of FhTLM was not due to binding but not initiating signalling from the receptor complex we examined the canonical intracellular signalling molecule Smad2/3 . Using immunofluorescence we can see that not only does FhTLM drive p-Smad2/3 translocation to the nucleus but also does so at slower rate when compared to mammalian TGF-β , again in line with our findings from our binding experiments . It took 4hrs of stimulation with FhTLM to drive a p-Smad2/3 signal distinct from background when compared with the higher translocation rate and shorter time period required in response to TGF-β . TGF-β is pleiotropic in terms of its effects being implicated in developmental processes , anti-proliferative in a context dependent fashion , responsible for fibrosis , and key to differentiation of two distinct CD4+ T-helper subsets . TGF-β is known to be anti-proliferative in terms of fibroblasts [30 , 34 , 35] and we confirm this finding here and also demonstrate that FhTLM can cause a similar response which is also dependent on TGF-β RI kinase activity . FhTLM reduced the number of CFUs formed to a similar rate of TGF-β over a 10 day period . Likewise when included as a growth factor in in vitro wound assays we found that FhTLM , like TGF-β , reduced the rate of wound closure . The role of TGF-β in wound responses is still disputed with some reports finding a positive or negative role dependent on the phase of wound resolution in which it is examined . A recent study however determined that arginase-1 was crucial for healing in murine model wounding [36] and here we found that in parallel with reducing wound closure FhTLM also reduced the cellular levels of arginase-1 . Campbell et al [36] found the reduction in arginase-1 levels also resulted in a reduction in pro-inflammatory cell recruitment , including macrophages . The benefits in delayed wound healing during a parasite infection are not apparent however given the migratory nature of F . hepatica infection , it could be speculated that reducing the rate at which wounds or migratory paths caused by the parasite are healed may confer a benefit to the parasite . The parasite excysts within the intestine and migrates into the peritoneal cavity where it gains access to liver before moving to bile ducts [48] . As the parasites migrate through the intestine they formed a cavity around themselves which would require healing post migration . The delay in healing may increase the rate of successful migrating NEJs; it is already knwon that NEJs secrete proteases to digest surrounding tissue to facilitate their movement [49] . Given the context specific effects of TGF-β we sought to determine its effects on other cell types . Studies suggest that helminth infection [39] and a recombinant helminth immunomodulator [40] can induce a macrophage phenotype that is distinct from the alternatively activated phenotype that is normally associated with helminth infection [50] . Our data demonstrated a subtle yet significant rise in arginase-1 levels following FhTLM , in contrast to our results in the fibroblast experiments , and no increase in NO and in comparison to the strongly polarising effect that IL-4 has on these readouts the results were not striking and more akin to the response to TGF-β . This pattern concurs with the findings of Smith et al . , [39] who found a helminth elicited macrophage population could protect from colitis but in an arginase-1 dependent manner . We next examined the cytokine profile , IL-10 and IL-12 , of these cells we found a more pronounced effect of FhTLM . FhTLM could upregulate IL-10 while also suppressing the expression of IL-12 , this has been a reported feature of regulatory macrophages for some time [51]; again this pattern of responses being more similar to TGF-β than IL-4 . Moreover mRNA expression of PD-L1 and the number of mannose receptor positive cells were significantly upregulated in FhTLM or TGF-β treated macrophages only . During infection with Taenia crassiceps PD-L1 has been shown to suppress T-cell responses and neutralisation of PD-L1 on macrophages from infected mice abrogated their suppressive capacity [52] . Recently , an Ancanthocheilonema viteae derived immunomodulatory was shown to induce macrophages with high levels of expression of IL-10 and PD-L1 capable of reducing signs of colitis in mice after cell transfer [40] . The mannose receptor ( CD206 ) has previously been shown to be upregulated on regulatory macrophages from a variety of settings [53 , 54] including controlling their role in regulating inflammatory cytokine release in Pneumocystis infection [55] and endotoxin-induced lung injury [56] . We found the effects of FhTLM on PD-L1 and IL-10 to be dependent on tgf-βRII expression , as use of siRNA resulted in a loss of their expression following stimulation . Given the effects of FhTLM on macrophages and the restricted expression of FhTLM to NEJs within the parasite itself; we sought to determine the effects of FhTLM on a NEJ targeting effector mechanism—ADCC . ADCC has been shown to kill NEJs when using cells from cattle [41] , rats [42] , and mice [10] but not sheep [57] . This is thought to be as a result of a lack of NO generation in sheep macrophages . Here we demonstrate that bovine macrophages , in the presence of immune sera , kill NEJs and release NO into the supernatant . Moreover the culture of macrophages with FhTLM or TGF-β prior to this assay effectively removed the killing phenotype and reduced the levels of NO . When these assays were repeated in the presence of the TGF-β RI kinase inhibitor the ADCC effect was rescued and parasites were rendered non-viable and NO levels were restored , again showing the effects of FhTLM to be TGF-β receptor dependent . The expression of TGF-β homologues within helminth parasites has been previously identified [58] however this , to our knowledge , is the first full description of the suppressive effect of a recombinant helminth TGF-β homologue on its host immune system . Our findings indicate a role for FhTLM in the modulation of host macrophages to avoid a well-recognised mechanism of killing ADCC . The complete function of FhTLM during infection has yet to be explored but the on-going development of stable gene silencing techniques in F . hepatica will make this achievable [59] . A loss-of-function approach would be the best method to approach this subject , however there exists a number of hurdles , metacercariae have yet to be successfully treated with RNAi and as such NEJs treated with RNAi would need to be transplanted into the intestines of suitable hosts . The macrophage response to surgery has been shown to tend towards alternative activation , thus attempting to analyse the immune phenotype in such circumstances may prove difficult . A second complicating factor are the parasite-intrinsic effects of FhTLM [17] , knock-out of FhTLM may yield a near-lethal or lethal phenotype , for reasons unrelated to host immunity , again complicating the analysis . A system for conditional gene targeting within the parasite metacercariae would best allow for natural infection and thus a faithful analysis of the resulting immune response; however these tools do not yet exist . Implementation of this technology will aid us in answering unresolved questions surrounding exact timing of FhTLM expression within the intestine of hosts , the full range of target cells and whether the effects of FhTLM are confined both physically and temporally confined to the intestine .
We have previously described the cloning and expression of FhTLM [17] . A pET28-based construct ( Novagen ) was used to express a 6XHis-Tagged protein in BL21 E . coli ( Novagen ) using kanamycin and chloramphenicol to select for transformed bacteria . Recombinant protein was purified using a Nickel resin column ( Sigma-Aldrich ) . Recombinant proteins were subject to two rounds of phase separation prior to use [60] . To generate the receptor-fusion proteins the bovine TGFβRI extracellular domain sequence from nucleotide ( nt ) +88 to +331 and TGFβRII extracellular domain sequence from nt +139 to +453 relative to the translation initiation site ( +1 ) were PCR amplified using specific primers . Using a forward primer with a NCOI restriction site incorporated and reverse primer with a Bg1II restriction site incorporated for TGFβRI and forward primer with an EcoRI restriction site incorporated and reverse primer with a Bg1II restriction site incorporated for generation of TGFβRII ( See S1 Table ) . The amplified extracellular domains of TGFβRI was sub-cloned into the NCOI and Bg1II and TGFβRII ED into EcoR1 and Bg1II multi cloning site of the pFUSE-hIgG1-Fc2 vector ( InvivoGen , UK ) respectively . Following ligation and confirmation of insertion plasmids were used to chemically transform E . coli DH5α cells . The transformed cells were grown on LB agar plates supplemented with 50μg/ml Zeocine ( InvivoGen , UK ) at 37°C overnight . Plasmid was purified and used to transfect mammalian HEK-293 cells ( Invivogen UK ) maintained in DMEM ( Sigma Aldrich ) supplemented with 10% FCS ( Sigma Aldrich ) , 100 μg/ml penicillin , 100 μg/ml streptomycin and grown to 80% confluency . Transfection with recombinant plasmids was carried out using jetPRIME DNA and siRNA transfection reagents ( Polyplus-transfection , USA ) as per manufacturer’s instructions . The day before transfection cells were seeded into 6 well culture plate at 2x105 cells /well , DMEM medium were added to final volume of 2ml per well and incubated at 37°C overnight 5% CO2 . 2 μg of plasmid was diluted into 200 μl of jetPRIME buffer and 4 μl of jetPRIME were added to each well for transfection . The transfection medium were replaced with complete DMEM medium after 4 hrs and incubated at 37°C for 72 hrs . A positive control GFP reporter plasmid , Pc-gfp-c2 ( Clontech ) , was used in parallel to confirm transformation . After 72 hrs the positive controls was assessed under an inverted microscope ( LEICA DMIL ) . Positive cells were cloned under limiting dilution conditions , using zeocine ( InvivoGen ) as a selective . 96-well plates was coated with 50 μl of hTGF-β1 or FhTLM , concentration indicated on figures , at room temperature overnight . The plate was washed three times with 0 . 05% Tween/PBS . Additional protein binding site were blocked by adding 200 μl of 4% BSA-PBS and incubatedfor 1 hr at room temperature . TGFβ-RI and RII Fc fusion proteins were used at 1 . 25μg/mL and supernatant from non-transfected HEK were used as negative control and added to the wells of the plate and incubated for 1 hr at room temperature . HRP-conjugated Anti-human IgG1 Ab ( HP6070 , Life Technology ) at a concentration of 5 μg/ml . Colour was developed with TMB substrate and the reaction was stopped with 1% HCL; absorbance was measured at 450nm using micro-plate reader ( LT-4000 , Labtech , UK ) . To estimate the avidity of the FhTLM interaction with Fc fusion proteins of the bTGFβ-RIED and RIIED , potassium thiocyanate ( KSCN ) was introduced into the ELISA to disrupt the binding between the bovine receptors and the recombinant FhTLM protein . ELISA was performed as stated above using FhTLM as coating antigen at concentration of ( 500 ng/ml ) . After addition and incubation with ( 1 . 25 μg/ml ) of Fc fusion proteins , different concentrations ( 1 , 2 , 3 , 4 , 5 and 0 M ) of KSCN were added to each well and incubated for 1 hr at RT . Thereafter the binding and optical densities were measured as above . To determine the extent of competition between FhTLM and TGF-β in the context of binding to receptor fusion proteins , FhTLM or TGF-β were coated on plates at 400ng/mL . After this the opposite increasing concentrations of competing protein were incubated with the Receptor-Fc fusion in solution at 37°C for 1hr , then added to the plate and the ELISA proceeded as above . Mink Lung epithelial cells ( MLECs—a gift from Prof D Rifkin , New York University ) were maintained in T25 flask ( Sarstedt ) containing 5 ml of Dulbecco’s modified Eagle’s Media ( DMEM ) ( Sigma-Aldrich ) supplemented with 10% of heat inactivated fetal calf serum ( Sigma-Aldrich ) , penicillin ( 100 U/ml ) , streptomycin ( 100 U/ml ) , L-glutamine and 200 μg/ml , G418 ( Sigma-Aldrich ) . For use in luciferase measurements cells were used at a concentration of 1 . 6x106 cells/ml . The suspension was plated in 96 well tissue culture plates ( Sarstedt ) 100 μl/well . The culture plate was incubated at 37°C , 5% CO2 overnight for optimal cell attachment . Proteins including a TGF-β standard curve were added to cells in DMEM with 0 . 1% BSA . Luciferase was measured the luciferase Assay ( Promega ) on a BMG luminometer as per Abe et al [24] . Whole blood was collected under terminal exsanguination from healthy donor animals under a Home Office regulated schedule 1 procedure . CD14+ cells were isolated and cultured as before [61] IL-4 was used at 20ng/mL , LPS was used at 100μg/mL while FhTLM was either used at the indicated dose or 200ng/mL . Cells were stimulated for 6hr for RNA isolation or 48hrs for collection of supernatants and cell lysates . RNAi knock-down of tgfβRII ( NM_001159566 . 1 ) was conducted using the methods of Jensen et al [62] Briefly , siRNA oligos were designed by Sigma Aldrich and used at a final concentration of 100nM . Macrophages were cultured to maturity in 48 well plates at a density of 1 x 105 cells and after 10 days were transfected with JetPrime media was replaced after 24hrs . Cells were tested for knock-down beginning at a further 24hrs after media change this using PCR primers designed against tgfβRII;Forward primer 5’ -GGACTATGAGCCTCCGTTCG- 3’ and reverse primer 5’–GGTTCCAGGAAGCATCGTCA- 3’ . Alternatively , once an optimum time post-transfection was selected– 12hrs—stimulation was conducted . The fibroblast cell line NIH 3T3 ( A gift from Dr Janet Daly University of Nottingham ) was routinely maintained and to conduct the scratch assay published methods were used . Briefly , 3x105 cells were seeded into 6 well plates and incubated overnight . To scratch the monolayer , a linear scratch was made to the fibroblasts from the top of the well to the bottom using a 20μl pipette tip at time 0; plates were then incubated with the indicated proteins for 24hrs . Images of scratches were obtained using an inverted light microscope set to 5 x magnification . Lecia imaging software ( leicra microsystems LTB Milton Keynes UK ) was used to acquire digital images . ImageJ , was used to analyse the images ( version 1 . 49v from National Institutes of Health , USA ) . Scratch areas were measured and compared by a blinded operator independent to the culture treatments for each well . To conduct the CFU assay 6 cells/well were seeded in a 6-well plate , thereafter proteins were added and plates incubated for 10 days . Plates were stained with 0 . 5% crystal violet and imaged as above . CFUs were determined per well by an operator blinded to treatments before data analysis . In some experiments cells were co-cultured with SB-431542 a TGF-β RI kinase inhibitor ( Tocris ) and TGF-β or FhTLM with inhibitor at a final concentration of 5μM . Inhibitor stocks were prepared in DMSO and vehicle controls were prepared using an appropriate comparative dilution of DMSO . For immunofluorescence staining cells were isolated and stimulated as above but grown on coverslips ( Corning ) . Following stimulation plates were centrifuged at 300xg for 10 min following incubation , to collect cells on the cover slips . Medium were removed and cells were washed three times with 1XPBS . PBMCs were then fixed by in 4% paraformaldehyde for 15 min at room temperature and washed with 1XPBS . Afterward , cells were permeabilized for 10 min at room temperature with 0 . 5% Triton X-100 in PBS and washed with PBS . Cells were incubated for 1 hr at room temperature with a 1:100 dilution of primary polyconal rabbit anti-GATA1 ( Santa Cruz sc-13053 ) or goat anti-pSmad2/3 ( Santa Cruz sc-11769 ) or mouse anti-mannose receptor ( ThermoFisher 2G11 ) . After extensive washing with PBS , the cells were incubated for 1 h at room temperature in the dark with 1:1000 dilution of secondary anti-IgG-FITC . Slides were then washed with PBS , mounted with Vectashield ( Vector Labs Ltd . , UK ) containing DAPI staining reagent . Images were captured using Leica DM500B microscope and DFC 350FX camera ( Leica Microsystem Ltd . , UK ) using X40 and X63 magnification . ELISAs or paired antibodies were used to detect cytokines were conducted as per manufacturer’s instructions the kits were as follows; IL-10 ( CSB-E12917B ) was purchased from Cusabio; IFN-γ ( ESS0026B ) from ThermoScientific; and IL-12 paired antibodies ( CC301 –capture and CC326 –detection ) were from AbD Serotec . Nitric oxide was measured using a Griess Reagent Kit ( Promega ) and arginase levels were determined using the method of [63] . To measure PD-L1 via qPCR RNA was isolated from cells 6hrs after stimulation and converted to cDNA using the GoScript Reverse Transcription Kit ( Promega ) . Primers and conditions used are as previously reported [64] . F . hepatica metacercariare were obtained from the University of Liverpool clonal strain FhepLiv and NEJs excysted as previously described [17] . Parasites were rested for 4hrs prior to use in the ADCC assay which was conducted as previously described by Piedrafita et al [57] and Van Milligen et al [65] . Briefly , macrophages were cultured as described above and mixed with rested NEJs in wells of a 48 well plate containing 40 NEJs and 2 x 105 macrophages per well . Serum was collected from three cattle prior to infection and 13 weeks post infection , with 250 metacercariae of F . hepatica ( Kind gift from Dr Divya Sachdev University of Nottingham ) . Sera was added to wells at a final concentration of 15% in a total volume of 250μl/well . Plates were incubated for 48hrs and NEJs were observed for viability by monitoring motility , absence of defined intestinal structures and exclusion of trypan blue . Parasites were only classified as non-viable if motility and intestinal structure were absent/not visible and trypan blue was taken up in the tegument . NO was measured in the same supernatant using the methods above . In some experiments , macrophages were pre-incubated with the inhibitor SB-431542 prior to FhTLM or TGF-β stimulation as described above . Data was entered into Prism 6 . 01 ( Graphpad ) for statistical analysis . Data was analysed using a 1-way Anova with post-test comparison using a Tukey correction . Apart from data in Fig 6 which was analysed using 2-way anova to determine the effect of serum type and macrophage stimulation . P values <0 . 05 were taken as significant and individual P values are listed in figure legends .
|
Parasitic worms , helminths , can cause long-lived chronic infection in many hosts that they infection . The liver fluke , Fasciola hepatica , is one such parasite causing global infection of both humans and animals . F . hepatica exerts an influence over the immune system such that it avoids effector mechanisms and prevents the development of effective immunity . Here we characterise a molecule—FhTLM—derived from juvenile parasites that is similar to the regulatory cytokine TGF-β . We show that FhTLM will bind to host TGF-β receptors with a reduced affinity when compared with mammalian TGF-β . Despite this FhTLM can induce Smad2/3 signalling in host leukocytes , which is key to initiating gene transcription . Phenotypically FhTLM causes fibroblasts to slow their growth and replication response resulting in slower wound healing . Importantly FhTLM induces a macrophage phenotype that resembles a regulatory macrophage phenotype identified in other species undergoing helminth infection . Finally we Our work highlights the potential of FhTLM to play important roles in controlling host immunity when initially infected with juvenile parasites , thereby preventing the development of effective immunity .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
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] |
2016
|
A Trematode Parasite Derived Growth Factor Binds and Exerts Influences on Host Immune Functions via Host Cytokine Receptor Complexes
|
A large fraction of human genes are regulated by genetic variation near the transcribed sequence ( cis-eQTL , expression quantitative trait locus ) , and many cis-eQTLs have implications for human disease . Less is known regarding the effects of genetic variation on expression of distant genes ( trans-eQTLs ) and their biological mechanisms . In this work , we use genome-wide data on SNPs and array-based expression measures from mononuclear cells obtained from a population-based cohort of 1 , 799 Bangladeshi individuals to characterize cis- and trans-eQTLs and determine if observed trans-eQTL associations are mediated by expression of transcripts in cis with the SNPs showing trans-association , using Sobel tests of mediation . We observed 434 independent trans-eQTL associations at a false-discovery rate of 0 . 05 , and 189 of these trans-eQTLs were also cis-eQTLs ( enrichment P<0 . 0001 ) . Among these 189 trans-eQTL associations , 39 were significantly attenuated after adjusting for a cis-mediator based on Sobel P<10-5 . We attempted to replicate 21 of these mediation signals in two European cohorts , and while only 7 trans-eQTL associations were present in one or both cohorts , 6 showed evidence of cis-mediation . Analyses of simulated data show that complete mediation will be observed as partial mediation in the presence of mediator measurement error or imperfect LD between measured and causal variants . Our data demonstrates that trans-associations can become significantly stronger or switch directions after adjusting for a potential mediator . Using simulated data , we demonstrate that this phenomenon is expected in the presence of strong cis-trans confounding and when the measured cis-transcript is correlated with the true ( unmeasured ) mediator . In conclusion , by applying mediation analysis to eQTL data , we show that a substantial fraction of observed trans-eQTL associations can be explained by cis-mediation . Future studies should focus on understanding the mechanisms underlying widespread cis-mediation and their relevance to disease biology , as well as using mediation analysis to improve eQTL discovery .
The development of technologies that enable high-throughput , genome-wide measurement of single nucleotide polymorphisms ( SNPs ) and mRNA transcripts have enabled researchers to comprehensively examine the effects of human genetic variation on gene expression . Genome-wide studies of expression quantitative trait loci ( eQTLs ) have been conducted using a wide-array of RNA sources , including lymphoblastoid cells lines [1]–[4] , whole blood [5] , monocytes [6] , [7] , B-cells [7] , liver cells [8] , [9] , and breast cancer tumor cells [10] . These studies consistently demonstrate that a large fraction of human genes ( perhaps all genes ) are regulated by variants near the transcribed sequence , typically referred to as cis-eQTLs ( or cis-eSNPs ) . Less is known regarding the effects of genetic variation on expression of distant genes and genes residing on other chromosomes ( i . e . , trans-eQTLs ) . Identifying trans-eQTLs should provide insight into the mechanisms of gene regulation , including mechanisms relevant to disease-associated variants and human disease biology . Trans-eQTLs are more difficult to identify than cis-eQTLs because trans effects are generally weaker than cis effects and because a huge number of tests must be conducted to comprehensively search the genome for trans-eQTLs , resulting in the use of stringent significance thresholds . Thus , large studies are needed for trans-eQTL identification . Several such studies ( >1 , 000 participants ) have focused on identifying trans- eQTLs , and these have typically used white blood cells as an RNA source [5] , [6] , [11] . In early trans-eQTL studies , the proportion of trans-eQTLs replicated across studies was quite low , much lower than cis-eQTLs [9] , but a recent study demonstrates that most trans-eQTLs replicate when very large sample sizes are used . [11] While the biological mechanisms underlying trans-eQTLs are largely unknown , it is likely that many trans-eQTLs are also cis-eQTLs , and it is the cis-transcript that affects the expression of a trans-gene; however , no prior trans-eQTL studies have systematically assessed evidence for cis-mediation among identified trans-eQTLs . While replication in independent samples is the gold standard method for validating trans-eQTLs , we propose that documenting cis-mediation can provide additional evidence that an observed trans-association is a true trans-eQTL and a potential biological explanation/mechanism for the observed trans-eQTL . In this work , we describe cis- and trans-eQTL associations using data on genome-wide SNPs and genome-wide RNA transcripts ( extracted from mononuclear cells ) for 1 , 799 Bangladeshi adults . For SNPs observed to be trans-eQTLs , we use a mediation analysis approach to assess evidence that the observed trans-eQTL associations are mediated by measured transcripts that are in cis with the SNP showing a trans-association . We observe evidence of mediation for a substantial fraction of the trans-eQTLs observed in our data and replicate several of our mediation signals in an independent sample , suggesting that many observed trans-eQTLs are due to mediation by expression levels of cis-transcripts in the vicinity of the trans-eQTL . These observations can be used to enhance our understanding of regulatory mechanisms and our ability to identify trans-eQTLs .
We conducted genome-wide cis-eQTL analysis using data on 1 , 016 , 489 genotyped and imputed SNPs and 22 , 973 expression probes ( corresponding to 16 , 006 genes ) measured for 1 , 799 Bangladeshi individuals , using DNA extracted from whole blood and RNA extracted from peripheral blood mononuclear cells ( PBMCs ) . For both SNP and probe data , stringent quality control ( QC ) measures were implemented to eliminate false positive associations ( see methods ) . Results of genome-wide eQTL analyses are summarized in Table 1 . At a genome-wide false-discovery rate ( FDR ) of 0 . 05 ( P<2 . 2×10−3 ) , we observed that 15 , 570 out of 22 , 973 expression probes ( 68% ) and 11 , 827 out of 16 , 006 unique genes ( 74% ) show evidence of a cis-eQTL in this population . In the genome-wide trans-eQTL analysis using an FDR of 0 . 05 ( P<8 . 4×10−9 ) , we observed 427 significant expression probes , corresponding to 414 unique genes ( Table 1 ) . Among these probes , there were 434 unique eQTL associations ( i . e . , unique probe and unique trans-eQTL region ) , corresponding to 419 unique trans-eQTL associations at the gene-level ( Table S1 and Figure S1 ) . There were 26 examples of a single variant ( or variants in strong LD ) showing association with multiple unique trans genes and 11 examples of a gene affect by multiple trans-eSNPs located in different regions of the genome . We observe many trans-eQTLs reported in prior studies , including the monocyte-specific master regulator at the LYZ locus on chromosome 12 identified by Fairfax ( rs10784774 ) [7] , the multiple trans-effects of variation type 1 diabetes region 12q13 . 2 described by Fehrmann and Fairfax [5] , [7] , and the lupus SNP rs7917014 ( tagged by rs4917014 in our data ) association with CLEC4C , CLEC10A , IFIT1 , and other genes highlighted by Westra [11] . Consistent with findings from previous studies [5] , [11] , [12] , SNPs known to be associated with human traits ( 1 , 930 unlinked SNPs with P<5×10−8 in the NHGRI GWAS catalog ) were more likely to show association with local gene expression ( P = 2 . 8×10−42 ) ( Table 1 and Figure S2 ) . Similarly , we observed that trait-associated SNPs are more likely to be trans-eQTLs ( enrichment P = 1 . 6×10−12 ) , with 67 trait-associated SNPs ( ∼3 . 5% ) showing strong evidence of trans-association ( Table 1 and Figure S3 ) . In addition , we show that trans-eSNPs are more likely to be cis-eSNPs than randomly-selected SNPs ( enrichment P<0 . 001 , consistent with Westra et al . [11] , Figure S4 ) , with ∼45% of lead trans-eSNPs identified as cis-eSNPs , suggesting that the effect of many trans-eQTL effects may be mediated by measured transcripts regulated in cis by the causal trans-eSNP . At trans-eQTL P-value thresholds of 10−15 , 8 . 4×10−9 , 10−7 , the percentages of trans-eQTLs ( lead SNPs ) that were also associated with cis-transcripts were 62% ( 45 out of 73 ) , 44% ( 189/434 ) , and 31% ( 781/2 , 536 ) , respectively ( Table 2 ) . Genes that lie <30kb from lead trans-eSNPs are more likely to be associated with SNPs in cis ( 76 . 2% of probes ) as compared to all transcripts ( 67 . 8% of probes ) ( P = 1 . 6×10−4 ) . Among the 434 unique probe-level trans-eQTL associations , there were 189 for which the strongest associated SNP ( i . e . , “lead trans-eSNP” ) was also associated with at least one local cis-transcript ( based on genome-wide significance in the cis-eQTL analysis ) , representing a potential mediator of the trans-eQTL association ( Fig . 1A ) . For these 189 trans-eQTL associations we performed mediation analysis ( see methods ) and obtained Sobel P-values for mediation as well as an estimate of the proportion of the trans-eQTL effect that is mediated by a cis-transcript ( i . e . , the proportion reduction in the magnitude of the beta coefficient after adjusting for the potential mediator: ( β - βadj ) /β ) . These are plotted in Fig . 2A , which shows an excess of “mediation proportion” estimates that are greater than zero . At a Sobel P threshold of 10−5 ( based on visual inspection of Fig . 2A ) 39 trans-eQTL associations ( 21% ) were significantly reduced in magnitude after adjusting for a cis-transcript , suggesting that the measured transcript is the primary mediator of the trans-eQTL effect ( Table S2 ) . Extending our analysis to the 592 trans-eQTL associations that did not pass the FDR threshold , but had a P<10−7 and a potential cis-mediator , we observed evidence of mediation for seven additional trans-eQTLs with Sobel P <10−16 and one trans-eQTL that changed direction after adjustment for the cis-transcript ( Fig . 2B and Table S2 ) , suggesting that mediation analysis can be used to enhance trans-eQTL discovery . Evidence for cis-mediation was stronger when LD between the lead trans-SNP and the lead cis-eSNP was high ( Table 2 and Fig . 2 ) , as expected under the mediation hypothesis , which implies the observed cis- and trans-eQTL associations are due to the same causal variant ( Fig . 1B ) . Evidence for cis-mediation was also stronger for trans-eQTLs with smaller P-values ( Table 2 ) , suggesting that mediation is a characteristic of true positives and that power for detecting mediation is higher for stronger trans-eQTLs . Similarly , at less stringent thresholds , a smaller percentage of trans-eQTL variants show association with a cis-transcript ( Table 2 ) , suggesting that observed trans-eSNPs that are not associated with a nearby transcript are , on average , less likely to be true trans-eQTLs . Among the 39 trans-eQTL associations showing strong evidence of mediation ( Table S2 ) , seven trans-eQTLs were present more than once , represented by cis-mediators AK125871 , GNLY , GATA2 , TREML1 , FCN1 , RPS26 , and RBPMS2 . The trans-eSNPs showing mediation by GATA2 ( 3q21 . 3 ) , FCN1 ( 9q34 . 3 ) , and RPS26 ( 12q13 . 2 ) are also associated with white blood cell subtypes [13] , systemic inflammation ( and FCN1 protein activity ) [14] , and type 1 diabetes risk [15] , respectively . All four trans-eQTL associations observed for the FCN1 region ( represented by rs10120023 ) were substantially reduced after adjustment for FCN1 expression ( ILMN_1668063 ) ( Fig . 3 ) . Similar results for GATA2 and RPS26 are shown in Figure S5 . Among the 245 trans-eQTL associations for which no potential mediator was identified in the cis-eQTL analysis , we selected the probe with the strongest association to the lead trans-eSNP and conducted mediation analysis . However , little evidence of mediation was observed ( Figure S6 ) . Evidence for mediation is often observed as “partial” mediation , as the trans-eQTL association is not completely eliminated after adjustment for a cis-transcript . To aid our interpretation of partial mediation , we simulated cis- and trans-eQTL expression data based on real genotype data assuming complete mediation ( see Methods ) . Our simulations show that evidence for mediation ( in terms of the “proportion mediated” and the Sobel P ) decreases as LD between the causal variant and the measured variant decreases and as measurement error increases ( measured as the correlation between the true mediator and our measurement of the mediator ) ( Fig . 4 ) . In other words , even when trans-eQTL associations are fully mediated by a cis-transcript , evidence for mediation will be detected as “partial mediation” when there is measurement error for the mediating probe and/or imperfect LD between the causal variant and the measured variant under analysis . In our mediation analysis , the estimate of the mediation proportion is less than zero , and occasionally greater than 1 ( Fig . 2 ) , a somewhat counter-intuitive finding that suggests the presence of bias . One potential source of bias is “mediator-outcome” confounding [16] , which occurs when an unobserved variable ( or set of variables ) affects both the cis-mediator and the trans-transcript . In this scenario , the estimate of association between the SNP and the trans-gene when adjusting for the potential mediator ( i . e . , the “direct effect” of the SNP on the trans-gene , βadj ) will be biased . When cis-trans confounding is absent , the direct effect under full mediation should be zero ( βadj = 0; percent mediation = 100% ) . Using simulated data , we demonstrate the effect of this bias on the estimate of proportion of the trans-eQTL effect that is mediated ( β - βadj ) /β ) ( Fig . 5 ) . This bias can go in either direction , depending on the directions of the effects of the confounder with the cis-mediator , the confounder on the trans-transcript , and the non-reference allele ( in our case , the minor allele ) on the cis-transcript ( Figure S7 ) . The magnitude of the bias depends on the strength of confounding and the effect of the cis-gene on the trans-gene . Thus , there is no expectation regarding the direction in which this bias should affect the estimate of βadj . Exceptionally strong bias has the potential to qualitatively change the results of mediation analysis , such large changes in the direction or substantial strengthening of a trans-eQTL association after adjustment for a potential cis-mediator , but we observe very few examples of these phenomena in our data ( see below ) . In addition to cis-trans confounding , bias can arise when the analyzed cis-transcript is not the true mediator , but is correlated with the true mediator . More specifically , evidence for mediation will be observed if the transcript used for mediation analysis is influenced by a cis-variant that is in LD with the causal trans-eSNP and is correlated with the true mediator , due either to confounding ( due to an unobserved transcript or factor ) or a causal relationship between the analyzed transcript and the true mediator ( Figure S8 and Figure S9 ) . When the causal relationships shown in Figure S8B and Figure S8C are positive effects ( producing positive correlations ) , and the LD between the expression-increasing alleles is positive , the adjusting for the selected transcript will attenuate the trans-eQTL association . However , when both positive and negative relationships are present , adjusting for the selected transcript can increase the magnitude of the trans-association ( Figure S9A and Figure S9B ) . Thus , even when the true mediator is not measured , it is still possible to obtain indirect evidence of that a trans-eQTLs is attributable to cis-mediation . In contrast , when an unobserved variable influences both the selected cis-transcript and the trans-gene ( Figure S8D ) , evidence of mediation can be falsely detected , and similar to cis-trans confounding , the estimate can be biased in either direction ( Figure S9C ) , depending on the direction of the effects of the confounder and the positivity/negativity of the LD between expression-increasing alleles . We observe several instances in which adjusting for a potentially-mediating transcript substantially strengthened or reversed the direction of the trans-association ( Sobel P<10−5 ) ( Table S3 ) . As noted in the above sections , this estimate could potentially be biased due to exceptionally strong cis-trans confounding . However , additional causal diagrams that are consistent with this phenomenon are shown in Figure S10 . In the first scenario , a causal trans-eQTL variant affects a trans-gene though multiple cis-mediators . In the second , two causal trans-eQTL SNPs are in LD , and each affects the same trans-gene , through two different cis-mediators . In order to determine if these proposed scenarios potentially explain our two most striking examples of these phenomena , we regressed the trans-gene on the trans-eSNP , adjusting for all measured cis-transcripts correlated with the trans-eSNP ( Table S3 ) . For these trans-eQTLs , adjusting for additional transcripts did not substantially attenuate the trans-association , suggesting that these “direction changes” are due to unmeasured mediators or unobserved confounding variables . For>140 of our 189 significant trans-/cis-eSNPs ( Fig . 2A ) , the Sobel P is>10−5 and the “mediation proportion” is distributed around zero . While it is difficult to identify specific examples of true mediation among this group , we hypothesize that non-uniformity of the Sobel P-value distribution in this range is likely due to a mixture of true mediation , bias due to confounding of the cis-trans association , and correlation between the true ( unmeasured ) mediator and the probe selected for mediation analysis . These phenomena are described in detail in the sections above , and the latter two phenomena can result in both positive and negative bias for the estimate of the “mediation proportion” . Among our observed trans-eQTLs , those that do not show mediation are , on average , further from transcription start sites or end sites ( TSS or TES ) as compared to those that show mediation ( Table S4 ) , suggesting that some trans-eQTLs do not influence the expression of nearby protein-coding genes . When considering only trans-eSNPs that lie <30 kb from a TSS or TES , those that do not show mediation are more likely to lie near a gene for which we do not have expression data ( Table S4 ) , suggesting that a substantial number of the true mediators for these trans-eQTLs are not represented by probes in our dataset . Trans-eSNPs showing no mediation were somewhat more likely to tag ( r2>0 . 7 ) non-synonymous SNPs than non-mediated trans-eSNPs , indicating that coding changes may mediate the effects of trans-eQTLs for which we could not identify clear mediators ( Table S4 ) . A similar pattern was not observed for splice-modifying SNPs . A total of 43 of the 419 ( 10% ) unique gene-level trans-eQTLs observed among our Bangladeshi participants ( FDR of 0 . 05 ) have been observed in prior trans-eQTL studies using RNA from blood cells ( peripheral or transformed ) from participants of primarily European ancestry [5] , [7] , [11] , [17] . Among the 2 , 493 trans-eQTLs with P<10−7 , 59 have been observed in prior studies ( Table S5 ) . Probability of replication depended strongly on P-value , with 27% of our findings with P<10−15 replicating , 7% with P between 8 . 4×10−9 and 10−15 , and 1% with P between 10−7 and 8 . 4×10−9 . For trans-eQTLs passing the FDR threshold , there was not strong evidence that those showing evidence of mediation were more likely to replicate than those that did not show evidence of mediation , however , for trans-eQTLs not passing the FDR threshold ( P>8 . 4×10−9 but P<10−7 ) , a higher percentage of replication was observed among mediated trans-eQTLs , although there were only 16 of mediation among this group ( Table S6 ) . Using data from two independent cohorts of European Ancestry , the Groningen ( n = 1 , 240 ) and Estonian EGCUT ( n = 891 ) cohorts [11] , we attempted to replicate our mediation signals . In these cohorts , complete data on the lead SNP , cis-probe , and trans-probe ( based on RNA from whole blood ) were available for 21 of the mediated trans-eQTL associations , and only one of these showed strong evidence of being a trans-eQTL in both cohorts . For this eQTL ( rs6785206 , associated with GATA2 in cis and CLC in trans ) , we observed evidence of mediation in both cohorts , with the trans-eQTL association reduced in magnitude by 21% and 38% , respectively , after adjusting for the cis-mediator . Six additional trans-eQTL associations were replicated in one or the two cohorts , and we observed evidence consistent with cis-mediation for five of the six ( Table S7 ) .
In this work , we have conducted a comprehensive analysis of cis-mediation underlying trans-eQTLs using data from the first large genome-wide eQTL study of South Asian individuals . Approximately 44% of all trans-eQTLs detected at an FDR of 0 . 05 also showed evidence of being a cis-eQTL , enabling analysis potential mediation by cis-transcripts . Among analyzed trans-eQTLs , ∼21% showed strong evidence of mediation by a measured cis-transcript . Analysis of simulated data demonstrated that partial rather than complete mediation will be detected in the presence of ( 1 ) measurement error for mediating transcripts and ( 2 ) imperfect LD between measured SNPs and the causal variants . Simulations also demonstrate that cis-trans confounding can bias estimates obtained from mediation analysis , while correlations among neighboring cis-transcripts , can enable detection of mediation when the true mediator is unmeasured . Observing evidence of mediation was more likely for trans-eQTLs with smaller P-values and when the lead trans-eSNP was in strong LD with the lead cis-eSNP for the potentially-mediating transcript . Demonstration of cis-mediation for observed trans-eQTLs provides a form of validation , a clear biological mechanism , and an approach for enhancing future trans-eQTL discovery . Among our 434 significant trans-eQTL associations , we lacked data on potential mediators for 245 trans-associations ( i . e . , the lead trans-eSNP was not identified as a cis-eSNP in genome-wide cis-eQTL analyses ) . This lack of data on potential mediators could be due to several factors . First , many mediators may be unmeasured or excluded as a consequence of QC ( Table S4 ) . Second , some trans-eQTL effects may not be mediated by expression of a cis-transcript . For example , a trans effect could be due to variation in the coding sequence of a regulatory factor ( Table S4 ) , physical inter-chromosomal interactions , non-coding RNA , or other mechanisms that do not entail mediation by cis-expression of a protein-coding gene . Third , some trans-eQTLs may be false positives . This is likely the case for many trans-eQTLs of marginal significance ( 5×10−9<P<10−7 ) , which are less likely to be cis-eQTLs than FDR-significant trans-eQTLs . However , even for highly-significant trans-eQTLs ( P<10−15 ) , ∼38% lack data on a potential cis-mediator ( Table 2 ) . Fourth , it is possible that trans-eQTLs may be due to cis effects that are detectable as very weak associations in our dataset; however , our mediation analysis for trans-eSNPs that were not identified as cis-eSNPs did not provide strong evidence for this hypothesis ( Figure S6 ) . All of these phenomena are possible explanations for the substantial number of trans-eQTLs for which we lack data on a potential cis-mediator . Our working hypothesis is that a substantial fraction of trans-eQTLs are fully-mediated by a transcript that is regulated in cis by the causal trans-eQTL variant ( Fig . 1 ) . While we did not observe complete mediation for most observed trans-eQTLs , we demonstrate that full mediation will be observed as partial mediation in the presence of mediator measurement error and/or imperfect LD between the causal variant and the variant used for analysis . Measurement error and imperfect LD are typically present in eQTL studies; thus , full mediation will frequently be observed as partial mediation . Factors that contribute to measurement error include: experimental error , cell type-specific eQTLs in the presence of cell mixtures , stochastic or temporal variability in expression , and non-specific measurement of the mediating transcript ( s ) ( i . e . , some probes bind multiple isoforms ) . Observing partial mediation may also be due , in part , to the Winner's curse , as trans-eQTL associations that are overestimated may not be fully explainable by a cis-mediator . For the trans-eQTL that lacked clear evidence of mediation , potential explanations include: analyzing a cis-transcript that is not the true mediator ( perhaps due to missing data ) , low power due to measurement error , or a false positive trans-eQTL . In this work , we observe a subset of trans-eQTLs that are clearly attenuated after adjustment for a cis-mediator ( i . e . , mediation proportion>0 and Sobel P<10−5 ) , as expected based on the mediation hypothesis ( Fig . 2A ) . However , for the remaining trans-eQTLs , the mediation proportion estimates are scattered around zero ( i . e . , the trans association often gets somewhat stronger after adjustment ) and the Sobel P distribution is non-uniform . We hypothesize that many of these “significant” estimates are due to a mixture of true mediation and the various sources of potential bias we describe in this work , including cis-trans confounding and correlation between the true ( unmeasured ) mediator and the transcript selected for mediation analysis . These types of bias have no expectation regarding directionality , so a distribution of mediation proportions that includes zero is expected . Potential confounders of the cis-trans association include demographic and environmental factors , as well as a wide-array of biological phenomenon , some of which may be captured by measured expression features . Omitting such variables from the regression model can bias the estimates of the “direct effect” of the SNP on the trans-gene and the “mediation proportion” . The direction of this bias will depend on the direction of the effects for the omitted confounder ( s ) . We attempted to control for potential confounding factors in this work using only principle components adjustment ( see methods ) , but this limitation did not prevent us from detecting many examples of cis-mediation . However , confounding bias is likely to prevent detection of weaker mediation signals . Because genome-wide expression data contains very large numbers of correlated genes ( too many to adjust for individually ) , additional research is needed to develop methods for comprehensive adjustment for cis-trans confounding in analyses of mediation in the genome-wide setting . Few prior studies have assessed cis-mediation for trans-eQTL associations at the genome-wide level . Jian et al . described the use of mediation analysis in order to identify eQTLs for CYP2D6 activity [18] . Battle , et al . [19] used RNA sequencing data from whole blood on 922 genotyped individuals to characterize the effects of regulatory variation on transcriptome diversity . They observed 138 genes regulated by trans-variants and 76 trans-eQTL SNPs that were associated with expression of a proximal gene . Using a likelihood-based approach [20] , [21] , Battle et al . reported that 85% of identified trans effects were mediated by cis transcripts , but with only 4% showing evidence of “full mediation” and the remaining 81% showing evidence of partial mediation . However , the likelihood-based test for partial mediation used by Battle , et al . is also based on a regression the trans-gene on the SNP and the cis-mediator , and is therefore also prone to confounding biases caused by unobserved variables . The number of trans-eQTLs observed in this South Asian population is somewhat larger than prior studies of similar sample size [5] , [6] , [11] . Prior studies have also noted low rates of replication for trans-eQTLs across studies , even for studies of similar ancestry [5] , [9] . For example , 46 of the 130 trans-eQTLs observed by Fehrmann et al . ( in whole blood among 1 , 469 samples ) could be replicated in an eQTL study of monocyte RNA at P<10−5 among 1 , 490 samples [6] . Difficulties in replication have also been observed in trans-eQTL studies of mice [22] . Replication was markedly better in a recent trans-eQTL meta-analysis , presumably due in part to large sample size ( n>5 , 000 ) and a focus on trait-associated SNPs [11] . Several factors may contribute to the low rates of replication of our observed trans-eQTLs in prior studies . First , differences between population , both genetic and environmental may impact trans-eQTL patterns . Second , there are differences in RNA source , as prior trans-eQTL studies used whole blood or monocytes , while our source is PBMCs , consisting of monocytes ( ∼15% ) , T lymphocytes ( ∼65% ) , and B lymphocytes ( ∼20% ) , representing ∼35% of peripheral white blood cells . Third , we lack complete lists of trans-eQTL for replications purposes , as we are limited to examining lists of only the strongest trans-eQTL associations provided by the authors of prior papers . Mediation analysis is an attractive method for characterizing observed trans-eQTL associations for several reasons . First , it provides putative regulatory mechanisms for observed trans-eQTLs , potentially enhancing our understanding of disease-associated variants and human disease biology . Second , evidence of mediation provides a form of validation for trans-eQTLs . Independent replication ( using the same cell type and population ) is the ideal form of validation , but such data sources are not always available . Third , detecting mediation is methodologically straightforward , requiring only conditional linear regression techniques and simple equations for estimating effects and significance ( see methods ) [23] . Fourth , evidence of mediation could potentially be used as weights in integrative analyses or as priors in Bayesian analyses to enhance discovery of trans-eQTLs that have measured mediators . Mendelian randomization ( i . e . , instrumental variable ( IV ) ) approaches could also be considered as a complimentary approach , in which cis-eSNPs are used as IVs for cis-transcripts and which are then screened , genome-wide , for effects on expression of trans-genes . Our ability to detect cis-mediation will be enhanced by using whole-transcriptome RNA-sequencing data , which capture the vast majority of transcribed sequences , better reflecting the full complexity of the transcriptome . Array-based platforms capture only a fraction of transcribed sequences and probe-based measures often reflect multiple isoforms of a gene . RNA-sequencing can also provide enhance measurement precision for mediating transcripts , as would cell-type specific RNA sources . Very high-density genotype data will also enhance mediation analysis , as statistical evidence of mediation will be stronger when causal variants or strong tagging variant are directly measured . In terms of sample size , this is the largest trans-eQTL study of humans to date that analyzes genome-wide variants in an agnostic fashion . An additional strength of this study is the rapid time between the blood draw and the extraction and processing of RNA . While this is often not reported in eQTLs studies conducted in the epidemiological setting , our processing protocol is excellent for studies of this size , especially for a low-resource setting such as Bangladesh . In addition , this is the first eQTL study conducted in a South Asian population . A recent multi-population eQTL study included U . S . residents of Indian ancestry [3]; however , the sample was relatively small , and RNA was obtained from lymphoblastoid cells lines and was thus prone to the effects of transformation with Epstein-Barr virus . In conclusion , we have described cis- and trans-eQTLs in a large sample of South Asians and used mediation analysis to provide evidence that cis-mediation is often observed for trans-eQTLs in humans . In addition , using simulated data , we demonstrate how unobserved confounding variables and incorrect mediator selection can bias mediation estimates . Mediation analysis will be useful for validation and discovery of trans-eQTLs ( especially when appropriate data for replication is not available ) and is a valuable tool for enhancing our understanding of the biological and regulatory mechanism underlying trans-eQTLs .
Subjects genotyped for this work were participants in the Bangladesh Vitamin E and Selenium Trial ( BEST ) [24] . BEST is a 2×2 factorial randomized chemoprevention trial evaluating the long-term effects of vitamin E and selenium supplementation on non-melanoma skin cancer risk among 7 , 000 individuals with arsenic-related skin lesions living in Araihazar , Matlab , and surrounding areas . Participants included in this work are a subset of BEST participants from Araihazar that have available data on genome-wide SNPs and array-based expression measures ( described below ) . DNA extraction was carried out from the whole blood using the QIAamp 96 DNA Blood Kit ( cat # 51161 ) from Qiagen , Valencia , USA . Concentration and quality of all extracted DNA were assessed using Nanodrop 1000 . As starting material , 250 ng of DNA was used on the Illumina Infinium HD SNP array according to Illumina's protocol . Samples were processed on HumanCytoSNP-12 v2 . 1 chips with 299 , 140 markers and read on the BeadArray Reader . Image data was processed in BeadStudio software to generate genotype calls . Quality control was conducted as described previously for a larger sample of 5 , 499 individuals typed for 299 , 140 SNPs [25] , [26] . First , we removed DNA samples with very poor call rates ( <90%; n = 8 ) and SNPs that were poorly called ( <90% ) or monomorphic ( n = 38 , 753 ) . Individuals with gender mismatches were removed ( n = 79 ) , as were technical replicate DNA samples run to assure high genotyping accuracy ( n = 53 ) . No individuals had outlying autosomal heterozygosity or inbreeding values . After inspecting distributions of SNP and samples call rates , we excluded samples with call rates <97% ( n = 5 ) and SNPs with call rates <95% ( n = 1 , 045 ) . SNPs with HWE p-values<10−10 were excluded ( n = 1 , 045 ) . This QC resulted in 5 , 499 individuals with high-quality genotype data for 257 , 747 SNPs . The MaCH software [27] was used to conduct genotype imputation using HapMap3 GIH reference haplotypes . Only high-quality imputed SNPs ( imputation r2>0 . 5 ) with SNPs with MAF>0 . 05 were included in this analysis . A subset 1 , 799 individuals with available data on array-based expression measures was used for this project RNA was extracted from PBMCs , preserved in buffer RLT , and stored at −86°C using RNeasy Micro Kit ( cat# 74004 ) from Qiagen , Valencia , USA . Concentration and quality of RNA samples were assessed on Nanodrop 1000 . cRNA synthesis was done from 250 ng of RNA using Illumina TotalPrep 96 RNA Amplification kit . As recommended by Illumina we used 750 ng of cRNA on HumanHT-12-v4 for gene expression . Expression data were quantile normalized and log2 transformed . The chip contains a total of 47 , 231 probes covering 31 , 335 genes . There were 1 , 825 unique individuals in both expression data and SNP data . For the vast majority of participants , between 30% and 47% of probes had detection P values <0 . 05 . However , 26 individuals had>30% of probes with detection p-value <0 . 05 , and these outlying individuals were excluded from the analysis , leaving an analysis sample size of 1 , 799 . For this analysis , no probes were excluded based on detection P-values . To ensure each probe mapped uniquely to a single gene , we aligned the Illumina probe sequences to all transcriptome sequences contained in both the knownGeneMrna and the knownGeneTxMrna tables from the UCSC Genome Browser ( version GRCh37/hg19 ) . Probe sequences were aligned using BLAST , as recommended by Barbosa-Morais et al . [28]: ( blastn -dust no -evalue 10e-6 ) . This resulted in alignments between 38 , 924 probes and 66 , 864 transcripts . From these alignments , we selected un-gapped alignments with up to 2 mismatches , as recommended by Barbosa-Morais et al . , resulting in alignments between 35 , 202 probes and 61 , 350 transcripts . We then determined which transcripts ( i . e . , isoforms ) belonged to the same gene using the knownIsoforms table from UCSC genome browser , which resulted in 35 , 202 probes mapped to 23 , 419 isoform clusters ( i . e . , genes ) . We did not disqualify probes that mapped to several isoforms of the same gene , provided they did not map to isoforms of any second gene . After excluding probes that mapped to multiple genes , we identified 31 , 853 probes were specific to 20 , 143 genes . For the 31 , 853 specific probes , we obtained absolute genomic coordinates from the UCSC knownGene table , which contains genomic coordinates for all transcripts in knownGeneMrna/knownGeneTxMrna , including gaps introduced by introns . We referred to the UCSC snp135Common table to count the number of SNPs in each probe , according to the probe's genomic coordinates . Out of the 31 , 853 specific probes , 8 , 880 probes contained one or more SNPs , and these were excluded from all cis-eQTL analyses . Of these , the majority ( 6 , 194 probes ) only contained a single SNP . We used probe-level data for this analysis ( as opposed to combining probe data into gene-level expression traits ) , primarily because some probes bind specific isoforms of a transcript that are not detected by other probes that target the same gene . Probe intensity values were log-transformed . Batch effects ( 22 batches total , representing 22 96-well plates ) were assessed using the empirical Bayes framework implemented in the Surrogate Variable Analysis ( SVA ) software package ( ComBat ) [29] . SVA did not detect any significant surrogate variables , thus we used principle components ( PC ) analysis to estimate 100 PCs that were subsequently considered as potential latent variables that may represent variability attributable to technical ( i . e . , non-biological ) factors . All expression values were log-transformed prior to analysis . Linear regression , as implemented in the matrix-eQTL software package [30] was used to conduct genome-wide cis- and trans-eQTL analyses . Cis associations were tested for SNPs and probes <1 Mb apart ( i . e . , a 2Mb window centered on each SNP ) . Trans-associations were tested for all inter-chromosomal SNP-probe pairs , as well as for intra-chromosomal SNPs-probe pairs>10 Mb apart . For both cis and trans analyses , we used an FDR of 0 . 05 to report the significant associations , as calculated by the matrix-eQTL software . We generated data on 100 PCs , and conducted cis-eQTL analyses multiple times , adjusting for 20 , 40 , 60 , 80 , and 100 PCs . The number of cis-eQTLs detected increased as we adjusted for additional PCs , but the increase in power was very small for 100 PCs as compared to 80 , so we elected to adjust for 80 PCs in the cis-eQTL analysis . For the trans-eQTL analysis , we only adjusted for the 14 ( of the 80 ) PCs that were not associated with any SNP at a P-value>5×10−8 . Genome-wide trans-eQTL analyses showed little evidence of inflation due to population structure of genotyping artifacts ( Figure S11 ) , consistent with our prior work demonstrating little evidence of population structure among our study participants . Lead SNPs fear each eQTL were defined as the SNP with the smallest P-value for an expression trait , with trans-associations>5Mb apart considered independent trans-eQTL associations . Among our observed trans-eQTL associations with P<10−7 , we sought to eliminate false positives due to loose , off-target binding of the expression probe near the correlated SNP . A localized high-sensitivity BLAST was performed ( blastn -dust no -evalue 1000 ) . For each instance of BLAST , the query was the sequence of the expression probe from our list of trans-eQTLs , and the target was the genome sequence from a 4Mb window centered on the corresponding SNP ( hg19 , retrieved from UCSC ) . Note that our initial probe QC used a lower-sensitivity BLAST with a much larger query and target sets ( i . e . : all HT-12 probes and all sequences UCSC's knownGeneMrna , knownGeneTxMrna ) . After identifying putative trans-eQTL associations , smaller query and target sets could be selected for a higher-sensitivity BLAST . This two-stage approach was also used by Fehrmann et al . [5] . From the BLAST results , we accepted alignments with: alignment length> = 15bp; or alignment length> = 20 and number of mismatches < = 1; or alignment length> = 30 and number of mismatches < = 2; or alignment length> = 50 and number of mismatches < = 15 . Trait-associated SNPs were selected from the NHGRI's GWAS catalog based on a reported P<5×10−8 . The resulting list of SNPs was pruned to eliminate SNPs with high LD ( no pair-wise r2>0 . 3 ) . For the GWAS enrichment analysis , we compared the catalog SNPs with a reported P<5×10−8 to catalog SNPs with P>5×10−8 , a method previously used by Westra et al . [11] . For this analysis , a Fisher's exact test was used to assess significance of enrichment . For the cis/trans enrichment analysis , random sets of SNPs were extracted from our dataset matched to our set of trait-associated SNPs based on MAF ( 10 categories ) and distance to transcription start site ( 10 bins ) . Empirical P-values were estimated using 1 , 000 replicate datasets . To identify trans-eQTLs showing evidence of mediation , we restricted to those lead trans-eSNPs ( P<10−7 ) which has at least one associated nearby ( <1 Mb ) probe ( cis-probe ) with association ( P<2 . 2×10−3 , the P threshold used for the cis-eQTL analysis ) . For trans-eSNPs associated with multiple cis-probes , we selected the associated cis-probe whose lead cis-eSNP was in strongest LD with the lead trans-SNP . Mediation analysis was conducted as follows: For lead trans-eSNPs that were associated with a cis probe , the trans-association was re-estimated , adjusting for expression of the local transcript measured by the probe . The difference between the beta coefficients for the trans-association before and after adjustment for the cis probe was expressed as the “proportion of the total effect that is mediated” ( i . e . , % mediation ) , calculated as ( βunadj – βadj ) /βunadj[23] , with βunadj and βadj known as the total effect the direct effect , respectively . The Sobel P-value for mediation [31] was calculated by first estimating the cis-adjusted trans-association for the lead trans-eSNP: We then estimated the trans-eSNP's association with the probe in cis ( the potentially mediating probe ) : The P-value was then estimated by comparing this following t statistic to a normal distribution: where SE is the pooled standard error term calculated from the above beta coefficients and their variances . β1 β2 is often referred to as the indirect effect . Using real genotype data for all 1 , 799 participants , we selected a causal variant for simulation purposes and generated data on a cis-transcript ( standard normal distribution ) influenced by the causal variant ( R2 = 0 . 1 ) and a trans-transcript ( standard normal distribution ) influenced by the cis-transcript ( β = 0 . 2 ) . We introduced measurement error by adding normally-distributed error components to both the cis- and trans-transcripts . Standard deviations for these components were chosen to produce specific r2 values ( 1 . 00 , 0 . 75 , 0 . 50 , 0 . 25 , and 0 . 10 ) for the correlation between the true mediator and the measure transcript , with lower values reflecting higher error . For each measurement error scenario , 500 datasets were simulated , and analyses were conducting using variants with a wide range of LD ( i . e . , r2 values ) with the true causal variant . In order to assess the impact of confounding between a cis-mediating transcript and a trans-gene involved in a trans-eQTL association , we generated data similar to that described above , but introduced an “unobserved” variable ( U ) that affects both the cis- and trans-transcripts . We varied the strength of the cis-eQTL effect in terms of its r2 ( 0 . 05 to 0 . 4 ) , the strength of the effect of the cis-transcript on the trans-transcript ( 0 . 1 , 0 . 2 , and 0 . 3 ) , and the strength of the confounding relationship in terms of the effects of U on the cis-transcript ( βU-cis ) and the trans-transcript ( βU-trams = |βU-cis| ) . The SNP was coded as the number of alleles that increase the abundance of the cis-transcript . In order to assess the impact of selecting a cis-transcript for mediation analysis that is not the true mediator , we conducted similar simulations as those described above , but generated data on an additional transcript influenced by a variant near the causal variant for the true mediating transcript . We first selected several SNPs near the selected causal variant for the primary cis-/trans-eQTL with a wide range of LD r2 values , and we then treated these variants as causal variants for a second eQTL association for a different transcript that does not affect the trans-transcript . In the simplest scenario , we simulated data with no dependency between the true cis-mediator and the selected transcript other than correlation due to LD between the causal variants that influence their expression ( Figure S8A ) . We then introduced correlation between the two cis-transcripts using several different approaches . First , we introduced confounding by an unmeasured factor , using effect sizes of 0 . 3 and 0 . 5 on both transcripts to represent “weak” and “strong” confounding ( Figure S8B ) . We also introduced “negative confounding” , in which the effect of the unmeasured confounder was positive for one cis-transcript and negative for the other . Second , we introduced an effect of the true cis-mediator on the selected transcript , exploring both positive and negative effect ( beta = 0 . 25 and −0 . 25 ) , as well as the reverse causal relationship where the selected transcript affects the true cis-mediator ( beta = 0 . 25 and −0 . 25 ) . Lastly , we introduced an unmeasured confounding factor affecting both the trans-gene and the selected cis-transcript ( Figure S8D ) . Data for replicating observed trans-eQTLs was obtained from several prior trans-eQTL studies using a RNA extracted from peripheral blood or subtypes of white blood cells [5] , [7] , [11] . Consensus trans-eQTLs from HapMap lymphoblastoid cells line studies were also used for replication purposes [17] . Considering multiple genotyping and expression platforms were used across these studies , replication as defined as trans-associations which involve the same expressed gene ( based on HUGO gene symbol ) and the SNPs in the same genomic region ( <500 kb apart ) . This research as approved by the Institutional Review Boards of The University of Chicago , Columbia University , and the Bangladesh Medical Research Council , and all study participants provided informed consent . Summary statistics for the cis- and trans-eQTL analyses as well as the mediation analysis are available at http://doi . org/10 . 5061/dryad . tp097 .
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Expression quantitative trait locus ( eQTL ) studies have demonstrated that human genes can be regulated by genetic variation residing close to the gene ( cis-eQTLs ) or in a distant region or on a different chromosome ( trans-eQTLs ) . While cis-eQTL variants are likely to affect transcription factor binding or chromatin structure , our understanding of the mechanisms underlying trans-eQTLs is incomplete . We hypothesize that a substantial fraction of trans-eQTLs influence expression of distant genes through mediation by expression levels of a cis-transcript . In this paper , we use genome-wide SNPs and expression data for 1 , 799 South Asians to identify cis- and trans-eQTLs and to test our hypothesis using Sobel tests of mediation . Among 189 observed trans-eQTL associations , we provide evidence of cis-mediation for 39 , 6 of which show mediation in an independent European cohort . We used simulated data to demonstrate that complete mediation will be observed as partial mediation in the presence of mediator measurement error or imperfect LD between measured and causal variants . We also demonstrate how unobserved confounding variables and incorrect mediator selection can bias mediation estimates . In conclusion , we have identified cis-mediators for many trans-eQTLs and described a mediation analysis approach that can be used to validate , characterize , and enhance discovery of trans-eQTLs .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
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"gene",
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2014
|
Mediation Analysis Demonstrates That Trans-eQTLs Are Often Explained by Cis-Mediation: A Genome-Wide Analysis among 1,800 South Asians
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Identifying gene-gene interaction is a hot topic in genome wide association studies . Two fundamental challenges are: ( 1 ) how to smartly identify combinations of variants that may be associated with the trait from astronomical number of all possible combinations; and ( 2 ) how to test epistatic interaction when all potential combinations are available . We developed AprioriGWAS , which brings two innovations . ( 1 ) Based on Apriori , a successful method in field of Frequent Itemset Mining ( FIM ) in which a pattern growth strategy is leveraged to effectively and accurately reduce search space , AprioriGWAS can efficiently identify genetically associated genotype patterns . ( 2 ) To test the hypotheses of epistasis , we adopt a new conditional permutation procedure to obtain reliable statistical inference of Pearson's chi-square test for the contingency table generated by associated variants . By applying AprioriGWAS to age-related macular degeneration ( AMD ) data , we found that: ( 1 ) angiopoietin 1 ( ANGPT1 ) and four retinal genes interact with Complement Factor H ( CFH ) . ( 2 ) GO term “glycosaminoglycan biosynthetic process” was enriched in AMD interacting genes . The epistatic interactions newly found by AprioriGWAS on AMD data are likely true interactions , since genes interacting with CFH are retinal genes , and GO term enrichment also verified that interaction between glycosaminoglycans ( GAGs ) and CFH plays an important role in disease pathology of AMD . By applying AprioriGWAS on Bipolar disorder in WTCCC data , we found variants without marginal effect show significant interactions . For example , multiple-SNP genotype patterns inside gene GABRB2 and GRIA1 ( AMPA subunit 1 receptor gene ) . AMPARs are found in many parts of the brain and are the most commonly found receptor in the nervous system . The GABRB2 mediates the fastest inhibitory synaptic transmission in the central nervous system . GRIA1 and GABRB2 are relevant to mental disorders supported by multiple evidences .
Gene-gene interactions have been proposed as one potential explanation of the well-known problem of missing heritability [1] , and a recent report [2] has quantitatively demonstrated that possibility . Researchers have long attempted to identify interactions , with methods ranging from evolutionary genetic studies [3] , [4] , systems biology studies of model microbes [5] and quantitative genetic studies of inbred model organisms , to linkage [6] and association studies in human populations [7]–[14] . Although the definitions of the term “epistasis” used by biologists ( Batson 1909 ) [15] and statisticians ( Fisher 1918 ) [16] are different , they have the same consequences regarding different distributions of genotype patterns among different phenotypes . The main obstacle of interaction analysis is that the large number of multi-locus genotype combinations generated from large numbers of genetic variants is too high for current computational resources . This is in fact a well-known computational problem , known in the field of computer science as the ‘curse of dimensionality’ [17] . In this work we developed AprioriGWAS , a tool to address this problem . This tool is based on a successful algorithm in the field of computer science , Apriori [18] . Apriori was originally designed for supermarket data mining to assist shop owners in designing the layout of displayed products . Given customers' transactions , the algorithm can identify sets of items that frequently co-exist in transactions . For example , by knowing that customers usually buy milk and bread together , the shop owner can put them near each other in the store . Before describing the algorithm , we briefly give definitions of a few key terms: item is defined as an individual product , for example , bought by a customer; itemset stands for a set of items purchased together; length of itemset is defined as the number of items in the itemset . The process of growing a short itemset to a longer itemset is referred to as pattern growth . Generally , the key insights of Apriori are that: ( 1 ) frequent itemset with many items can be gained by growing itemset of short length; and ( 2 ) since subsets of any frequent itemset should also be frequent during pattern growth , itemsets predicted not to have any effect can be dropped during pattern growth , thereby significantly reducing the search space . In the case of GWAS , the number of individual genotypes is analogous to the number of transactions in supermarket data . The genotype of a variant is an item , and genotype combinations of different variants are an itemset , here also called a genotype pattern . Instead of just finding frequent genotype patterns , we want to find genotype patterns with different frequencies in cases and controls . We call them differential genotype patterns . While Apriori originally works on one database to find the most frequent itemsets , we are interested in patterns with different frequencies in two databases ( cases and controls ) . To assess whether a pattern should be retained during pattern growth , we make use of the proportion test [19] ( Methods ) . Interaction among variants is carried out after obtaining all differential genotype patterns . We test the possibility of interaction among variants involved in a differential genotype pattern by conducting Pearson's Chi-square test for the contingency tables composed of all genotype patterns found for variants and phenotypes ( Methods ) . In this step , we try to distinguish whether a differential pattern is caused by variants with marginal effects or by interaction effect . The process of pattern growth helps to narrow down the number of variant combinations to be tested for interaction effect . Using simulations following Marchini et al's procedure [11] , we demonstrate that AprioriGWAS can approximately achieve the same coverage of associated patterns as an exhaustive search , but with far lower CPU time . Determining all potential combinations that are statistically associated with disease does not automatically identify genuinely interacting genes . The daunting number of all combinations of variants heavily increases the load of multiple tests and mixes genuine signals with noise . As summarized by Anderson [20] , in the regression model with two main effects terms and one interaction term , there is no exact permutation method for testing the significance of the interaction term . Buzkova et al [21] proposed a parametric bootstrap test for gene-gene and gene-environment interactions , which unfortunately is not practical for very large numbers of possible combinations of variants . Computer simulation [22] shows that whenever a trait is controlled by more than a single factor , it becomes possible for a neutral variant together with a major-effect variant as a pattern to be more strongly associated with the trait than with any of the causative factors [13] . These indirect associations are true associations for statistical purposes , and can be indistinguishable from medical causative associations [22] . To distinguish general association and interaction effects , we developed a new conditional permutation test to distinguish genuine interactions from the artifacts generated by the combination of a major-effect variant with a neutral variant ( Methods ) . We demonstrate that our new approach has a magnitude lower false discovery rate ( FDR ) compared with regular permutation , while maintaining comparable power . We applied AprioriGWAS to age-related macular degeneration ( AMD [MIM 153800] ) , which has been deemed a good example of a small number of common variants explaining a large proportion of heritability [1] . Among the most significant patterns , we found six pairs of retinal genes interacting with each other . An exciting example is the interaction of a gene involved in an AMD treatment target , ANGPT1 , with another important AMD gene , CFH . Overall , the potentially interacting genes were enriched in glycosaminoglycan biosynthetic process ( ) . Many studies have shown that the interaction between glycosaminoglycans ( GAGs ) and CFH plays an important role in the disease pathology of AMD . We also applied AprioriGWAS to bipolar disorder; we found potential interactions inside individual gene ( 8 out of 18 genes are related with mental disorder ) and interactions across gene or chromosomes . Further results will be presented in full later . The remainder of this paper is organized as follows . In the next section we introduce the AprioriGWAS algorithm for mining possible interaction variants , as well as the conditional permutation approach for testing interactions . We then evaluate the performance of AprioriGWAS with simulated data and compare it with logistic regression implement in Epistasis function of PLINK . Lastly we demonstrate applications of AprioriGWAS to AMD and WTCCC bipolar data and exciting findings from both datasets .
Historically , the Apriori algorithm can be traced back to the seminal paper published by IBM Research in 1993 [18] . The concept of the main technique is that a subset of frequent itemset should also be frequent . Based on this concept , frequent itemset with more items may be found by stepwise growth of smaller frequent itemset , which saves substantial computational resources . Interested readers may refer to their original paper [18] for a professional description or to our own longer report [23] for illustrative descriptions . Here we briefly outline the main steps . Suppose one wants to mine frequent itemset with length no more than n . Apriori will usually scan dataset in n rounds ( unless there is no new frequent itemset generated in a certain round before n , thereby forcing the algorithm to halt ) . In the first round , it will initiate the 1-itemsets that are frequent . In each subsequent round , it will take the frequent itemset generated in the last round as starting point and grow any itemset by adding one more item . Retention of the new itemset will be decided by firstly predicting how likely it will be and then , given a positive prediction , by checking the actually supporting transactions . Finally , the collection of all frequent itemset in all rounds will be reported . In this paper , genotype patterns are defined as genotype combinations of different variants . We use integer numbers as ids of variants; then we can have , for instance , a pattern like 46AT_609GG_1099CC , denoting a pattern composed of a variant with id 46 and genotype AT combined , a variant with id 609 and genotype GG , and a variant with id 1099 and genotype CC . The key goal is to find genotype patterns that have a significant frequency difference in cases and controls ( called differential patterns in this paper ) . The algorithm of AprioriGWAS is divided into two steps . First , detecting differential genotype patterns by an Apriori-like strategy . Obviously , the same set of variants can lead to several differential genotype patterns . Second , testing interaction among a set of variants by testing association of all possible combinations of genotype patterns against case/control status . The first step helps to narrow down the combinations of variants need to be tested . Due to multiple test problems and potential association of single variants involved in the differential genotype pattern , we adopt a new conditional permutation in the second step to control the marginal effect of single variants for testing of variant interactions .
We simulated data by two-locus interaction models proposed by Marchini et al [11] ( Methods ) , in which three types of interactions are generated . We then applied regular permutation and conditional permutation to control family-wise type I error . The performances of regular permutation and conditional permutation test ( Methods ) are demonstrated in Figure 1A and 1B . We compared both power and FDR , using regular permutation and conditional permutations to adjust thresholds for type I error . Family-wise type I error was set to 0 . 05 for both methods . It is evident that the FDR was significantly reduced by the conditional permutation test , although some power is sacrificed compared with regular permutation . To demonstrate that the nominal p-value of a contingency table for multi-variants could be in large part caused by individual variants with strong marginal effect , we took a real example from analyzed AMD data . Figure 2A shows two variants , each with no marginal effect , but in combination with strong marginal effect . Figure 2B shows two variants , one has strong marginal effect , and the other does not show any marginal effect . Although the nominal p-value of the contingency table is more significant than the pair of variants in Figure 2A , one can deduce that the low p-value from Figure 2B is in large part caused by the variants with strong marginal effect; in Figure 2A , on the other hand , there must be some interaction effect . As mentioned , AprioriGWAS manages to dramatically speed up the search process by dropping the candidate genotype patterns unlikely to grow to differential pattern . Since it is based on prediction at an early stage in the search , it still theoretically runs the risk of mistakenly dropping sensible patterns . Here we quantitatively tested the percentage of mistakenly dropped differential patterns by comparing AprioriGWAS and exhaustive search ( Method ) . Figure 3 shows the comparison between searching for combinations of variants ( with default parameters in AprioriGWAS ) and exhaustive search . We found that 97% of all differential genotype patterns found by exhaustive search were covered by the results from AprioriGWAS . With such high coverage , the chance of losing possible interaction variants is minimized . There are a few points below 85% , reflecting that there is variation of power to cover all potential combinations . It is true that the overall coverage is subject to lots of parameters , like sample size and allele frequency . To minimize this variation , larger sample size is always desirable . We compared the ability of AprioriGWAS to find interacting variants with traditional single locus genotypic test and exhaustive search in PLINK [28] ( epistasis function ) . The epistasis function in PLINK for case control data is basically stepwise logistic regression . We chose to use the all combinations option . The power comparison is based on two levels: finding at least one casual variant , or finding both interacting variants ( Figure 4 ) . For Level 1 , detecting at least one causal variant , we found that the traditional single variant test had the highest power in Model 1 , which has explicit marginal effects for both causal variants . AprioriGWAS performed similarly with the single loci test in Model 2 , and had better power in Model 3 ( Figure 5 ) . This is natural , since Model 2 and 3 , which contain no explicit marginal effects , are expected to be harder to detect without an interaction-based searching strategy . For Level 2 , detecting both interacting variants , it is evident that AprioriGWAS had the highest power in most cases of Model 2 and 3 ( Figure 4 ) . On the other hand , the performance of the epistasis function in PLINK , which exhaustively searches all combinations , was not as good in all cases . This is because: ( 1 ) stepwise logistic regression does not capture the interactions well , since the effects of the terms are added in a linear manner , whereas AprioriGWAS explicitly addresses detailed patterns; ( 2 ) in stepwise logistic regression the genuine interactions are buried by the noise of a too large number of combinations , whereas with the conditional permutation test used in AprioriGWAS , genuine interactions are able to stand out . When comparing corresponding panels in Figure 4 and Figure 5 , it is observed that for the single variant test the power of finding both interacting variants ( i . e . , Level 2 ) dropped significantly compared with the power of finding at least one causal variant ( i . e . , Level 1 ) . By contrast , interaction based methods , i . e . , both AprioriGWAS and PLINK epitasis , maintained similar power for both levels . This was not unexpected since the interaction-based strategies should be better able to find an epistasis effect . We also simulated data that have more SNPs ( 1 , 000 , 000 ) and find that the relative power between three methods and interaction models remain similar although the absolute powers are all decreased . ( Figure S1 ) Figure 6 shows the power of AprioriGWAS and single variant test on three classical genetic models studied in model organisms . There are three powers for each genetic models: power for detecting at least one gene using single variant test , power for detecting both genes using single variant test , and power for detecting both genes using AprioriGWAS . Since PLINK is not scalable for such a dataset , we have not achieved power estimates for logistic regression . For the model “Duplicated Dominant” , AprioriGWAS outperforms single marker test for detecting single gene or both genes , whereas for models “Duplicated Recessive” and “Dominant & Recessive Interaction” , AprioriGWAS is more powerful for detecting both genes , but not for detecting single genes . It is notable that the power of detecting both genes in the model “Dominant & Recessive Interaction” , in which epistasis is functioning; single variant test has almost zero power ( 0 . 1% ) while AprioriGWAS has around 50% power . We compared the speed of our method with the epistasis function in PLINK . Figure 7 shows that the default threshold setting in AprioriGWAS was approximately a magnitude faster . Although retaining candidate genotype patterns in memory can help speed up the algorithm , its affordability is subject to the particular computational resources . We took the strategy of writing candidate patterns on hard disk for each round of pattern extension . The genotype data used to be relatively small comparing with the patterns however is getting larger and larger empowered by new sequencing platforms . To solve this problem , we implemented AprioriGWAS using HDF5-based data format [29] which stores genotype data on disk and accesses them as though stored in main memory . Therefore , the memory usage is scalable to whatever size of potential dataset and the speed is not scarified . ( See more on computational and memory complexity in section Discussion . ) We applied AprioriGWAS on published AMD data [26] . We identified 168 significant pairs of variants ( family-wise type I error = 0 . 01 ) , presented in Table S1 . By checking published functional literals and gene annotations , as well as GO enrichment of the genotype patterns , we learned that the findings are well validated by existing functional studies and clinical applications . Besides AMD data that were extensively analyzed by the community interested in gene-gene interactions , we also applied AprioriGWAS on Bipolar Disorder data from WTCCC [27] to further test whether it is scalable for larger dataset . The whole task was distributed onto 1 , 000 CPUs in a cluster and the average execution time for a single job is 56 . 8 hours . Only 4 Gb memories were employed during the computation , evidencing the great performance of HDF5-based implementations .
Regardless the goal being interaction or single gene , statistical tests all suffer from the problem of false positives . Since the numbers of variants ( and their combinations ) are usually a few magnitudes larger than the sample size for most association studies , it will be common to see false positives . The current practice in the community is that researchers who would like to claim association or carry out experimental validations usually have to check whether the results are replicable in other independent dataset ( s ) Researchers who use AprioriGWAS can also use this to filter results before doing experimental validations . As an example , we use another independent dataset for AMD study [64] to check whether the results are replicable . Among the five interactions with CFH reported in this paper , we found that BBS9/CFH and CHRM2/CFH are replicated in the other dataset . However , we understand that these two datasets are very different: one is wet AMD and the other is dry AMD . One of them is more prevalent in Asia than the other . Therefore , our further analysis of data in [64] may not serve as perfect replication of the findings presented , although it suggests that BBS9 and CHRM2 may be of higher priority for further experimental validations . The most commonly used multiple variants analysis is stepwise regression , in which variants are added to the regression equation one after another by some suitable criteria . But statistical analysis shows that the usual stepwise model selection methods are path dependent and therefore suboptimal [65] . Besides regression , some methods are based on discrete mathematics , like the Combinatorial Partition Method ( CPM ) [66] and its refined version , the Restricted Partition Method ( RPM ) [67] . However , RPM still requires a daunting number of tests when the number of variants is high . This is because its insight into reducing tests lies in its practice to combine close phenotypes , which consequently does not entirely solve the problem of too many combinations of genotypes . Another well-known method of counting potential combinations is multifactor dimensionality reduction ( MDR ) . It collapses cells in a contingency table into two groups and conducts a test on them . Essentially however it reduces the dimensionality of testing , rather than reducing the dimensionality of the process of counting genotype patterns . Therefore , when the number of variants is large , it still suffers from the “curse of dimensionality” [17] . Bayesian methods leveraging MCMC , e . g , BEAM [53] or epiMODE [8] , should theoretically suffer less from computational limitations , but they do not directly test detailed combinations of genotype patterns and thereby sacrifice the advantages of fine scale learning of gene-gene interactions . Another branch of frequently used methods is two-stage analysis [68] , by which the investigator can utilize relatively “simple” or computationally efficient tests to choose qualified variants in the first stage analysis . Then , taking advantage of the relatively small number of variants , the investigator can adopt some advanced but computationally heavy test to identify interacting genes . However , due to a lack of strong prior knowledge , the true signals might have been removed from the first stage if the procedure was not well designed . As an example , interacting variants with no marginal effect may be filtered out if one uses tests based on marginal effects of single variants in the first stage . Nevertheless , with good design , this approach is still very promising and can be combined with all the approaches reviewed above; and it can naturally also be combined with the method proposed in this work . Computation time and spatial complexities of the tool may be interesting to the reader . The number of transactions for original Apriori corresponds to sample size in GWAS; the number of items is equivalent to the number of variants and the itemsets . In contrast to supermarket data , GWAS data have a limited number of “transactions” , but a large number of “items” in two datasets , cases and controls . Both conditions make the problem more difficult . The time spent reading the data in each round of pattern growth is constant . In addition , the computational resources cost depends on how many combinations of genetic variants will be generated and tested . The more combinations are tested , the less likely it is that genuine patterns are missed , though of course more resources will be used . In AprioriGWAS , there are several parameters for the user to specify according to their computer resources and understanding of the disease model . The threshold for the proportion test and minimal support of concerned itemsets are parameters that affect candidate search space , algorithm speed , and power of detecting all distinct genotype patterns . When these parameters are set to zero , AprioriGWAS will exhaustively search all possible combinations . ( Please refer to our Manual of AprioriGWAS for the tradeoffs and discussions on setting these parameters according to computational resources . ) Those familiar with Apriori may suggest that , given Apriori's ability to also mine association rules , one could also treat the case control label as items and directly adopt Apriori for case/control data . The result will then be a subset of variants that can imply the case/control labels . But searching frequent itemsets and then mining the association between genotype pattern and disease status is inefficient , since frequent genotype patterns are not necessarily associated with phenotype; on the other hand , genotype patterns strongly associated with phenotype may not necessarily be in high frequency , and such an association could be distributed in different patterns than the same variants combinations . Instead of the conditional permutation proposed here , one could also consider Bonferroni correction . For n variants with search length of m , the total number of combinations is huge . Given the natural correlation of the combinations , it is clearly far more stringent than necessary . However , only correcting on the number of differential pattern tested produces a bias in the other direction , since the nominal value of the significance level of the chi-square test for the contingency table will be inflated by the selection procedure [69] . It is therefore always preferable to use a permutation test for the whole procedure . With regular permutation , one permutes the Case/Control label and then performs the whole test process . The smallest P-value of each permutation are ranked , allowing one to get the distribution of test statistics under “Null” from the permuted dataset . With regular permutation , no variant should have marginal effect , and the p-value of the contingency table for the combination of variants is under the null hypothesis of no variants having marginal effect . However , regular permutation suffers from an inflated significance level for contingency tables containing variants with marginal effects . This is due to the fact that when a contingency table is composed of at least one variant with strong marginal effect , the p-value for that contingency table becomes extremely small compared with regular permutation results . The FDR is therefore very high , even close to 1 . To solve the problem of an inflated significance level by a contingency table composed of at least one variant , v , with strong marginal effect , we developed a conditional permutation procedure ( Methods ) , which helps get the null distribution of the p-value of a contingency table composed of the variant and other variants . Simulation results show that , when we control the family-wise type I error by conditional permutation , we also keep FDR well controlled . Compared with INTERSNP [13] , which lists only the top 50 variant combinations including the variant with marginal effect , conditional permutation in AprioriGWAS keeps FDR well controlled in a systematic way . Another concern might be whether these differential genotype patterns are artifacts caused by linkage disequilibrium ( LD ) . We believe this is not the case , since the LD should impact both cases and controls , and therefore the pattern created by LD will not be differential unless the LD structure is significantly different in cases and controls for particular genetic variants . If that is the case , then there must be some reason of selection to explain the deviation in the genotype pattern , and it is difficult to judge whether this is an artifact or something of interest . In addition , our conditional permutation also breaks LD between interacting variants . Low-frequency or rare variation might impact the performance of the method , even when explicitly only testing for interactions among common variants . What matters is the extent of LD between causal rare variants and testing common variants . We haven't addressed this problem in the current method . It would be interesting to extend AprioriGWAS toward that direction . There may be non-trivial statistical challenges since the low-frequency or rare variants are usually less shared by the individuals therefore their combinations that form genotype patterns will be even less shared by individuals . For a given set of variants , we will have many patterns with little supports .
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Genes do not operate in vacuum . They interact with each other in many ways . Therefore , to figure out genetic causes of disease by case-control association studies , it is important to take interactions into account . There are two fundamental challenges in interaction-focused analysis . The first is the number of possible combinations of genetic variants easily goes to astronomic which is beyond current computational facility , which is referred as “the curse of dimensionality” in field of computer science . The other is , even if all potential combinations could be exhaustively checked , genuine signals are likely to be buried by false positives that are composed of single variant with large main effect and some other irrelevant variant . In this work , we propose AprioriGWAS that employees Apriori , an algorithm that pioneers the branch of “Frequent Itemset Mining” in computer science to cope with daunting numbers of combinations , and conditional permutation , to enable real signals standing out . By applying AprioriGWAS to age-related macular degeneration ( AMD ) data and bipolar disorder ( BD ) in WTCCC data , we found interesting interactions between sensible genes in terms of disease . Consequently , AprioriGWAS could be a good tool to find epistasis interaction from GWA data .
|
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"Abstract",
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"Methods",
"Results",
"Discussion"
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2014
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AprioriGWAS, a New Pattern Mining Strategy for Detecting Genetic Variants Associated with Disease through Interaction Effects
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Cognitive skills undergo protracted developmental changes resulting in proficiencies that are a hallmark of human cognition . One skill that develops over time is the ability to problem solve , which in turn relies on cognitive control and attention abilities . Here we use a novel multimodal neurocognitive network-based approach combining task-related fMRI , resting-state fMRI and diffusion tensor imaging ( DTI ) to investigate the maturation of control processes underlying problem solving skills in 7–9 year-old children . Our analysis focused on two key neurocognitive networks implicated in a wide range of cognitive tasks including control: the insula-cingulate salience network , anchored in anterior insula ( AI ) , ventrolateral prefrontal cortex and anterior cingulate cortex , and the fronto-parietal central executive network , anchored in dorsolateral prefrontal cortex and posterior parietal cortex ( PPC ) . We found that , by age 9 , the AI node of the salience network is a major causal hub initiating control signals during problem solving . Critically , despite stronger AI activation , the strength of causal regulatory influences from AI to the PPC node of the central executive network was significantly weaker and contributed to lower levels of behavioral performance in children compared to adults . These results were validated using two different analytic methods for estimating causal interactions in fMRI data . In parallel , DTI-based tractography revealed weaker AI-PPC structural connectivity in children . Our findings point to a crucial role of AI connectivity , and its causal cross-network influences , in the maturation of dynamic top-down control signals underlying cognitive development . Overall , our study demonstrates how a unified neurocognitive network model when combined with multimodal imaging enhances our ability to generalize beyond individual task-activated foci and provides a common framework for elucidating key features of brain and cognitive development . The quantitative approach developed is likely to be useful in investigating neurodevelopmental disorders , in which control processes are impaired , such as autism and ADHD .
The development of increasingly sophisticated cognitive skills relies on the maturation of control processes for orienting attention and allocating resources for task relevant information [1] , [2] . Such control processes are important for virtually every complex cognitive task , and there is growing evidence that they rely on functional interactions between multiple brain regions [3] . Despite the critical role of control processes in cognitive development , little is known about the maturation of functional brain systems underlying control mechanisms in the developing brain . Here we use a novel neurocognitive network approach with multimodal imaging to investigate the maturation of functional brain systems underlying control processes that support problem solving skills in young children . Based on experimental studies across a wide range of cognitive domains , a number of cortical areas within the frontal lobe , including the anterior cingulate cortex ( ACC ) , ventrolateral prefrontal cortex ( VLPFC ) , dorsolateral prefrontal cortex ( DLPFC ) and the fronto-insular cortex ( FIC ) have emerged as putative sites for implementing different aspects of control [4] , [5] , [6] , [7] , [8] , [9] , [10] . Yet , even in adults , how these brain regions interact and implement control is poorly understood . This is especially surprising because , almost by definition , control processes should involve multiple interacting nodes of a network . A key challenge in untangling the potentially complex hierarchy of frontal control mechanisms is identifying patterns of their interconnectivity and how causal interactions emerge during performance of a cognitively demanding task . To date , however , there have been few systematic investigations of network interactions underlying control processes in adults and almost nothing is known about how these processes mature with development . In this study we use a theoretically motivated approach to this problem based on neurocognitive network models derived from studies of intrinsic brain connectivity . Studies in adults have shown that the human brain is intrinsically organized into distinct functional networks [11] , [12] , [13] . Remarkably , intrinsic functional connectivity analysis has identified two distinct neurocognitive networks which are particularly important for implementing dynamic control across a wide range of cognitive tasks: a ‘salience network’ ( SN ) [12] , anchored in the FIC and dorsal ACC , and a dorsal fronto-parietal ‘central executive network’ ( CEN ) anchored in the DLPFC and the supramarginal gyrus within the posterior parietal cortex ( PPC ) [4] , [12] , [14] . In adults the FIC node of the SN has been shown to play a major role in attentional capture , task-switching and generation of control signals that facilitate access to working memory resources necessary for a wide range of cognitive tasks [8] . The FIC consists of at least two cytoarchtectonically distinct regions – the VLPFC and the anterior insula ( AI ) . While the VLPFC has been the focus of many investigations of control [15] , [16] , [17] , there is growing evidence to suggest that the AI , by virtue of its tight coupling with the ACC , plays a critical and distinctive role [8] , [14] , [18] , [19] . Notably , analysis of dynamic causal interactions has suggested that the AI initiates control signals which engage the ACC , DLPFC and PPC while disengaging the default mode network during cognitively challenging tasks [8] . In this study we use a neurocognitive network model based on the SN and CEN for investigating fundamental mechanisms mediating the development of dynamic control processes during cognition . Over the past decade , several studies have examined developmental changes in the recruitment of brain areas belonging to these networks using cognitive tasks ranging from response inhibition , attention , and memory , to decision-making , reasoning and problem solving [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] . Both increased and decreased recruitment of insula-cingulate and fronto-parietal systems have been reported over the course of development [28] . Although developmental neuroimaging studies have provided evidence for immature task-related activation in the VLPFC , AI , ACC , and DLPFC [1] , [20] , [25] , [28] , nothing is currently known about the maturation of dynamic interactions between these brain regions . Based on previous studies which have pointed to developmental changes in activation of areas that overlap with the SN and CEN we hypothesized that a neurocognitive network model would help clarify and significantly enhance our understanding of the mechanisms by which control processes mature in children . A systematic network approach has the potential for providing insights into general development mechanisms mediating dynamic control processes during cognition . However , in both adults and children , the differential role and primacy of control signals has been difficult to disentangle , partly because these areas are typically coactivated during a wide range of cognitive tasks [12] . More specifically , it has been difficult to disambiguate the contributions of multiple overlapping frontal lobe regions using task-based functional magnetic resonance imaging ( fMRI ) . Critically , the SN and CEN are often co-activated during cognitive tasks in children and adults , and isolating focal responses in a consistent manner from task-based fMRI activations is not straightforward . This is especially true in developmental studies since children tend to show more diffuse activations in the prefrontal cortex , making it difficult to disambiguate regional functional cortical responses [29] . To address this issue in a principled manner , we used multimodal imaging combining resting-state fMRI , cognitive task fMRI and DTI to examine developmental changes in dynamic interactions between the SN and CEN during cognition , and the underlying structural connectivity . Resting-state fMRI ( rsfMRI ) data were acquired and used to characterize the SN and CEN and to identify their five major nodes ( SN: AI , VLPFC , ACC and CEN: DLPFC , PPC ) . We demarcated SN and CEN , and their nodes using analysis of rsfMRI data . An arithmetic problem solving task was used to investigate dynamic interactions between the SN and CEN during cognition . The arithmetic task used is easily understood and performed with high levels of accuracy by most 7–9 year old children , and several previous imaging studies have shown that it consistently activates all major nodes of the SN and CEN in both children and adults [28] , [30] . DTI , performed in the same group of children and adults , was used to examine whether maturation of functional interactions between specific brain regions was related to the maturation of white matter pathways that link them . We predicted that the AI node of the SN would be a hub mediating dynamic causal interactions in adults but not in children . We further predicted that , compared to adults , children would have weaker dynamic causal interactions between the SN and CEN , and that weaker causal interactions would contribute significantly to reduced levels of activation as well as lower levels of task performance in children . Linking functional and structural connectivity measures , we predicted that immature causal interactions in children would be reflected in weaker integrity and density of white matter pathways linking key nodes of the SN and CEN . Together , these findings would provide novel information on temporal hierarchy of among prefrontal and parietal regions implicated in control processes [5] , [16] , [17] and for immature fronto-parietal causal control signals in children .
Children ( ages 7–9 ) and adults ( 19–22 ) did not differ on IQ ( p = 0 . 93 ) or gender ( p = 0 . 75 ) ( Table S1 ) . Although both groups performed the arithmetic problem solving task with a high level of accuracy , children were significantly less accurate ( t ( 38 ) = 5 . 54; p<0 . 001 ) and slower ( t ( 38 ) = 10 . 99; p<0 . 001 ) than adults ( Figure S1 ) . The two main networks of interest – SN and CEN – were identified using ICA applied to resting-state fMRI data ( Figure S2 ) . From the SN ICA map , we identified ROIs in the AI , ACC and VLPFC bilaterally . From the right CEN ICA map , we identified ROIs in the right DLPFC and right PPC . From the left CEN , we identified the left DLPFC and left PPC . The anatomical location of these nodes is shown in Figure S2 and Table S2 . Subsequent analyses were based on these five canonical nodes of the SN and right CEN . Our analysis focused primarily on these five right hemisphere ROIs . Additional analyses using ROIs based on regional peaks selected from task-related activation ( Figure S3 ) , and findings from homologous left hemisphere ROIs ( Figure S4 ) and sensory ROIs are described in Supplementary Information ( Text S1 ) . We first examined fMRI responses within the five SN and CEN ROIs during the arithmetic problem solving task . Task-related brain activation was identified using a general linear model with arithmetic problem solving task versus rest/null condition contrast . Only correct trials were included in the analysis . All five right-hemisphere nodes showed significant task-related activation in both children and adults ( Figure 1A , 1B ) . Compared to adults , children showed stronger activation in the rAI ( t ( 38 ) = 3 . 23; p<0 . 01 , FDR corrected ) and weaker activation in the rPPC ( t ( 38 ) = 3 . 41 ; p<0 . 01 , FDR corrected ) ( Figure 1C ) . We then examined differences in functional connectivity between children and adults . Functional connectivity here is measured as instantaneous correlations between pairs of ROIs after removal of drift and physiological noise . We found that rAI connectivity with ACC , rDLPFC , and rPPC , and between the rVLPFC and rDLPFC was significantly greater in adults , compared to children ( p<0 . 01 , FDR corrected ) . No ROI pairs showed greater functional connectivity in children , compared to adults ( p<0 . 01 , FDR corrected ) . We examined differences in the onset latency of the event-related fMRI responses in the five right hemisphere ROIs . We extracted the mean time-course in each ROI , and used a linear basis function that is a combination of the SPM canonical hemodynamic response function and a temporal derivative to fit the event related BOLD response for each subject and event , and then averaged the fitted responses across events and subjects . Onset latencies were then computed as the time point at which the slope of the fitted response reached 10% of its maximum positive ( or negative ) slope in the initial ascending ( or descending ) segment [8] . This analysis revealed that the event-related fMRI signal in the rAI has an onset significantly earlier compared to signals in the rVLPFC , ACC , rDLPFC and rPPC ( p<0 . 01; FDR corrected ) , an effect that was observed in both children and adults ( Figure 2; Figure 3 , Figure S5 ) . The rAI , but not the other four ROIs , had onset latencies significantly earlier in adults , compared to children . We used two different quantitative methods to examine causal interactions in fMRI data . Based on our previous studies , we first used multivariate Granger causal analysis ( MGCA ) [8] to investigate dynamic interactions between all five right hemisphere ROIs . While there are some concerns that systematic differences across brain regions in hemodynamic lag can potentially lead to spurious estimations of causality [31] recent analyses suggest that when applied at the group level , MGCA has good control over spurious results [32] [33] . Our detailed simulations [34] suggest that MGCA is able to recover causal network structure in spite of the presence of HRF delay confounds . In light of these considerations and other recent discussion about the merits and limitations of MGCA [33] , [35] we conducted additional analyses using Multivariate Dynamical Systems ( MDS ) [34] . MDS is a novel state-space model to estimate intrinsic and experimentally-induced modulatory causal interactions between multiple brain regions that overcomes several limitations of existing methods [34] . Briefly , MGCA detects causal interactions between brain regions by assessing the relative prediction of signal changes in one brain region based on the time-course of responses in another . We performed MGCA using a multivariate model on the time-courses extracted from each of the ROIs . We used bootstrap techniques to create null distributions of influence terms ( F-values ) and their differences . In children , MGCA revealed statistically significant direct causal influences from the rAI to the rVLPFC , ACC , rDLPFC , and rPPC ( Figure 4A ) . In adults , MGCA revealed causal influences from the rAI to these same regions ( Figure 4B ) . Quantitative comparison of the strength of causal influences revealed that the strength of interactions from the rAI to rPPC was significantly greater in adults , compared to children ( p<0 . 01; FDR corrected ) , as shown in Figure 4C . No links showed reduced causal influence in adults , compared to children . An identical set of analyses were conducted using MDS methods which have the advantage of modeling causal interactions in the latent “neuronal” signals , rather than in the fMRI signal itself . Furthermore , MDS also takes into account inter-regional variations in hemodynamic response in an explicit manner [34] . This analysis confirmed results from the MGCA and demonstrate the robustness of our findings ( Figure 4D , E , F ) . To quantify the causal interactions of each node of the network , we performed graph-based network analyses . Analysis of the causal network identified with MGCA revealed that the rAI had the highest number of causal outflow connections ( out-degree ) , the lowest number of causal inflow connections ( in-degree ) , and the shortest path length among all regions . The rAI also had a significantly higher net causal outflow ( out-in degree ) than all of the other regions ( p<0 . 05; FDR corrected ) . These results were observed in both children and adults , suggesting that the rAI is an outflow hub in both groups ( Figure 5A , Figure 5B ) . There were no group differences in the node-wise net causal outflow nor path length between the groups . Similar graph-based analyses were conducted on causal network identified using MDS . This analysis gave results that were identical to those observed from the MGCA and demonstrate the robustness of our findings ( Figure 5C , 5D ) . We used DTI and quantitative tractography to investigate the anatomical correlates of developmental changes in causal interactions between the rAI and rPPC . The density of fibers along the superior longitudinal fasciculus linking the rAI and rPPC was significantly lower in children compared to adults ( p<0 . 01 ) , as shown in Figure 6 . Mean fractional anisotropy ( FA ) of rAI-rPPC tracts was also significantly lower in children ( p<0 . 01 ) . The rAI-rPPC fibers observed here have been previously identified to be part of the third component of the superior longitudinal fasciculus ( SLF III ) which connects the rostral part of the inferior parietal lobule with the lateral inferior frontal lobe [36] . Visual inspection of individual subject rAI-PPC SLF III fibers suggest that tracts emanating from magno-cellular supramarginal area ( PFm ) [37] connect to the mid/posterior aspect of the insula . In the absence of anatomical atlases that clearly demarcate the subregions of insula , a more definitive statement on the exact trajectories of the insula-parietal fibers would require future studies that demarcate insula subregions based on parcellation techniques [38] , [39] , [40] , [41] , [42] , [43] , [44] . We compared the relationship between functional and structural connectivity between the rAI to rPPC in children and adults . We found that functional connectivity , measured by instantaneous temporal correlations , and structural connectivity , measured by fiber density , between the rAI to rPPC was significantly correlated in adults ( r = 0 . 44; p<0 . 05 ) but not in children ( r = 0 . 02; p = 0 . 9 ) , as shown in Figure 7 . Similarly , causal connectivity , measured by causal influence terms were correlated with structural connectivity , measured by fiber density in adults ( r = 0 . 23; p<0 . 05 ) but not in children ( r = 0 . 06; p = 0 . 78 ) . We used multivariate sparse regression analysis , based on GLMnet [43] , to investigate causal network interactions which collectively predict behavior . Causal functional connectivity strength between brain regions was used as predictor variables and either reaction time or accuracy was used as the dependent variable .
The rAI showed strong causal influences on the ACC node of the SN and the DLPFC and PPC regions of the CEN in children and adults , suggesting that the role of the rAI as a primary node that drives the CEN is established early in development . Two additional analyses were performed to confirm these findings . First , we used a novel state-space MDS model , which estimates causal interactions in latent neuronal signals , rather than the recorded fMRI signals , after taking into account inter-regional variations in hemodynamic response [34] . This analysis confirmed findings based on the MGCA that the rAI has significant causal interactions with several other nodes of the SN and the CEN ( Figure 4 ) . Second , a completely different analysis based on the temporal profile of event-related fMRI responses revealed that , in both children and adults , the rAI had the shortest onset latency of all brain regions examined . Graph-theoretic analyses using causal connectivity patterns estimated by MGCA and MDS confirmed that the rAI had the highest net causal outflow and shortest path length of all nodes examined in this study . By definition , nodes which have a higher number of outgoing edges and the shortest path from all other nodes in a graph are referred to as hubs and are thought to play a key role in coordinating information flow [45] . Together , these findings provide converging evidence that the rAI is established as a major causal outflow hub by age 9 . Our findings further suggest a novel pattern of temporal hierarchy among prefrontal and parietal regions implicated in control processes [5] , [16] , [17] . The rAI emerged as a major source of signals to attentional and working memory systems anchored in the ACC , DLPFC and PPC . Additional follow-up exploratory latency and causal analyses including a sensory ROI along with SEN and CN ROIs suggest that the rAI receives weak input from the sensory cortices , which it further amplifies by exerting top-down control on attentional and working memory systems ( see Supplementary Text S1 for details ) . Critically , our findings indicate that the rAI is the initial locus of control signals , as revealed by converging evidence from causal analysis of ongoing task activity and by onset latency analysis . These results are consistent with previous findings in adults performing auditory and visual attention tasks [8] , and extend them to higher-order cognition for the first time in both children and adults . Taken together , these findings suggest that the rAI plays a crucial role as a hub that initiates key control signals during higher-order cognition not only in adults but also in children as young as 9 years of age . An important novel finding of our study is that the strength of causal influence from rAI to rPPC was significantly weaker in children , compared to adults . Notably , this group difference was observed using both MGCA and MDS , two different and complementary methods for estimating causal interactions in fMRI data . In addition to differences in the causal interaction between the rAI and rPPC , MDS analysis revealed that the strength of causal influence from rAI to ACC was also significantly weaker in children . Here , we focus on the convergent findings from the two analyses on developmental differences in the causal link from the rAI to the rPPC . The only previous study to have examined developmental changes in causal interactions during cognition did not examine rAI connectivity and no connectivity differences were reported between the extended FIC or any regions of the inferior frontal gyrus with the PPC [46] . In parallel with the causal interactions differences observed in our study , instantaneous functional connectivity between rAI and rPPC regions was also weaker in children . Critically , weak control signals from rAI predicted lower rPPC activation in children . Children showed higher rAI activation and lower rPPC responses than adults , suggesting that the strength of causal interactions from the rAI , rather than overall signal level , is more important for regulating rPPC responses . The PPC node of the CEN examined here was anatomically localized to the supramarginal gyrus , part of the posterior association cortex that helps to maintain task-related representations in working memory during problem solving [47] , [48] . In particular , arithmetic problem solving tasks involve dynamic integration of symbolic information within working memory [49] , [50] , [51] , and the right supramarginal gyrus is consistently activated during tasks involving visuo-spatial working memory in both children and adults . Right supramarginal gyrus involvement in working memory is also known to undergo protracted developmental changes from childhood to adulthood [24] , [25] , [52] . Taken together , these findings suggest weak signaling within the fronto-parietal nodes of the CEN in children negatively impacts the ability to maintain task-relevant representations needed for achieving adult-like levels of performance . Our results extend previous research by showing for the first time that both causal and instantaneous cross-network connectivity is immature in children and that the rAI is a major locus of immature cross-network frontal control signals to the parietal cortex . Multimodal analysis of fMRI and DTI data revealed that functional connectivity differences between the rAI and rPPC were associated with weak structural links between these areas . Quantitative DTI-based tractography showed that the density of white-matter fiber tracts connecting the rAI and rPPC was significantly lower in children , compared to adults . This result is consistent with previous studies showing slow maturation of long-distance white matter tracts [53] , [54] , including those linking prefrontal and posterior parietal cortices [55] , [56] , [57] . We also found that children had lower FA along tracts linking the rAI and rPPC , indicating slow development of microstructural integrity of white matter . Thus , both functional and structural connectivity between rAI and rPPC is significantly weaker in children compared to adults . Notably , we found that both causal and instantaneous measures of functional connectivity between these regions were correlated with structural connectivity in adults . No such relation was observed in children suggesting that function-structure relationships between the rAI and rPPC become more stable with development , consistent with previous evidence of similar patterns of function-structure relationships [58] . Our findings provide the first direct evidence that the development of structural connectivity between the rAI and rPPC may play an important role in the maturation of fronto-parietal control signals . As noted above , children were significantly slower and less accurate than adults . We examined whether this behavioral difference was the result of weak network interactions . We found that the strength of causal network interactions collectively were strongly predictive of reaction times; in contrast , the rAI→rPPC link by itself was only weakly correlated with response latency in children and adults . Using multivariate sparse regression analysis , we found that network interactions better predicted reaction time in both children and adults . In children , the strength of rAI→rPPC along with rAI→rVLPFC collectively predicted reaction times , while in adults the strength of rAI→rPPC along with rAI→ACC and rAI→rDLPFC collectively predicted reaction times . It is noteworthy that even though a different set of links predicted reaction times in both groups , the rAI→rPPC link was common to both . We also found that reaction times were better predicted in adults , compared to children . These results suggest that it is the multiple network interactions as a whole , rather than individual links by themselves , that moderate performance . Critically , similar results were observed when accuracy instead of reaction time was used as the performance measure . Thus , casual interactions between the rAI and rPPC are an important factor for mediating performance improvements in higher-order cognition with development . It is noteworthy that the rAI showed the strongest causal signals , even though the rVLPFC has been most commonly implicated in control [10] . Previous studies , have not however , directly examined causal influences from these two distinct regions of the FIC , and quite often have mislabeled what are clearly AI activations as VLPFC or IFG . To address this issue , in the present study , we separated the FIC into two distinct nodes , one centered in the rAI and the other in the rVLPFC . Our analysis showed that the rAI has an earlier onset and a stronger causal influence on other nodes than the rVLPFC . These results show unequivocally that the AI has strong causal influences on the rVLPFC , reiterating its role as a principal source of prefrontal control signals that precedes the rVLPFC . Furthermore , onset latencies did not significantly differ between rVLPFC and rPPC , although they were significantly different between rAI and rVLPFC and between rAI and rPPC . These results provide further evidence for the primacy of control signals from the rAI , and suggest that systems involved in detecting saliency [12] also play an important role in control . Our findings are consistent with the hypothesis that the rAI plays a more primal role in initiating control signals [18] . We propose that in young children , as in adults , the rAI is critically involved in attentional capture , task-switching and generation of control signals that facilitate access to working memory resources necessary for cognition . From a neurodevelopmental point of view , it is noteworthy that such a control system is already in place by age 9 , even though the forward causal paths are not fully mature . Further research is needed to clarify the extent to which these findings hold for other cognitive domains such as response inhibition . Efficient control requires the concerted coordination between multiple brain regions and there is growing evidence to suggest that this is implemented via dedicated neurocognitive networks [3] . In this study , we used a neurocognitive model for examining the role of key nodes within the insula-cingulate SN and fronto-parietal CEN in fundamental control processes . Importantly , the key nodes of these networks were determined independently using task-free rsfMRI data . The locations of the five major nodes were virtually identical in children and adults and all five right hemisphere frontal and parietal ROIs in the SN and CEN showed significant task-related activation in both groups . The profile of causal interactions observed in our study is particularly noteworthy because the CEN and SN are often co-activated during cognitive tasks in children and adults , and isolating focal responses in a consistent manner from task-based fMRI activations is not straightforward . This is especially true in developmental studies since children tend to show diffuse activations in the prefrontal cortex , making it difficult to disambiguate functional interactions within this cortical region [29] . To circumvent this problem we demarcated specific networks and their nodes using intrinsic connectivity analysis of rsfMRI data [8] . In principle , the nodes of the two networks , and in particular , the AI and PPC could have been chosen in several different ways . Indeed , multiple additional analyses with alternate choices of brain regions demonstrated the same pattern of the results reported here . The network perspective allows us to not only examine developmental changes using a principled approach for characterizing brain systems but also has the advantage of integrating the present study with an emerging literature on insula-cingulate and fronto-parietal circuits involved in fundamental aspects of control in the human brain [4] , [8] , [18] . A unified network approach – wherein we first specify intrinsic brain networks using rsfMRI data and then analyze interactions among anatomically discrete regions within these networks during cognitive information processing – enhances our ability to generalize beyond individual task activated foci and also provides a common framework for comparing brain response and connectivity in children and adults . Our findings are likely to have important implications for understanding the development of control mechanisms subserved by dynamic interactions between neurocognitive networks . Further studies are needed to examine whether similar control mechanisms underlie the functional maturation of specific cognitive processes involving inhibition , memory and decision-making . The quantitative approach developed here is likely to be useful in the investigation of neurodevelopmental disorders , such as autism and attention deficit/hyperactivity disorder , in which control processes are impaired .
Twenty-three children and twenty-two IQ-matched adults participated in this study after providing written informed consent . For those subjects who were unable to give informed consent , written , informed consent was obtained from their legal guardian . The study protocol was approved by the Stanford University Institutional Review Board . Children ( 10 males , 13 females ) ranged in age from 7 to 9 ( mean age 7 . 95 ) with an IQ range of 88 to 137 ( mean IQ: 112 ) . Adults ( 11 males , 11 females ) ranged in age from 19 to 22 ( mean age 20 . 4 ) with an IQ range of 97 to 137 ( mean IQ: 112 ) . The fMRI experiment consisted of 52 arithmetic problems presented in a jittered event-related design along with “rest” or “null” trials in which participants passively viewed a cross on the screen . In the arithmetic trials , participants were presented with an equation involving two addends and a resultant and were asked to indicate via a button box whether the resultant was correct or incorrect . Half the addition trials consisted of problems with addends different from ‘1’ ( e . g . 3+4 = 7 ) . One operand ranged from 2 to 9 , the other from 2 to 5 ( tie problems such as ‘5+5 = 10’ , were excluded ) , and resultants were correct in 50% of the trials . Incorrect answers deviated by ±1 or ±2 from the correct sum . The other half of the addition trials had the same format but one addend was ‘1’ ( e . g . 5+1 = 7 ) . Stimuli were displayed for 5 seconds with an inter-trial interval of 500 msec followed by a blank screen for 500 msec and an inter-trial jitter that varied between 0 to 3500 msec with an average duration of 1814 msec . Each subject underwent a math task scan and 8-min resting-state scan . fMRI data acquisition , preprocessing , analysis of task data with General Linear Model ( GLM ) , Independent component analysis ( ICA ) of resting data , and functional connectivity analysis procedure is described in detail in Supplementary Information ( Text S1 ) . Here we describe methods specifically related to analysis of causal interactions . DTI data was obtained from 18 of the 23 children subjects and 15 of 22 adults . Acquired images underwent the following preprocessing steps: eddy-current correction , alignment with T1-weighted anatomical images , resampling , and tensor computation . Fiber tracts between the rAI and rPPC were computed as previously described [58] . For each subject , the density and mean fractional anisotropy of the fibers connecting the rAI to the rPPC was measured , in native space ( see Supplementary Text S1 for details ) . In this study , we used fiber density and mean FA as measures of the integrity of the fiber tracts of interest .
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The human brain undergoes significant maturational changes between childhood and adulthood that are thought to enable increasingly sophisticated thoughts and behaviors . One of the skills that we develop over time is the ability to problem solve , which relies in turn on the ability to control our attention and successfully direct our cognitive efforts . Using a novel multi-pronged neuroimaging approach , we identify for the first time the dynamic brain systems underlying the maturation of problem solving abilities . We find that the anterior insula , part of a larger network of regions previously shown to be important for salience processing and generating influential control signals , shows weaker influences over other key brain regions important for task performance in children compared to adults . In addition , structural connections between the anterior insula and other key regions were found to be weaker in children compared to adults . Importantly , measures of causal influences between key regions could be used to predict individual differences in behavioral performance . Our study is the first to show that the anterior insula , by virtue of its dynamic influences on other key brain regions , shows developmental differences in both structural and functional connectivity , which may contribute to more mature cognitive abilities in adulthood compared to childhood .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"cognitive",
"neuroscience",
"psychology",
"neuroanatomy",
"mental",
"health",
"signal",
"processing",
"biology",
"neuroscience",
"neuroimaging",
"engineering"
] |
2012
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Developmental Maturation of Dynamic Causal Control Signals in Higher-Order Cognition: A Neurocognitive Network Model
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The consumption of a vertebrate blood meal by adult female mosquitoes is necessary for their reproduction , but it also presents significant physiological challenges to mosquito osmoregulation and metabolism . The renal ( Malpighian ) tubules of mosquitoes play critical roles in the initial processing of the blood meal by excreting excess water and salts that are ingested . However , it is unclear how the tubules contribute to the metabolism and excretion of wastes ( e . g . , heme , ammonia ) produced during the digestion of blood . Here we used RNA-Seq to examine global changes in transcript expression in the Malpighian tubules of the highly-invasive Asian tiger mosquito Aedes albopictus during the first 24 h after consuming a blood meal . We found progressive , global changes in the transcriptome of the Malpighian tubules isolated from mosquitoes at 3 h , 12 h , and 24 h after a blood meal . Notably , a DAVID functional cluster analysis of the differentially-expressed transcripts revealed 1 ) a down-regulation of transcripts associated with oxidative metabolism , active transport , and mRNA translation , and 2 ) an up-regulation of transcripts associated with antioxidants and detoxification , proteolytic activity , amino-acid metabolism , and cytoskeletal dynamics . The results suggest that blood feeding elicits a functional transition of the epithelium from one specializing in active transepithelial fluid secretion ( e . g . , diuresis ) to one specializing in detoxification and metabolic waste excretion . Our findings provide the first insights into the putative roles of mosquito Malpighian tubules in the chronic processing of blood meals .
The Asian tiger mosquito Aedes albopictus is considered one of the most invasive mosquito species in the world; since 1979 it has spread to over 28 countries outside of its native range in Asia and Southeast Asia , aided by the international trade of used automobile tires [1] , [2] . Within the United States , the mosquito has spread to at least 36 states and models of its potential for range expansion in the northeastern United States within the next few decades are alarming [3] . Moreover , this species is a known or suspected vector of several medically important arboviruses , including chikungunya , dengue , eastern equine encephalitis , La Crosse , West Nile , and yellow fever [4] . Thus , A . albopictus is an emerging threat to global health for which effective control measures need to be developed . Historically , mosquitoes have been controlled through the use of insecticides that target the nervous system ( e . g . , carbamates , organophosphates , organochlorines , and pyrethroids ) . However , resistance to these control agents is limiting their efficacy . In particular , the yellow fever mosquito Aedes aegypti exhibits high levels of resistance to insecticides in certain parts of the world , and there is concern that A . albopictus will soon develop such resistance [5] . Thus , it is important to identify new control agents that target novel physiological systems in mosquitoes to help combat the emerging threat of insecticide resistance . A recent study by our group demonstrated that the renal excretory system ( Malpighian tubules ) of mosquitoes represents a valuable new physiological target for insecticides [6] . The Malpighian tubules produce urine via transepithelial fluid secretion , which is mediated by the coordinated actions of a V-type H+-ATPase along with several ion transporters , ion channels , and water channels [7] . In adult female A . aegypti mosquitoes , the Malpighian tubules play an especially important role in the post-prandial diuresis when the mosquito excretes urine during and after the engorgement of vertebrate blood [8] . The diuresis lasts for up to two hours after feeding and excretes a significant fraction of the ingested Na+ , K+ , Cl− , and water from the blood [8] . Once this diuresis ends , the female mosquito will continue to digest and metabolize the blood meal over the next two days to nourish the development of her eggs . The physiological importance of the Malpighian tubules during this time is unknown , but they presumably play a critical role in excreting excess nitrogenous wastes and other metabolites that are generated during the processing of the protein-rich meal [9] , especially within the first 24 hours when ∼75–90% of the ingested protein is digested [10] , [11] . Other groups have documented the effects of ingesting blood on the transcriptomes of adult female A . aegypti and Anopheles gambiae mosquitoes [12] , [13] , [14] , [15] , [16] , including more focused studies on how blood feeding influences tissue-specific transcriptomes in the antennae , fat body , midgut , and salivary glands of these species [17] , [18] , [19] , [20] . However , no previous studies have examined the effects of blood-feeding on the transcriptome of mosquito Malpighian tubules . The goal of the present study was to characterize the global changes in transcript expression that occur in the Malpighian tubules of A . albopictus during the first 24 h after female mosquitoes consume a blood meal ( using RNA-Seq ) , with the aim of identifying key metabolic pathways and transcripts that are activated or suppressed in the renal tubules during the processing of the blood meal . We found that blood feeding elicits dramatic , time-dependent changes to the Malpighian-tubule transcriptome of A . albopictus . A functional cluster analysis of the differentially-expressed transcripts revealed a potential functional transition of the tubule epithelium after blood feeding from one specializing in active transepithelial fluid secretion to one specializing in detoxification and metabolic waste excretion .
A . albopictus eggs were obtained from the Malaria Research and Reference Reagent Resource Center ( MR4 ) as part of BEI Resources Repository , NIAID , NIH ( ALBOPICTUS , MRA-804 , deposited by Sandra Allan ) . Eggs were raised to adults using a protocol similar to that described for A . aegypti [21] with the exception that larvae were fed pulverized TetraMin flakes ( Melle , Germany ) . Adult females between 5 to 10 days post-eclosion were used for the present study . The experimental design consisted of two treatments , blood fed ( BF ) and non-blood fed ( NBF ) females at three different time points . In brief , the BF mosquitoes were fed on heparinized rabbit blood for 30 minutes ( see details below ) and collected at 3 h , 12 h , or 24 h after feeding . These time points occur after the post-prandial diuresis , which ends within 2 h after feeding [8] . Moreover , one or more of these time points has been commonly used in other studies examining the effects of blood feeding on gene expression in mosquitoes [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [22] . NBF females were only offered a 10% sucrose solution and dissected at similar time points to serve as controls . Each treatment/time point was replicated three times using females from different cohorts ( i . e . , 3 biological replicates per time point ) . For each experimental treatment , 90 adult female mosquitoes were transferred to two small 32 oz . cages ( 45 females per cage ) without access to a sucrose solution for 24 h prior to offering them blood or sucrose . To one cage , a membrane feeder ( Hemotek , Blackburn , UK ) was used to feed the mosquitoes warmed blood ( 37°C ) , which consisted of heparinized rabbit blood ( purchased from Hemostat Laboratories , Dixon , CA ) supplemented with adenosine 5′-triphosphoric acid ( disodium salt; Sigma , St . Louis , MO ) at a concentration of 0 . 01 g/ml . A solution of 10% lactic acid was applied to the membrane surface as an attractant . Females were given access to the blood for a period of 30 min before the feeder was removed from the cage . In the other cage , the mosquitoes were given access to cotton balls soaked with 10% sucrose for 30 min . At 3 h , 12 h , or 24 h after removing the feeder or cotton balls , the cage was refrigerated on ice to immobilize the mosquitoes . Before dissecting the mosquitoes that were offered blood , their abdomens were visually examined to confirm their engorgement . The alimentary canal of each mosquito was then extracted by tugging on the last segment of the abdomen with fine forceps under Ringer solution . The Ringer solution consisted of ( in mM ) : 150 NaCl , 3 . 4 KCl , 1 . 7 CaCl2 , 1 . 8 NaHCO3 , 1 . 0 MgCl2 , 5 glucose , and 25 HEPES ( pH 7 . 1 ) . The Malpighian tubules were isolated from their attachment to the alimentary canal and immediately immersed in 50 µL of TRIzol Reagent ( Life Technologies , Carlsbad , BA ) in a sterile 1 . 5 ml microcentrifuge tube on ice . A total of 200 Malpighian tubules ( from 40 females ) were pooled for a given replicate . Altogether , Malpighian tubules were isolated from 1 ) mosquitoes fed a blood meal at 3 different time points ( 3 h , 12 h , 24 h; 40 mosquitoes at each time point ) and 2 ) mosquitoes not fed a blood meal at similar time points ( 40 mosquitoes each ) . A total of 3 biological replicates was obtained for each time point in both the BF and NBF groups , resulting in 18 sets of tubules for RNA isolation and cDNA library preparation ( triplicates each of 3 h BF , 12 h BF , 24 h BF , 3 h NBF , 12 h NBF , and 24 h NBF ) . Total RNA was extracted from each set of Malpighian tubules immediately after they were isolated from the mosquitoes using the method of Chomczynski and Sacchi [23] . The resulting RNA was treated with TURBO DNA-free ( Life Technologies ) to remove genomic DNA and then purified with a RNA Clean & Concentrator-5 kit ( Zymo Research , Irvine , CA ) , according to the manufacturer's protocol . The purified RNA was initially measured for quantity and quality using a NanoDrop 2000c Spectrophotometer ( Thermo Fisher Scientific , Waltham , MA ) . Samples with a concentration <20 ng/µl or poor absorbance ratios ( i . e . , 260/280 value <1 . 6; 260/230 value <1 . 6 or >3 . 0 ) were discarded . RNA quality was further assessed using the Experion Automated Electrophoresis System ( Bio-Rad , Hercules , CA ) . Only samples with RNA Quality indicator values of 7 . 5 or higher were used . The concentration of RNA was determined with a Qubit 2 . 0 Fluorometer ( Life Technologies ) . Total RNA ( 565 ng ) was used to synthesize adaptor-indexed double-stranded cDNA libraries using the TruSeq DNA Sample Prep Kit V2 , Set A and B ( Illumina , San Diego , CA ) . The size chosen for libraries was ∼270 bp . The quality of the synthesized libraries was evaluated using the Agilent 2100 Bioanalyzer High Sensitive DNA Chip ( Agilent Technologies , Santa Clara , CA ) and the quantity determined using the Qubit 2 . 0 Fluorometer ( Life Technologies ) . The 18 resulting cDNA libraries were diluted to 18 nM and pooled to generate a multiplexed cDNA library ( using 18 unique indexed adapters ) of 36 fM . The pooled library was sequenced using the Illumina HiSeq 2000 platform at the Ohio State University Comprehensive Cancer Center ( Columbus , OH ) . Demultiplexing was performed with CASAVA 1 . 8 . 2 . FASTQ files were generated from the ‘basecall’ files . All single-end reads were submitted to the NCBI sequence read archive ( accession number SRP034701 ) . The sequencing of all 18 libraries generated over 232 million single-end raw reads ( ∼13 million reads per sample ) ( Table S1 ) . The MCIC-Galaxy pipeline was implemented for preprocessing , filtering , and data analysis [24] . The raw reads were first analyzed with the “FASTQC” tool ( http://www . bioinformatics . babraham . ac . uk/projects/fastqc/ ) to assess quality . Adapters were removed using CUTADAPT [25] with an error rate of 0 . 1 and a minimum overlap length of 6 . Reads were trimmed for length and quality using the “Trim the reads” tool , version 1 . 2 . 2 , ( Phred threshold score <20; read length <20 bp ) , discarding all miscalled bases , but not duplicates or polyA tails , because reads were aligned on a reference transcriptome . The number of reads retained after removing adapters , low quality , and short reads was >194 million , which is ∼84% of the total number of original 232 million reads . After the preprocessing and filtering , reads were aligned to the following two reference transcriptomes using “Burrow-Wheeler Aligner” , version 1 . 2 . 3 [26]: 1 ) A . aegypti ( www . vectorbase . org; AaegL1 . 4 , v1 . 00; 18 , 769 sequences ) [27] and 2 ) A . albopictus ( www . ncbi . nlm . nih . gov;transcript shotgun archive accession numbers JO845359-JO913491 , 68 , 413 sequences ) [28] . The number of reads that aligned to unique and redundant transcripts in the reference transcriptome was determined for each sample using “Count Features” tool , version 0 . 91 . The dataset was filtered to contain only transcripts with a minimum of five mapped reads for any two replicates in at least one treatment/time combination . Only the subset of transcripts meeting this criterion was used in subsequent analyses . Considerably more reads mapped onto the A . albopictus transcriptome ( ∼9 . 6 million reads/sample; 75% reads mapped ) compared to the A . aegypti transcriptome ( ∼2 . 4 million reads/sample; 18 . 5% reads mapped ) . However , the A . albopictus transcriptome assembly and annotation is incomplete and contains redundancies ( i . e . , there are several transcript identification numbers corresponding to the same transcript ) , which limited our ability to accurately identify and quantify specific individual transcripts of interest ( e . g . , aquaporins , NH3 detoxification enzymes ) . Moreover , mapping onto the A . aegypti transcriptome resulted in the detection of 9 , 813 non-redundant transcripts , which is within the range of ∼7 , 000–18 , 000 transcripts detected in RNA-seq studies of A . aegypti isolated tissues or whole animals [12] , [17] , [29] , [30] . Thus , we use the A . aegypti transcriptome and its transcript nomenclature for our downstream analyses . For quality assessment , “Count Features” output was implemented to examine the dispersion of the biological replicates ( libraries per treatment ) . Counting reads were normalized calculating the RPKM ( reads per kilobase per million reads ) . Pearson's correlation and Principal Component Analysis graphics were generated to assess the similarity of the replications for each condition . The read counts generated using “Count Features” output were submitted to the “DESeq” pipeline , version 1 . 0 . 0 [31] , to identify transcripts with a significant differential expression between treatments/time points . This tool is based on the negative binomial distribution , with normalized libraries by size factor ( developing an estimate effective library size ) [31] . The comparisons made were a NBF treatment against BF treatments at three time points ( 3 h , 12 h and 24 h ) . All of the transcripts with a significant expression change were filtered by a FDR-adjusted P value threshold of 0 . 05 , and their log2 fold-change value was recorded . The DAVID v6 . 7 annotation clustering module [32] was used to classify differentially expressed transcripts into functional groups . Clustering analysis was carried out for the subset of transcripts that had showed sustained up- or down-regulation ( i . e . , at least two consecutive time points of differential expression ) . The DAVID is currently not compatible with A . aegypti transcript IDs . Thus , the differentially-expressed transcripts were first converted to A . gambiae transcript IDs using tBLASTx ( E-value <10−20 ) . Then , enrichment of GO and other annotation terms in candidate sub-lists were explored using the functional annotation clustering tool . This clustering method condenses the input transcript list into functionally related transcripts ( annotation clusters ) , taking into account the similarity of their annotation profiles based on multiple annotation sources ( e . g . GO terms and Interpro keywords ) . The clusters are assigned an enrichment score , which represents the minus log-transformed geometric mean of the modified Fisher Exact ( EASE ) Scores within the cluster . Significantly enriched annotation clusters were defined as those containing a minimum enrichment score of 1 . 3 , because the –log ( 0 . 05 ) = 1 . 3 . Thus , an enrichment score of >1 . 3 corresponds to a P<0 . 05 . The enrichment score is based on the following parameters: Similarity Term Overlap = 3; Similarity Threshold = 0 . 7; Initial Group Membership = 3; Final Group Membership = 5; Multiple Linkage Threshold = 0 . 3 . Once the significantly-enriched functional clusters were identified , the A . gambiae transcript IDs within them were converted back to their respective A . aegypti transcript IDs . We also performed a preliminary DAVID clustering analysis using reads mapped to the existing A . albopictus reference transcriptome , despite its incomplete assembly and annotation . Notably , there was a good correlation between the enriched functional clusters identified using this reference and those using the A . aegypti reference . For example , the thioredoxin , glutathione S-transferase , vitamin binding , cofactor metabolic process , oxidative phosphorylation/ATP synthesis , protein biosynthesis , glycoylsis , and ATPase activity clusters were enriched in both DAVID analyses . Thus , our decision to use the A . aegypti transcriptome , which allows for better identification and quantification of specific transcripts ( see above ) , does not compromise our ability to identify enriched functional pathways .
DESeq was used to search for transcripts differentially expressed between the 24 h NBF control and the BF treatments at each time point . Using the A . aegypti transcriptome as a reference , a total of 1 , 857 non-redundant transcripts was found to be differentially expressed over all of the time points ( ∼10% of the A . aegypti transcriptome ) . Table 1 shows that the Malpighian tubules from the BF mosquitoes were characterized by progressive increases in differential expression throughout the time series . At each time point , the differentially expressed transcripts consist of similar numbers of up- and down-regulated transcripts . We next aimed to identify enriched , functional pathways within the differentially expressed transcripts using a DAVID functional clustering analysis [32] , [33] . We focused our analysis on transcripts that exhibited ‘sustained’ changes in differential expression after blood feeding , which we defined as those significantly up- or down-regulated for at least two consecutive time periods . Based on the A . aegypti transcriptome , a total of 669 transcripts met our ‘sustained’ criterion , consisting of 340 up-regulated transcripts and 329 down-regulated transcripts . As shown in Table 2 , the DAVID analysis revealed a significant enrichment ( enrichment score > 1 . 3 ) of 1 ) nine functional groups among the sustained , up-regulated transcripts and 2 ) six functional groups among the sustained , down-regulated transcripts . The identities of the transcripts that comprise the functional groups of Table 2 and their respective heat maps of differential expression are shown in Figures S1–S15 . Cursory interpretations of the changes to these broad functional groups and the transcripts within them suggests that blood feeding promotes the expression of transcripts associated with 1 ) antioxidants and detoxification , 2 ) proteolytic activity , 3 ) amino acid metabolism , and 4 ) cytoskeletal dynamics . On the other hand , blood feeding appears to suppress the expression of transcripts associated with 1 ) oxidative metabolism , 2 ) active transport , and 3 ) mRNA translation . Below , we discuss these interpretations in more detail with the caveat that transcript levels may not necessarily reflect protein abundance , biochemical activity , or physiological function . Thus , we consider our interpretations as the building of hypotheses that will require testing in future studies using functional genetic , biochemical , and physiological techniques . The mosquito Malpighian tubule epithelium is well-studied , because of its remarkable capacity for active transepithelial fluid secretion , which mediates the post-prandial diuresis . The vacuolar ( V-type ) H+-ATPase is the ultimate energizer of transepithelial fluid secretion in the epithelium [34] . This proton pump resides in the luminal brush border of principal cells where it is situated in close proximity to mitochondria that fuel the pump with ATP [35] , [36]; the pump is a multisubunit protein consisting of two sectors: 1 ) a catalytic , cytosolic V1 sector and 2 ) a H+-translocating , membrane-bound V0 sector [37] . Inhibiting the pump or the production of ATP in the epithelium effectively inhibits fluid secretion [34] , [38] . Thus , the enrichment of the ‘oxidative phosphorylation/ATP synthesis’ , ‘ATPase activity’ , ‘glycolysis’ , and ‘sugar/inositol transporter’ functional clusters among the transcripts that exhibited a sustained down-regulation ( Table 2 ) caught our attention . Listed prominently among the down-regulated transcripts in the ‘oxidative phosphorylation/ATP synthesis’ ( Figure S1 ) and ‘ATPase activity’ ( Figure S2 ) functional clusters are those encoding subunits of the V-type H+-ATPase . Remarkably , as shown in Figure 2 , fourteen transcripts encoding subunits of the V-type H+-ATPase exhibit a sustained down-regulation after blood feeding , while only one shows a sustained up-regulation ( AAEL003743-RA ) . Furthermore , a manual search of all the differentially-expressed transcripts ( including those not considered ‘sustained’ ) revealed three other transcripts associated with the V-type H+-ATPase that are down-regulated transiently at one or two non-consecutive time points ( i . e . , AAEL002464-RA , AAEL010819-RA , AAEL010819-RB in Figure 2 ) . Among all of these down-regulated transcripts , nine encode subunits of the V1 sector of the V-type H+-ATPase , seven encode subunits of the V0 sector , and one encodes an accessory protein ( Figure 2 ) . The only up-regulated transcript encodes subunit ‘a’ of the V0 sector ( a . k . a . vha100-1 ) . The above changes to transcripts of the V-type H+-ATPase in the Malpighian tubules of A . albopictus contrast with those previously reported in the midgut of A . aegypti after blood feeding [19] . In our study , nearly all subunits of the V-type H+-ATPase exhibited a down-regulation in Malpighian tubules after blood feeding ( Figure 2 ) , whereas Sanders et al . found that transcripts encoding V-type H+-ATPase subunits exhibited an up-regulation in the midgut of A . aegypti at 12 h and 24 h after blood feeding [19] . These contrasting results suggest that the regulation of V-type H+-ATPase expression in response to blood feeding is tissue dependent in mosquitoes . Notable among the transcripts that exhibited a sustained down-regulation after blood feeding in the ‘oxidative phosphorylation/ATP synthesis’ ( Figure S1 ) and ‘glycolysis’ ( Figure S3 ) functional clusters are those encoding enzymes associated with glycolysis , the citric acid cycle , and ATP synthesis , such as phosphofructokinase , pyruvate kinase , enolase , 2-oxoglutarate dehydrogenase , glycerol-3-phosphate dehydrogenase , and acetyl-CoA synthetase . In addition , within the ‘oxidative phosphorylation/ATP synthesis’ ( Figure S1 ) and ‘sugar/inositol transporter’ ( Figure S4 ) functional clusters are transcripts encoding putative SLC2-like sugar transporters , which import glucose into cells for use as a fuel to generate ATP . Thus , in Malpighian tubules , blood-feeding leads to a decrease in the abundance of transcripts encoding enzymes associated with the catabolism of glucose and synthesis of ATP , which is consistent with the aforementioned decrease in abundance of transcripts encoding subunits of the V-type H+-ATPase subunits . Also notable among the transcripts listed in the ‘oxidative phosphorylation/ATP synthesis’ ( Figure S1 ) functional cluster are those encoding ion transport mechanisms that are known or hypothesized to play a role in the transepithelial secretion of ions by mosquito Malpighian tubules . We discuss these mechanisms below . The other functional clusters enriched among the transcripts that exhibited a sustained down-regulation are related to the translation of mRNA ( i . e . , protein biosynthesis and translational elongation; Table 2 ) . These transcripts consist primarily of ribosomal protein subunits and translation/elongation factors that exhibit a down-regulation at 12 h and 24 h after a blood meal ( Figure S5 and Figure S6 ) , which suggests that most newly-translated proteins in the tubules in response to blood feeding are synthesized within 12 h . Furthermore , the data suggest that during the chronic processing of blood meals ( 24–48 h after blood feeding ) the capacity of the tubules for de novo protein synthesis may decrease . These observations in the Malpighian tubules of A . albopictus are similar to results of previous studies in the midgut and fat body of A . aegypti , where blood feeding led to a down-regulation of transcripts encoding ribosomal protein subunits and/or translation factors within 12 h to 24 h after a blood meal [17] , [19] , [52] . Hemoglobin is the most abundant protein in mammalian blood , and its digestion leads to the production of heme , a highly toxic metabolite that causes cell and tissue damage via oxidative stress and/or the disruption of plasma membranes [53] . Mosquitoes utilize a variety of mechanisms to detoxify heme and limit its absorption into the hemolymph . The first line of defense is found in the midgut , which secretes a peritrophic matrix that encapsulates the ingested blood cells and may sequester more than half of the amount of heme in a typical blood meal [54] , [55] . Additional mechanisms for heme detoxification include the 1 ) enzymatic degradation of heme by heme oxygenase ( HO ) to produce biliverdin , Fe2+ , and carbon monoxide [56] , 2 ) chelation of heme by xanthurenic acid ( XA ) , which is a product of the kynurenine pathway of tryptophan catabolism [57] , [58] , and 3 ) binding and/or catabolism of heme by glutathione S-transferases ( GSTs ) [59] , [60] . Furthermore , protection against heme-induced , free-radical damage in mosquitoes can be mediated by: 1 ) antioxidant enzymes , such as glutathione peroxidase ( GP ) , thioredoxin peroxidase ( THP ) , thioredoxin reductase ( THR ) , superoxide dismutase ( SOD ) , and catalase ( CAT ) ; 2 ) antioxidant proteins , such as thioredoxin ( TH ) ; and 3 ) small molecule antioxidants , such as glutathione and uric acid [53] . Thus , we were intrigued by the enrichment of the ‘thioredoxin’ and ‘glutathione S-transferase’ functional clusters among the transcripts that exhibited a sustained up-regulation after blood feeding ( Table 2; Figures S7–S8 ) . Furthermore , we noticed that the ‘cofactor metabolic process’ ( Figure S9 ) and ‘vitamin biosynthetic process’ ( Figure S10 ) functional clusters contained several transcripts associated with putative antioxidant and detoxification mechanisms , and that the ‘ATPase/AAA+ type’ ( Figure S11 ) functional cluster contained several transcripts encoding putative ATP-binding cassette ( ABC ) transporters , which play key roles in insect metabolite/xenobiotic excretion [61] . Below , we discuss the up-regulation of transcripts after a blood meal within the aforementioned functional clusters in the context of 1 ) the prevention of heme-induced oxidative cell and tissue damage and 2 ) the detoxification/excretion of heme and heme-related metabolites . Another functional cluster enriched among the transcripts that exhibited a sustained up-regulation is the ‘proteasome complex’ ( Table 2 ) , which is related to the degradation of protein . These transcripts consist entirely of those encoding proteasome or protease regulatory subunits and most exhibit an up-regulation at 3 h and 12 h after a blood meal ( Figure S12 ) , which suggests that the tubules enhance their molecular capacity for proteolytic activity early after a blood meal . Enhanced proteolytic activity within the tubule epithelium may lead to the degradation of 1 ) proteins encoded by the transcripts that are down-regulated after blood feeding , and/or 2 ) proteins that may experience oxidative damage from heme . An increase of proteasome activity would also be expected to result in an increase of free amino-acids available for the synthesis of new proteins [73]—perhaps those associated with the ‘thioredoxin’ and ‘glutathione S-transferase’ functional clusters mentioned above . Consistent with the enrichment of the ‘proteasome complex’ functional cluster among up-regulated transcripts , which is expected to increase the abundance of free amino acids ( see above ) , there is a corresponding enrichment in the ‘amine biosynthetic process’ cluster ( Table 2 ) . The transcripts within this cluster consist primarily of those encoding enzymes associated with amino-acid catabolism and/or biosynthesis , such as cysteine dioxygenase , 2-amino-3-ketobutyrate coenzyme A ligase , glutamine synthetase , phosphoserine phosphatase , ornithine decarboxylase , and phosphoserine aminotransferase ( Figure S13 ) . Similar ( as well as redundant ) transcripts are also found in the ‘vitamin binding’ functional cluster , such as alanine-glyoxylate aminotransferase and alanine aminotransferase ( Figure S14 ) . Notably , the up-regulation of these transcripts occurs at 12 h and 24 h , appearing to follow the up-regulation of transcripts associated with the proteasome ( Figure S12 ) . Thus , the potential increase in the availability of amino acids derived from proteasome activity in the tubules may be followed by an increased molecular capacity to breakdown and/or convert amino acids into other products . The aforementioned up-regulation of glutamine synthetase and alanine aminotransferase drew our attention to a potential role of the Malpighian tubules in the handling of ammonia . As mosquitoes metabolize a protein-rich blood meal , they face a potentially toxic accumulation of ammonia in their hemolymph and tissues from the catabolism of proteins and amino acids in the blood meal . Glutamine synthetase and alanine aminotransferase play prominent roles in detoxifying the ammonia ( see below and Figure 6 ) . In brief , to prevent the build-up of toxic ammonia , mosquitoes have a remarkable capacity to fix and assimilate it into free amino acids ( e . g . , alanine , proline , glutamine ) via a series of biochemical reactions catalyzed by enzymes , such as glutamine synthetase ( GS ) , glutamate dehydrogenase ( GDH ) , glutamate synthase ( GltS ) , alanine aminotransferase ( ALAT ) , and pyrrolidine-5-carboxylate synthase ( P5CS ) and reductase ( P5CR ) ( Figure 6 ) [9] , [74] , [75] , [76] . Moreover , glutamine can serve as a substrate for the production of uric acid through a pathway that includes xanthine dehydrogenase ( XDH ) among other enzymes [9] , [76] . Once uric acid is produced , it can be excreted directly or further converted into allantoin , allantoic acid , and urea through a series of biochemical reactions catalyzed by urate oxidase ( UO ) , allantoinase ( ALLN ) , and allantoicase ( AALC ) , respectively ( Figure 6 ) [9] , [76] . Figure 7 shows the transcripts associated with this pathway in Malpighian tubules that are differentially expressed after blood feeding . Namely , two transcripts encoding GS , one transcript encoding ALAT , and one transcript encoding XDH exhibit a sustained up-regulation after blood feeding . Three transcripts encoding a GDH and two transcripts encoding ALAT are each transiently up-regulated at 12 h , whereas one transcript encoding GltS is transiently down-regulated at 12 h ( Figure 7 ) . The up-regulation of these transcripts occurs at 12 h and/or 24 h after blood feeding , which coincides with the putative availability of amino acids from increased proteasome activity in the tubule ( see above ) , as well as a period of intense protein digestion of the blood meal in the midgut [10] , [11] . Interpreting these molecular findings in the context of the ammonia detoxification pathway suggests that the Malpighian tubules detoxify ammonia by converting it to glutamate via GDH and/or glutamine via GS ( Figure 6 ) . Given the up-regulation of ALAT , it is reasonable to propose that any newly-formed glutamate is converted into alanine ( Figure 6 ) . This putative handling of ammonia is similar to that reported for the midgut of A . aegypti , which fixes and assimilates ammonia into glutamine and alanine , as opposed to the fat body which fixes and assimilates ammonia into glutamine and proline [77] . The fate of the alanine in Malpighian tubules is unknown , but it is possible that it is shuttled to the fat body for conversion into proline , which can then be shuttled to the flight muscle for use as an energy source [78] . The fate of glutamine in Malpighian tubules is also unknown , but it is possible that it is converted into uric acid for excretion , as indicated by 1 ) the sustained up-regulation of XDH , and 2 ) the transient down-regulation of GltS , after blood feeding ( Figure 7 ) . Since transcripts encoding UO , ALLN , and ALLC were not differentially expressed after blood feeding ( data not shown ) , the uric acid may be directly secreted by the tubules for excretion ( perhaps by an ABC transporter in Figure 5 ) , or it may be retained by the epithelium for use as an antioxidant to combat potential oxidative damage due to heme . Consistent with the former notion , uric acid is excreted by mosquitoes after a blood meal [79] . Likewise , uric acid is excreted by tsetse flies and reduviid bugs following a blood meal [80] , [81] , [82] . The remaining functional cluster enriched among the transcripts that exhibited a sustained up-regulation is related to cytoskeletal dynamics ( i . e . , ‘tubulin , GTPase domain’ ) ( Table 2 ) . The transcripts in this functional cluster consist primarily of those encoding components of microtubules , such as tubulin chains ( alpha and beta ) , dynein light chains , and microtubule-associated proteins ( Figure S15 ) . Another related transcript populating this cluster is one encoding a putative Rab GTPase ( AAEL006091-RA ) ; Rab GTPases play important roles in regulating microtubule-mediated trafficking of vesicular cargo [83] . These changes suggest that blood feeding leads to a more dynamic microtubule-based cytoskeleton , which may be associated with the intracellular trafficking of vesicles and/or organelles . At least one study has found that the actin cytoskeleton of principal cells plays a key role in modulating diuretic fluid secretion in A . aegypti Malpighian tubules [84] . Putative changes in the microtubule-based cytoskeleton would also be consistent with a potential functional transition of the epithelium after blood feeding . For example , if the capacity of the tubule for diuresis indeed decreases , as expected by the down-regulation of transcripts associated with the V-type H+-ATPase and other ion/water transport mechanisms ( Figures 2–3 ) , then enhanced vesicular trafficking may play a key role in the endocytosis and degradation of these membrane-bound proteins . Likewise , if the capacity of the tubules for detoxification and metabolite excretion indeed increases , as expected by the up-regulation of transcripts associated with heme and ammonia detoxification/excretion ( Figures 4–6 ) , then the cytoskeletal dynamics may facilitate the movements of newly synthesized membrane-bound transporters that mediate the excretion of metabolites , such as ABC transporters . It is also possible that a more dynamic microtubule cytoskeleton would facilitate the movements of organelles within the epithelial cells . For example , retraction of mitochrondria from the apical microvilli in principal cells is associated with a decrease of fluid secretion in Malpighian tubules isolated from pupal stages of mosquitoes [85] . Thus , similar microtubule-mediated movements of mitochondria may occur during the chronic processing of blood meals ( 24–48 h after blood feeding ) to further contribute to a putative decreased capacity for diuresis . The present study provides the first transcriptomic analysis of the Malpighian tubules of a mosquito after a blood meal , and is also the first to be conducted in A . albopictus after blood feeding . The results reveal molecular changes in transcript accumulation in the tubule epithelium within the first 24 h after a blood meal that suggest a remarkable functional transition of the epithelium from one dedicated to electrolyte and fluid excretion to one dedicated to detoxification and metabolite processing ( Figure 8 ) . Moreover , the results uncover new putative roles of the Malpighian tubules in the chronic processing of blood meals after the post-prandial diuresis ends ∼2 h after a blood meal [8] . Thus , the tubule epithelium may represent an even more valuable target for the development of novel insecticides than has been previously appreciated . The next important step to complete is to validate the hypothesized functional transition of the epithelium after a blood meal using biochemical and physiological approaches .
|
The Asian tiger mosquito Aedes albopictus is a vector of several medically-important arboviruses and one of the most invasive mosquito species in the world . Existing control measures for mosquitoes are presently being challenged by the emergence of resistance to insecticides that target the nervous system . Thus , it is necessary to identify novel physiological targets to guide the development of new insecticides . We recently demonstrated that the ‘kidneys’ ( Malpighian tubules ) of mosquitoes offer a valuable , new physiological target for insecticides . However , our understanding of how this tissue contributes to the chronic metabolic processing of blood meals by mosquitoes is limited . Here we characterize the changes in transcript expression that occur in the Malpighian tubules of adult female A . albopictus with the goal of identifying key molecular pathways that may reveal valuable targets for insecticide development . We find dramatic changes in transcript accumulation in Malpighian tubules , which 1 ) provide new insights into the potential functional roles of Malpighian tubules after a blood meal , and 2 ) reveal new potential molecular pathways and targets to guide the development of new insecticides that would disrupt the renal functions of mosquitoes .
|
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"anatomy",
"cell",
"biology",
"entomology",
"renal",
"physiology",
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"gene",
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2014
|
Transcriptomic Evidence for a Dramatic Functional Transition of the Malpighian Tubules after a Blood Meal in the Asian Tiger Mosquito Aedes albopictus
|
Human T lymphotropic Virus type 1 ( HTLV-1 ) is the etiological agent of Adult T cell Leukemia/Lymphoma ( ATLL ) and HTLV-1-Associated Myelopathy/Tropical Spastic Paraparesis ( HAM/TSP ) . Both CD4+ T-cells and dendritic cells ( DCs ) infected with HTLV-1 are found in peripheral blood from HTLV-1 carriers . We previously demonstrated that monocyte-derived IL-4 DCs are more susceptible to HTLV-1 infection than autologous primary T-cells , suggesting that DC infection precedes T-cell infection . However , during blood transmission , breast-feeding or sexual transmission , HTLV-1 may encounter different DC subsets present in the blood , the intestinal or genital mucosa respectively . These different contacts may impact HTLV-1 ability to infect DCs and its subsequent transfer to T-cells . Using in vitro monocyte-derived IL-4 DCs , TGF-β DCs and IFN-α DCs that mimic DCs contacting HTLV-1 in vivo , we show here that despite their increased ability to capture HTLV-1 virions , IFN-α DCs restrict HTLV-1 productive infection . Surprisingly , we then demonstrate that it is not due to the antiviral activity of type–I interferon produced by IFN-α DCs , but that it is likely to be linked to a distinct trafficking route of HTLV-1 in IL-4 DCs vs . IFN-α DCs . Finally , we demonstrate that , in contrast to IL-4 DCs , IFN-α DCs are impaired in their capacity to transfer HTLV-1 to CD4 T-cells , both after viral capture and trans-infection and after their productive infection . In conclusion , the nature of the DCs encountered by HTLV-1 upon primo-infection and the viral trafficking route through the vesicular pathway of these cells determine the efficiency of viral transmission to T-cells , which may condition the fate of infection .
Human T-Lymphotropic Virus type 1 ( HTLV-1 ) infects 5–10 million people [1] . HTLV-1 is mainly present in Japan , inter-tropical Africa , the Caribbean and South America [2 , 3] . After a long period of clinical latency , HTLV-1 infection leads , in a fraction of infected individuals , either to Adult T-cell Leukemia/Lymphoma ( ATLL ) [4] , an uncontrolled CD4+ T–cell proliferation of very poor prognosis , or to an inflammatory disorder named HTLV-1 Associated Myelopathy / Tropical Spastic Paraparesis ( HAM/TSP ) [5] . In vivo , in chronically infected individuals , HTLV-1 is mainly found in CD4+ T-cells , but infected dendritic cells ( DCs ) are also detected in vitro and more importantly in vivo [6 , 7 , 8 , 9 , 10 , 11] . Their function is subsequently altered in vivo [11 , 12 , 13] and these cells are therefore likely to be involved in viral pathogenesis . The role of DC infection in HTLV-1 dissemination to T-cells has been investigated in mice exposed to chimeric HTLV-1-infected cells , in which the HTLV-1 envelope had been replaced by that of the Moloney murine leukemia virus , to allow HTLV-1 to enter murine cells . In this context , DC depletion leads to a decreased proviral load in mouse CD4+ T-cells [14] . In addition , HTLV-1 viruses harboring mutations in the p12 and p30 regulatory genes that have lost their ability to infect human DCs , also have an impaired ability to infect macaques [15] . Altogether , these in vivo experiments strongly suggest that infection of DCs is required for the establishment and maintenance of HTLV-1 infection in animal models . Consistent with these data , we recently showed that human monocytes-derived dendritic cells ( MDDCs ) are more susceptible to HTLV-1 infection than autologous lymphocytes in vitro [9] , supporting a model where DC infection represents an important step upon primo-infection in vivo . In addition to the blood , DCs are widely distributed in peripheral tissues and in mucosae , where antigen capture might occur . DCs play a central role in activating naïve T-cells and directing the subsequent immune response . DCs are constituted of several distinct subsets that differ in their ability to activate immunity or to promote tolerance [16] . Since HTLV-1 is transmitted through contact with infected cells present in maternal milk , semen or blood , viruses may first be in contact with different DC subsets present in these different tissues . The interaction of HTLV-1-infected cells with a given DC subset may thus affect the subsequent immune response . In addition , interaction with distinct DC subsets might have an impact on the risk of developing ATLL in the case of mother-to-child transmission or on the risk of developing HAM/TSP in the case of infection with contaminated blood [17 , 18] . We therefore investigated whether different MDDC subsets that are similar to tissue-resident DCs exposed to HTLV-1 during primary infection in humans , are equally susceptible to HTLV-1 infection . Human MDDCs were generated with cytokines that allow differentiation into different subsets: interleukin-4 ( IL-4 ) for myeloid DCs of the blood , transforming growth factor beta ( TGF-β ) for mucosal DCs of the gut and interferon-alpha ( IFN-α ) for inflammatory DCs found in injured skin [19] . We demonstrate that IL-4 DCs and to a lesser extent TGF-β DCs are susceptible to HTLV-1 infection , while IFN-α DCs are not . We then demonstrate that IFN-α DC resistance to HTLV-1 infection is not due to IFN-α production . In contrast , we demonstrate that DC maturation , in addition to viral trafficking through acidic vesicles , contributes to DC resistance to HTLV-1 infection . Finally , we show that HTLV-1 productive infection of immature DCs , rather than viral capture by matured-DCs , is required for HTLV-1 transmission from DCs to T-cells . These results demonstrate for the first time that immature DCs represent the main DC population that allows infection of T-cells .
The French Comité de protection des personnes Ile de France has approved the use of human samples used in this study . CPP-IDF-2012-10-04SC to Dr . Gessain ( Institut Pasteur , France ) . Chlorpromazine ( 20 μM , resuspended in H2O ) , diphenyl iodonium chloride ( 10 μM , resuspended in DMSO ) ; dynasore ( 100 μM , resuspended in DMSO ) , methyl-β-cyclodextrin ( 10 mM , resuspended in H2O ) , nystatin ( 270 μM , resuspended in DMSO ) , amiloride ( 375 μM , resuspended in H2O ) , latrunculin ( 2 μM , resuspended in DMSO ) , cytochalasin D ( 2 μM , resuspended in DMSO ) , genistein ( 200 μM , resuspended in MeOH ) , wortmanin ( 40 μM , resuspended in DMSO ) , rottlerin ( 25 μM , resuspended in EtOH ) , Gö6953 ( 1 μM , resuspended in DMSO ) , NSC23766 trihydrochloride ( 250 μM , resuspended in H2O ) and IPA-3 ( 125 μM , resuspended in DMSO ) were purchased from Sigma . Toll like receptor ( TLR ) -2 agonist ( PAM3CSK4; 1 μg/ml ) ; TLR-5 agonist ( Flagellin; 200 ng/ml ) ; TLR-3 agonist ( Poly ( I:C ) ; 10 μg/ml ) ; TLR-4 agonist ( LPS; 1 μg/ml ) ; TLR-7/8 agonist ( R848; 3 μg/ml ) were purchased from Invivogen . Recombinant IFN-α ( 500–2000 IU/ml ) was purchased from Tebu-Bio . The following antibodies were purchased from BD Biosciences: APC-H7-labeled mouse anti-Human CD14 ( MφP9 ) ; V450-coupled mouse anti-Human CD11c ( clone Ly6 ) ; PE-Cy7-coupled mouse anti-Human CD40 ( clone 5C3 ) ; PerCPCy5 . 5-coupled mouse anti-Human HLA-DR ( clone G46 . 6 ) . APC-coupled mouse anti-Human DC-SIGN ( DCN47 . 5 ) and Vio-Bright-FITC-coupled mouse anti-Human BDCA4 ( clone AD517F6 ) were from Miltenyi . PE-coupled mouse anti-Human CD86 ( clone IT2 . 2 ) was from Ebioscience . Mouse anti-HTLV-1 Gag p19 antibody ( clone TP7 ) was from Zeptometrik , and biotin-coupled mouse anti-HTLV-1 Tax antibody ( clone LT4 , [20] ) was provided by Pr Tanaka . Jurkat cells stably transfected with a plasmid encoding the luciferase ( Luc ) gene under the control of the HTLV-1 long terminal repeat ( LTR ) promotor ( Jurkat-LTR-Luc ) [21] were maintained under hygromycin ( 450 μg/ml , Sigma ) selection in RPMI 1640 medium supplemented with 10% fetal calf serum ( FCS; Gibco Life Technologies ) and penicillin-streptomycin ( 100 μg/ml; Gibco Life Technologies ) . HTLV-1 infected T-cells ( C91-PL ) were maintained in RPMI 1640 medium supplemented with 10% FCS and penicillin-streptomycin ( 100 μg/ml ) . The human fibrosarcoma cell line containing a plasmid encoding the luciferase gene under the control of the immediate early IFN inducible 6–16 promoter ( HL116 ) [22] were maintained under HAT selection in DMEM medium supplemented with 10% FCS and penicillin-streptomycin ( 100 μg/ml ) . Monocyte-derived dendritic cells were maintained in complete MDDC medium composed of RPMI 1640 medium supplemented with 10% FCS , 100 μg/ml of penicillin-streptomycin , non-essential amino acids ( 2 . 5 mM; Gibco Life Technologies ) , sodium pyruvate ( 1 mM; Sigma ) , β-mercaptoethanol ( 0 . 05 mM; Gibco Life Technologies ) , and HEPES ( 10 mM; Gibco Life Technologies ) . Granulocyte-macrophage colony stimulating factor ( GM-CSF; 100 ng/ml; Miltenyi ) and interleukine 4 ( IL-4 , 100 ng/ml; Miltenyi ) , or transforming growth factor beta ( TGF-β , 10 ng/ml , Eurobio ) , or interferon alpha ( IFN-α , 500 IU/ml , Tebu-bio ) were added to the medium for differentiation . All cells were grown at 37°C in 5% CO2 . Blood was collected by Etablissement Français du Sang ( Lyon , France ) from non-infected blood donors . Peripheral blood mononuclear cells ( PBMCs ) were isolated from heparinized blood on Ficoll density gradient . Monocytes were purified from PBMCs on Percoll gradients . Cells were cryopreserved in 10% dimethylsulfoxide ( DMSO ) - 50% FCS—40% culture medium before being used . Dendritic cells were generated after culture of monocytes in complete MDDC medium . IL-4 DCs were obtained after culture of monocytes in complete MDDC medium supplemented with GM-CSF and IL-4 for 5 days . Fresh cytokines were added 72 h later . When indicated , IL-4 DCs were further treated for 18 h by the addition of TLR agonists or recombinant IFN-α in the MDDC medium . TGF-β DCs were obtained after culture of monocytes in complete MDDC medium supplemented with GM-CSF , IL-4 and TGF-β for 5 days . Fresh cytokines were added 72 h later . IFN-α DCs were obtained from monocytes cultured in complete MDDC medium supplemented with GM-CSF and IFN-α for 3 days . Fresh cytokines were added 24 h later . MDDCs ( 2 . 5 x 105 cells ) were co-cultured with C91-PL cells ( 2 . 5 x 104 cells ) , pre-treated with mitomycin C ( 50 μg/ml , Sigma ) . MDDCs were then collected after 3h or 72h of co-culture , fixed and used for flow cytometry analyses ( FACS Canto II; BD Biosciences ) and imaging . When indicated , MDDCs were infected in presence of azido-thymidine ( AZT , 10 μg/ml , Sigma ) or treated with chloroquine ( 150 μM , Sigma ) or diphenyl iodonium chloride ( 10 μM , Sigma ) before co-culture with mitomycin-treated C91-PL cells . When indicated , MDDCs ( 2 . 5 x 105 cells ) were infected by biofilm preparation ( 100 μl ) purified as previously described [9] . At each time point , MDDCs were collected , washed several times with PBS and stored as pellets before genomic DNA extraction and real-time PCR analysis . When indicated , biofilm was heat-inactivated at 56°C before contact with MDDCs . MDDCs ( 2 . 5 x 105 cells ) were plated in 48-well plates and treated with drugs targeting several endocytosis pathways for 3 h . Untreated cells were incubated in the same conditions and were used as controls . Treated and control MDDCs were then co-cultured with mitomycin-treated C91-PL cells ( 2 . 5 x 104 cells ) for an additional 3 h . Cells were then fixed and stained for flow cytometry analyses . MDDCs ( 150 x 103 cells per well ) were plated in Lab-Tek chamber slides ( Nunc ) previously treated with Poly-L-lysine ( Sigma , P4832 ) according to the manufacturer’s instructions and cultured for 18 h . When needed , LPS ( 1 μg/ml ) was added . MDDCs were then co-cultured with mitomycin-treated C91-PL cells ( ratio 10:1 ) for 3 h and fixed with 4% paraformaldehyde ( PFA ) . Cells were extensively washed with PBS , quenched with NH4Cl solution ( 50 mM ) and permeabilized with PBS—1% BSA—0 . 1% saponin . Cells were then stained using a serum from an HTLV-1 infected patient ( gift from Dr Gessain , Institut Pasteur ) followed by a DyLight 488-labeled Goat anti-Human IgG Antibody ( 1/1000; Vector laboratories ) and anti-CD82 antibody ( AM26701PU-N , Acris ) followed by a DyLight 549-labeled anti-Mouse IgG Antibody ( 1/1000; Vector laboratories ) . When indicated , cells were stained with anti p19Gag antibody followed by a DyLigh 549-labelled anti-mouse antibody and anti-human EE1A antibody ( N19 , Santa-Cruz biotechnology ) followed by a FITC-labelled anti-goat antibody ( Dako ) . After washes in PBS and lastly in water , slides were mounted in Dapi-Fluoromount G ( Southern Biotech ) . Samples were examined under a Leica spectral SP5 confocal microscope equipped with a 63x 1 . 4–0 . 6 oil-immersion objective using the LAS-AF software , or using a Zeiss LSM800 confocal microscope equipped with a 63x 1 . 4 oil immersion objective on the ZEN software . Images were analyzed using ImageJ . MDDCs were plated in Lab-Tek wells and incubated with the lysotracker Red DND-99 ( 5 μM , Thermofisher scientific , L7528 ) for 30 minutes . Cells were then extensively washed and fixed in 4% PFA . For lipid droplets staining , cells were fixed in 4% PFA , and stained with Nile Red ( 0 . 25 mg/ml; Sigma ) for 15 min . After several washes in PBS and lastly in water , slides were mounted in Dapi-Fluoromount G and fluorescence analyzed with routine inverted Zeiss axiovert 135 microscope . Fluorescent signals were quantified with ImageJ . MDDCs ( 2 . 5 x 105 cells ) were collected , washed in PBS and stained with the following panel: APC-H7-labeled mouse anti-Human CD14; V450-coupled mouse anti-Human CD11c; PE-Cy7-coupled mouse anti-Human CD40; PerCPCy5 . 5-coupled mouse anti-Human HLA-DR , APC-coupled mouse anti-Human DC-SIGN , Vio-Bright-FITC-coupled mouse anti-Human BDCA4 and PE-coupled mouse anti-Human CD86 . To analyze HTLV-1 capture , MDDCs were collected after co-culture with mitomycin-treated C91-PL cells , washed in PBS and fixed with 4% PFA . Cells were permeabilized with PBS—1% BSA—0 . 1% saponin , and stained with a mouse anti-p19gag antibody ( 1:1000 ) followed by a DyLight 488-coupled goat anti-mouse antibody ( 1:1000 ) . Cells were then washed twice with PBS—1% BSA—0 . 1% saponin and once with PBS—1% BSA and finally surface-stained with V450-coupled anti-human CD11c antibody . To analyze productive infection , MDDCs were collected after co-culture with C91-PL , washed in PBS and in normal goat serum ( 7% , Sigma ) , fixed and permeabilized according to the manufacturer’s instructions ( eBiosciences ) . Cells were stained with biotin-coupled anti-Tax antibody ( LT4 ) followed by streptavidin labeled with PE-Cy7 ( BioLegend , Ozyme ) . After extensive washes , cells were finally surface-stained with a V450-coupled anti-CD11c antibody . All results were acquired by flow cytometry using at least 50 000 events ( FACSCantoII; BD-Biosciences ) and analyzed with FlowJo software . Genomic DNA was extracted using the Nucleospin blood kit ( Macherey-Nagel , Düren , Germany ) according to the manufacturer’s instructions . DNA concentration was determined with a NanoDropND1000 spectrophotometer ( Thermo Scientific ) . Real-time quantitative PCR ( qPCR ) was performed on 10 ng of genomic DNA as previously described [9] . IL-4 DCs ( 2 . 5 x 105 cells ) were plated in 48-well plates and treated with 500 IU/ml of recombinant IFN-α for 18 h . Cells were washed twice in phosphate-buffered saline ( PBS ) and total RNA was extracted using the RNeasy minikit ( Quiagen , 74106 ) . Extracted RNA was treated with RQ1-RNAse-free DNAse ( Promega ) . RNA concentration was determined with a NanoDropND1000 spectrophotometer ( Thermo Scientific ) . RT-PCR was performed on 500 ng of RNA using the SuperScript Reverse Transcriptase ( Thermofisher ) . cDNA concentration was then determined with a NanoDropND1000 spectrophotometer ( Thermo Scientific ) . cDNA amplification was performed on 200 ng of cDNA using the GoTaq polymerase ( Promega ) and Mx1 primers as previously described [23] . PCR products were analyzed by electrophoresis on a 2% agarose gel . To determine the ability of MDDCs to transfer HTLV-1 without being infected , MDDCs ( 3 x 106 cells ) were exposed to mitomycin-treated C91-PL cells ( 6 x 105 cells ) for 4 h in 6-well plates . Then , DCs were magnetically isolated using the CD304 ( BDCA-4/Neuropilin-1 ) Microbead kit ( Miltenyi Biotec , 130-090-532 ) . Isolated DCs ( 105 cells ) were co-cultured with Jurkat-LTR-Luc ( 105 cells ) in 48-well plates . Co-culture was performed for 48 h . Cells were then collected , and luciferase reporter activity assayed ( Luciferase kit Promega ) . Results were normalized according to the amount of proteins determined by Bradford ( Biorad ) . To determine the ability of productively infected MDDCs to transfer the virus to T-cells , MDDCs ( 3 x 106 cells ) were exposed to mitomycin-treated C91-PL cells ( 6 x 105 cells ) for 4 h in 6-well plates . Then , DCs were magnetically isolated using the CD304 ( BDCA-4/Neuropilin-1 ) Microbead kit . Isolated DCs ( 1 x 105 cells ) were cultured in 48-well plates for 72 hours in the presence or absence of 100 μM of AZT added in culture medium each day . Jurkat-LTR-Luc cells ( 105 cells ) were then added and the co-culture was maintained for 48 h . Cells were then collected , and luciferase reporter activity assayed . Results were normalized according to the amount of proteins determined by Bradford ( Biorad ) . Jurkat LTR-Luc cells were transduced with Lenti-IRES-GFP as previously described [24] . C91-PL cells were stained with 20 nM of cell tracker red CMPTX ( Invitrogen C33542 ) in RPMI 1640 medium for 30 minutes at 37°C , and washed twice with RPMI 1640 medium supplemented with 10% FCS and penicillin-streptomycin ( 100 μg/ml ) before use . Mock treated and LPS-treated IL-4 DCs or IFN-α DCs ( 5 x 104 cells ) from the same donor were mixed with transduced Jurkat LTR-Luc cells ( 5 x 104 cells ) and red-labelled C91-PL ( 5 x 103 cells ) , plated in Lab-Tek chamber slides previously treated with Poly-L-lysine and cultured for 45 minutes at 37°C . Cells were then fixed with 4% PFA for 15 minutes at room temperature . After several washes in PBS , slides were mounted in Dapi-Fluoromount G . Signals were acquired with an Axioimager Z1 microscope ( Zeiss ) , and analyzed with ImageJ . Contacts between unstained MDDCs and GFP transduced Jurkat-LTR-Luc cells were manually counted from at least 10 pictures taken from the same slide . HL116 cells were seeded at 2 x 104 cells/well in 96-U-bottom-well plates and incubated for 24 h . Supernatant collected from MDDCs cultures ( 100 μl ) or serial dilutions of recombinant IFN-α used for standard curve determination were added for an additional 17 h . Cells were then lysed and luciferase activity assayed . One-way analysis of variance ( ANOVA ) with Bonferroni’s post-hoc multiple comparison test was used to determine statistically significant differences . Paired two-tail t-test was used to compare two groups from the same donor . Differences were considered significant if the p-value was < 0 . 05 .
To determine whether all DC subsets were equally susceptible to HTLV-1 , IL-4 DCs , TGF-β DCs and IFN-α DCs were generated from human primary monocytes . DC differentiation was controlled by the loss of CD14 expression and the acquisition of CD11c expression ( S1A Fig ) . Consistent with previous data [19] , IL-4 DCs and TGF-β DCs displayed the most immature phenotype , with a low surface expression of CD86 , CD40 and HLA-DR markers ( S1A Fig ) . Expression of these maturation markers was increased in IFN-α DCs confirming their more mature phenotype ( S1A Fig ) . The three different DC subtypes were then exposed to infected C91-PL cells . Infection was first monitored by the accumulation of the structural p19gag viral protein over time ( Fig 1A ) . In IL-4 DCs , the relative abundance of p19gag-positive cells significantly increased between 3h and 72 h post infection ( p . i . ) , while it significantly decreased in IFN-α DCs , suggesting that IFN-α DC are not productively infected . Of note , in TGF-β DCs , the amount of p19gag-positive cells did not change significantly , suggesting a reduced level of infection . To confirm this result , the amount of proviral DNA was quantified 3 and 5 days after contact of the different DCs with purified HTLV-1 viral biofilm ( Fig 1B ) . Viral biofilm are membrane-bound viruses that are embedded in extracellular matrix-rich structures at the surface of infected cells [21] . Infection with purified viral biofilm allows quantification of viral DNA resulting from de novo infection of target cells , without contamination with viral DNA from C91-PL that occurs when infection is perfomed through co-culture with infected cells . As previously shown [9] , proviral DNA was detected in IL-4 DCs exposed to viral biofilm , and its amount significantly increased during the course of infection ( Fig 1B ) . Proviral DNA was also detected in TGF-β DCs exposed to viral biofilm but , consistent with the low accumulation of viral p19gag , its amount did not significantly increase during the course of exposure ( Fig 1B ) , suggesting that TGF-β DCs are less susceptible than IL-4 DCs to HTLV-1 infection . In contrast , HTLV-1 viral DNA was barely detected in IFN-α DCs exposed to viral biofilm and its amount did not change during the course of infection ( Fig 1B ) . As a control , no viral DNA was detected when heat-inactivated viral biofilm was used ( Fig 1B ) . Productive infection was further confirmed by detection of Tax expression , since this viral protein is absent from the viral particle . Consistent with the amount of viral DNA , a higher percentage of IL-4 DCs expressed Tax compared to TGF-β DCs , with a mean of 0 , 6% infected cells ( Fig 1C ) . A significant reduction in the number of Tax-expressing cells was observed among IL-4 DCs when infection was performed in the presence of AZT ( S1B Fig ) , confirming that Tax expression results from productive infection [9] . In addition , a very low percentage of Tax-expressing cells were found in the IFN-α DC population ( Fig 1C ) with a mean of 0 , 1% infected cells . Altogether , these results demonstrate that the IL-4 DC population is more susceptible to HTLV-1 infection than the TGF-β one , and that the IFN-α DC population is resistant to HTLV-1 infection . To understand the mechanism leading to differential susceptibility to HTLV-1 infection in the three DC subsets , we first controlled the expression of HTLV-1 ( Fig 2 ) . Two proteins , the binding receptor NRP-1 and the fusion receptor Glut-1 , have been involved in HTLV-1 entry in CD4+ T lymphocytes [25] . In addition , DC-SIGN is involved in HTLV-1 binding to DCs [7] and is important for DC infection [6] . NRP-1 was expressed in all DC subsets , although its level was significantly higher in IL-4 and TGF-β DCs than in IFN-α DCs ( Fig 2A ) . Similarly , DC-SIGN was expressed in all DC subsets but its expression was significantly higher in IL-4 DCs ( Fig 2B ) . In contrast , Glut-1 expression was similar in all three DC subsets ( Fig 2C ) . These results suggest that HTLV-1 binding might be lower in IFN-α DC and TGF-β compared to IL-4 DC . We therefore measured viral capture using detection of p19gag in the DC populations 3h after contact with C91-PL infected cells ( Fig 2D ) in addition to productive infection determined 3 days post contact ( Fig 2E ) . Surprisingly , capture was 6-fold higher in IFN-α DCs and 3 fold higher in TGF-β DCs compared to IL-4 DCs ( Fig 2D ) . This suggests that viral capture is inversely correlated to productive infection ( Fig 2E ) , and could be independent of NRP-1 and/or DC-SIGN expression level . Thus , IFN-α DC resistance to HTLV-1 infection is not linked to decreased capture efficiency or to a lower expression of viral fusion receptor . It is well established that type I IFN controls several viral infections through the induction of numerous interferon-inducible genes ( ISGs ) that restrict viral cycle at different levels [26] . Furthermore , HTLV-1 infection is restricted when target T-cells are treated with IFN-α before contact with the virus [23 , 27] . We therefore measured the basal level of IFN-α production by the three different DC subtypes . As expected , IFN-α DCs produced and released higher levels of type I IFN compared to IL-4 or TGF-β DCs ( Fig 3A ) . Given that IFN-α production by IFN-α DCs could be responsible for their resistance to HTLV-1 infection , we treated IL-4 DCs with increasing doses of recombinant IFN-α 2a before co-cultivating them with C91-PL infected cells . We first verified that recombinant IFN-α treatment did not affect IL-4 DC phenotype ( S2A Fig ) and was sufficient to induce expression of ISGs as measured by the up-regulation of Mx-1 mRNA ( Fig 3B ) . Surprisingly however , treatment of IL-4 DCs with recombinant IFN-α did not affect their susceptibility to HTLV-1 as exemplified by the number of CD11c-positive Tax-positive cells shown for a representative experiment ( Fig 3C ) and the percentage of infected DCs observed in different experiments performed with cells from several independent donors ( Fig 3D ) , or by the number of p19gag-positive cells after 3h of DCs exposure to C91-PL cells ( S2B Fig ) . These results suggest that IL-4 DC exposure to recombinant IFN-α 2α does not affect their susceptibility to HTLV-1 infection . Type I IFN includes IFN-β and 12 different IFN-α proteins that have different antiviral properties . IFN-α 2a was recently shown to have the weakest antiviral activity against HIV-1 compared to eight others [28] . Thus , recombinant IFN-α 2a used above might not be sufficient for the restriction of HTLV-1 infection in IFN-α DCs . To address this issue , we tested the ability of the physiological type I IFN-α present in the supernatant of IFN-α DCs to restrict HTLV-1 infection in IL-4 DCs . Again , the number of Tax positive IL-4 DCs did not differ significantly when cells were cultured for 18 h with or without the supernatant from IFN-α DCs ( Fig 3E and 3F ) , confirming that type I IFN produced by IFN-α DCs is not responsible for the resistance of IFN-α DCs to HTLV-1 infection . IFN-α DCs are more mature than IL-4 DCs , as shown by the up-regulation of HLA-DR and CD40 markers ( S1A Fig and [19] ) . We thus asked whether DC maturation could account for restriction of HTLV-1 infection in IFN-α DCs . To test this hypothesis , IL-4 DCs were treated with LPS , a TLR-4 agonist , which induces the up-regulation of DC maturation markers such as HLA-DR , CD40 and CD86 ( S3A Fig ) . Both immature and LPS-matured IL-4 DCs were then exposed to C91-PL cells for 3 h to measure viral capture , or for 3 days to measure productive infection . Similar to our results with IFN-α DCs , the percentage of p19gag-positive DCs was significantly higher in LPS-matured DCs ( Fig 4A ) , while the percentage of Tax positive DCs was significantly lower when cells had been matured with LPS ( Fig 4B ) . This indicates that LPS treatment of IL-4 DCs increases HTLV-1 uptake but restricts their productive infection and seems to recapitulate IFN-α DC restriction of HTLV-1 . LPS-treatment of IL-4 DCs induced their maturation and was also accompanied with type I IFN production ( S4B Fig ) . We thus tested whether type I IFN production after LPS-treatment could account for the decreased level of HTLV-1 productive infection . IL-4 DCs were treated with LPS for 3 h . Medium was then removed , cells were extensively washed to remove any trace of LPS and cultured for 18 h in LPS-free medium . Culture supernatant was then collected and added to another set of the same autologous IL-4 DCs . Addition of this supernatant induced an increase in CD86 expression level although lower to that of fully matured DCs , without any other change in the expression of CD40 or HLA-DR maturation markers ( S3A Fig ) . Interestingly , treatment of IL-4 DCs with the supernatant from LPS-matured DCs did neither affect DC susceptibility to HTLV-1 as shown using a result from one representative experiment ( Fig 4C ) nor the percentage of infected DCs observed in different experiments performed on independent donors ( Fig 4D and S3B Fig ) , or the number of p19gag-positive cells ( S3C Fig ) . These results suggest that maturation rather than type I IFN production accounts for the decreased susceptibility of LPS-matured DCs to HTLV-1 infection . To confirm this hypothesis , we induced IL-4 DC maturation with different TLR agonist treatments . Stimulation of IL-4 DCs with PolyI:C , R848 or LPS , that are agonists of TLR-3 , TLR-7/8 or TLR-4 respectively , induced a fully mature phenotype , as measured by the up-regulation of HLA-DR , CD86 and CD40 ( S4A Fig ) and type I IFN production ( S4B Fig ) . In contrast , stimulation of IL-4 DCs with PAM3 or flagellin that are ligands of TLR-2 or TLR-5 respectively , induced an incomplete mature phenotype ( S4A Fig ) with no type I IFN production ( S4B Fig ) . Interestingly , while treatment with ligands of TLR-3 or TLR-4 significantly increased viral capture ( Fig 4E lanes 4 and 5 ) , treatment with ligands of TLR-3 , TLR-7/8 or TLR-4 significantly decreased productive infection ( Fig 4F , lanes 4–6 ) . In contrast , treatment with ligands of TLR-2 or TLR-5 did not affect viral capture ( Fig 4E , lanes 2–3 ) but led to an increased productive infection of IL-4 DCs ( Fig 4F , lanes 2–3 ) . Altogether , these results suggest that DC maturation independently of type I IFN present in the supernatant of matured DC is responsible for the restriction of HTLV-1 infection in DCs . HTLV-1 capture is more important in mature DCs but is not linked to a productive infection . We therefore investigated whether after its capture , HTLV-1 virus might be stored in different compartments according to the different DC subsets . After 3 h of contact with infected cells , p19gag signal appeared as cytoplasmic punctae in the three DC subsets ( Fig 5A ) suggesting a vesicular localization . None of the p19gag vesicles were co-stained with the EE1A marker of early endosomes in any DC subset ( S5 Fig ) . In contrast , most virus-containing vesicles also stained positive for the tetraspanin CD82 marker of multivesicular bodies ( MVBs , Fig 5A , see white arrows ) , with only 10% of virus-containing vesicles that did not stain positive for CD82 ( Fig 5A , see green arrows ) . Interestingly , the same percentage of CD82-positive virus-containing vesicles was observed in the three DC subsets ( Fig 5B ) , suggesting that HTLV-1 might be stored in similar compartments despite a different infection outcome . To explain the differential susceptibility to HTLV-1 infection , we hypothesized that viral entry mechanisms might differ in IL-4 DCs vs . LPS-matured and IFN-α DCs . Vesicular entry pathways are mediated by several mechanisms of endocytosis that can be blocked by pharmacological inhibitors . Hence , immature IL-4 DCs , LPS-matured IL-4 DCs or IFN-α DCs were treated with inhibitors targeting dynamin-mediated and clathrin-mediated endocytosis ( dynasore and chlorpromazine respectively ) , caveolin-mediated endocytosis ( methyl-β-cyclodextrin and nystatin ) , actin polymerization ( latrunculin , cytochalasin D ) or macropinocytosis ( amiloride ) before being co-cultured with C91-PL infected cells . We controlled that treatment with inhibitors did not change the viability of the different DC subsets compared to untreated DCs ( S6A and S6B Fig ) . Viral capture was measured after 3 h of contact with C91-PL . Treatment with methyl-β-cyclodextrin and nystatin , that depleted cholesterol as controlled by the lack of lipids droplets stained by Nile red in IL-4 treated DCs ( S6C Fig ) , did not affect p19gag levels ( S6D Fig ) , suggesting that HTLV-1 entry does not follow caveolin-mediated endocytosis . However , HTLV-1 entry was significantly reduced by both chlorpromazine and latrunculin ( Fig 6A ) , suggesting a requirement for clathrin-mediated endocytosis and actin polymerization . Interestingly , HTLV-1 entry was significantly reduced by amiloride and dynasore in immature IL4 DCs and LPS-matured DCs but not in IFN-α DCs ( Fig 6A ) , suggesting the requirement of macropinocytosis specifically in immature and mature IL-4 DCs . Macropinocytosis involves PI3K and PKC that control actin polymerization through the activation of Rac 1 GTPase . Rac 1 GTPase in turn controls the polymerization of actin through the activation of Pak-1 . All these processes finally lead to the extension of filopodia that fold back onto the cell membrane to form large irregular macropinosomes [29] . HTLV-1 entry was blocked in immature and LPS-matured IL-4 DCs by the PI3K inhibitor wortmanin , the PKC inhibitors rottlerin and Gö6976 , and inhibitors of Rac 1 ( NSC23766 ) and Pak1 ( IPA-3 ) ( Fig 6B ) . This further confirms the implication of macropinocytosis in HTLV-1 entry in these subsets . Interestingly , compared to entry in IL-4 DCs , HTLV-1 entry in LPS-matured DCs was more reduced after treatment with genistein , rottlerin and NSC , less reduced after treatment with Gö6976 and not blocked by the inhibitor of actin fibers elongation cytochalasin D ( Fig 6B ) , suggesting the involvement of a different macropinocytosis process in LPS-matured DCs . Finally , HTLV-1 entry in immature and LPS-matured IL-4 DCs was not blocked by nocodazole and blebstatin , suggesting that microtubule or myosin contraction are not involved in the macropinocytosis process of HTLV-1 entry ( S6E Fig ) . Altogether , these results demonstrate that HTLV-1 entry in immature and LPS-matured IL4-DCs requires macropinocytosis as well as clathrin-mediated endocytosis while entry in IFN-α DC used mainly clathrin-mediated endocytosis . Since HTLV-1 entry pathways , and intracellular localization are similar in immature IL-4 DCs that are susceptible to infection and in IFN-α DCs that are resistant to infection , we next wondered whether intravesicular pH could modify the ability of the virus to establish a productive infection . Indeed , previous studies reported that immature DCs have vesicles with neutral pH , while vesicles pH is more acidic in mature DCs [30] . Using acidic lysotracker , which fluorescence intensity is dependent on acidic pH , and using drugs that induce intravesicular acidification ( diphenylene-iodonium DPI ) or alkalization ( chloroquine ) , we were able to modulate and monitor the pH of vesicles . The neutral pH of vesicles in immature IL-4 DC was acidified after DPI treatment ( Fig 7A ) , while the slightly more acidic pH of vesicles in IFN-α DCs was alkalized after chloroquine treatment ( Fig 7D ) . As a control , the acidic pH of vesicles in LPS-treated DCs was alkalized after choloroquine treatment ( Fig 7G ) . IL-4 DCs treated with DPI captured similar amount of HTLV-1 than untreated IL-4 DCs when co-cultured with C91PL ( Fig 7B ) . However , productive infection was significantly reduced under these conditions ( Fig 7C ) . This suggests that HTLV-1 trafficking into acidic vesicles inhibits productive infection of IL-4 DCs . In contrast , in the same co-culture conditions , chloroquine treatment of IFN-α DCs , reduced viral capture to a level similar to that of IL-4 DCs , ( Fig 7E ) . In addition , Tax expression was significantly increased in chloroquine-treated IFN-α DCs compared to untreated IFN-α DCs , although it did not reach the level of Tax expression in IL-4 DCs ( Fig 7F ) . This suggests that productive infection is only partially restored by alkalization of IFN-α DC vesicles . Similar results were obtained after treatment of LPS-matured DCs before their co-culture with C91-PL ( Fig 7H and 7I ) , further suggesting that alkalization of matured DC vesicles partially restores productive infection . Altogether , our results suggest that productive infection of DCs by HTLV-1 requires a neutral pH of vesicles and an immature phenotype of the DCs . We have previously shown that productive infection of IL-4 DCs was the main route of HTLV-1 transmission to lymphocytes , although passive transfer from HTLV-1-exposed IL-4 DCs in presence of AZT may have occurred in rare cases [9] . This passive transfer , also known as “trans-infection” ( for a review see [31] ) , results from transmission of captured viral particles without productive infection , and is the main route used by HIV-1 for spreading to T-cells [32] . Since viral capture is around three-fold higher in LPS-matured DCs ( see Fig 4E ) and six-fold higher in IFN-α DCs ( see Fig 2D ) than in IL-4 DCs , we hypothesized that trans-infection might occur preferentially with mature DCs compared to IL-4 DCs . IL-4 DCs , LPS-treated DCs or IFN-α DCs were thus exposed to C91-PL for 3 h to allow viral capture , and then separated from C91-PL using positive selection with anti-BDCA-4 antibodies . We controlled that the DC phenotype was not changed after the purification process ( S7A and S7B Fig ) and allowed the removal of at least 95% of C91-PL ( S7C Fig ) . Purified DCs were then co-cultured for 2 days with Jurkat LTR-Luc reporter cells , ( Fig 8A ) . Luciferase signals significantly decreased when co-culture was performed with LPS-treated IL-4 DCs or IFN-α DCs , compared to co-culture with IL-4 DCs ( Fig 8A ) . Interestingly , when IFN-α DCs were treated with chloroquine before co-culture with C91-PL , luciferase signals were significantly higher compared to co-culture with untreated IFN-α DCs ( Fig 8A see graph ) . These results suggest that although HTLV-1 capture is higher in mature DCs , internalized virus in acidic vesicles cannot be transferred to T-cells . To control that the decreased luciferase signals were not due to a decreased ability of IFN-α or LPS-treated DCs to engage cell-to-cell contacts with Jurkat-LTR-Luc cells , we counted the number of DC-Jurkat-LTR-Luc conjugates in co-cultures containing DCs , C91-PL and Jurkat-LTR-Luc cells . To distinguish the different cell types in the co-culture , C91-PL were stained with a cell tracker and appeared in red , while Jurkat-LTR-Luc cells were transduced with GFP-expressing lentivectors , and appeared in green . DCs were left unstained ( Fig 8B ) . The number of cell conjugates between unstained DCs and GFP-labelled Jurkat-LTR-Luc cells after 3 h of co-culture did not change significantly when IL-4 , IFN-α or LPS-treated DCs were used ( Fig 8B , see graph ) , suggesting that decreased viral transfer from IFN-α DCs and LPS-treated DCs was not due to a lack of contacts with Jurkat cells . Next , IL-4 DCs , LPS-treated DCs or IFN-α DCs were exposed to C91-PL for 3 h , purified using positive selection with anti-BDCA-4 antibodies and cultured for three days to allow productive infection . Then , Jurkat-LTR-Luc reporter cells were added in the MDDCs culture and luciferase signals monitored two days later ( Fig 8C ) . In that setting , IL-4 DCs were able to transfer significant amounts of HTLV-1 ( Fig 8C ) , while reduced luciferase signals were detected using LPS-treated IL-4 DCs or IFN-α DCs ( Fig 8C ) , confirming that productive infection is required to allow virus transfer to T-cells . This was further controlled using AZT treatment , which significantly reduced luciferase signals . Surprisingly , AZT treatment of LPS-treated IL-4 DCs or IFN-α DCs also reduced luciferase signals observed with the untreated DC subsets , suggesting that the viral transfer observed with these subsets relies on their productive infection and not on long term viral capture ( Fig 8C ) . In addition , when IFN-α DCs were treated with chloroquine before their exposure to C91-PL and their purification , viral transfer to T-cells was partially restored , but only in the absence of AZT . Altogether , these results suggest that viral transmission from DCs to T-cells mainly results from transmission of newly synthesized virus from productively infected DCs , and not from passive transmission after viral capture by LPS-matured or IFN-α DCs .
Apart from circulating CD4+ T lymphocytes that represent the main infected cell population in chronically infected individuals , HTLV-1 proviral DNA is also detected in antigen presenting cells ( APCs ) such as myeloid DCs [33] , monocytes and plasmacytoid DCs [11] . We previously showed that in vitro MDDCs are more susceptible to HTLV-1 infection than autologous primary T-cells [9] , supporting a model in which DCs would be the first cells to be infected during primary infection . In vivo , DCs come with different flavors and functions depending on their location in the body . Thus , virus interaction with DCs may have different outcomes depending on the nature of the DCs encountered in the course of infection . In this study , we used dendritic cells differentiated in vitro from human monocytes to generate different DC subsets mimicking those present in vivo . We demonstrate for the first time that DCs are not all equally susceptible to HTLV-1 infection . While immature DCs are more susceptible than tolerogenic-TGF-β DCs to HTLV-1 infection , inflammatory DCs and mature DCs are resistant to HTLV-1 infection . Surprisingly , resistance is neither due to the presence of high level of type-I interferon produced by inflammatory DCs or mature DCs , nor to the exogenous recombinant interferon used to generate those DCs . Interestingly , these results are different from previous reports , including ours , obtained in T-cells , which showed that their susceptibility to HTLV-1 infection is sensitive to IFN-α [23 , 27] . Of note , cell-type-dependent susceptibility to IFN has already been reported and was linked to the use of cell-type-specific patterns of STAT activation as well as to usage of additional signaling components that can finely tune ISGs expression in a given cell type [34 , 35] . In addition , substantial quantitative differences in STAT activation in different cell-types may be linked to the induction of the same ISGs but with different amplitude [36] . In T-cells , treatment with exogenous IFN-α2 results in a block in HTLV-1 protein expression , due to protein kinase R ( PKR ) activation while the early steps of the viral cycle are maintained [23] . Strikingly , it was shown that treatment with recombinant IFN-α2 induced a higher expression of PKR in DCs than in T-cells [36] . Thus , our results , which demonstrate a lack of antiviral control in DCs after IFN treatment , suggest that PKR induction in DCs might not be as efficient as in T-cells to restrict HTLV-1 infection . Alternatively , PKR phosphorylation that is required for its antiviral activity might not be present or sufficient in DCs treated with type I IFN . In addition , PKR antiviral activity could be counterbalanced by the concomitant induction of ADAR-1 ( Adenosine Deaminase Acting on RNA type 1 ) , another ISG that has been shown to enhance replication of several viruses [37] . Interestingly , ADAR-1 expression in T-cells suppresses IFN-α inhibitory effect on HTLV-1 expression through the repression of PKR phosphorylation [38] . Thus , the inability of IFN-α to restrict HTLV-1 infection in DCs might be the result of an unbalanced expression of ADAR-1 over that of PKR . Our results also demonstrated that restriction was not due to a lack of viral entry in mature DCs , although the level of viral binding receptors is lower than in susceptible DCs . This suggests that viral capture by DCs that have been exposed to infected cells may use other receptors . Interestingly , HTLV-1 was observed in CD82 positive compartment in all DC subsets . CD82 is a tetraspanin receptor located in MVBs and at the plasma membrane , and has been shown to interact with the HTLV-1 envelope glycoprotein in T-cells [39] . Thus , one could hypothesize that CD82 is used by HTLV-1 to enter DCs during cell-to-cell transfer , and maybe involved in the targeting of HTLV-1 to vesicles in DCs . This is currently under investigation in our laboratory . Indeed , localization of incoming viruses in vesicles in both susceptible and resistant DCs could reflect a distinct entry route in these cell types compared to T-cells , in which it is assumed that HTLV-1 entry occurs after fusion at the plasma membrane [40] . We observed that the level of the fusion receptor Glut-1 is similar in the three DC subsets , suggesting that HTLV-1 capsids could be delivered to the cytosol after fusion of the viral envelope with the membrane of the vesicles . Accordingly , both in susceptible DCs and in restricted DCs in which vesicles had been alkalized after treatment with chloroquine , Tax expression was detected . This indicates that the viral cycle can be completed after the virus had trafficked through the endocytosis pathway . In contrast , Tax was not detected in susceptible DCs in which vesicles had been acidified after treatment with DPI , thus in which incoming vesicular viruses had been inactivated . This suggests that viral fusion at the plasma membrane , even if it is present , does not allow a productive infection . Vesicular uptake of viruses may not be limited to DCs and could be a feature of cell-to-cell transmission . Indeed , such a mechanism was also reported during HIV-1 cell-to-cell transfer between T-cells [41 , 42 , 43] . In that case , viral fusion occurred in the vesicles of the target cells after the late maturation of Gag proteins , authorizing the interaction of HIV-1 envelope with its co-receptor and thus the release of HIV-1 capsids into the cytoplasm , and subsequent productive infection [44] . It is thus possible that similar entry pathways occur during HTLV-1 cell-to-cell transmission . We showed here that maturation of DCs strongly limited their productive infection . Interestingly , it was shown that stimulation of primary hepatocytes with TLR agonists was able to inhibit replication of woodchuck hepatitis B virus ( WHBV ) in an interferon-independent pathway [45] . In this study , restriction was not due to the expression of a soluble factor nor to activation of JAK/STAT pathway that is specific to IFN signaling , but was induced by TLR signaling via activation of the MAPK/ERK and PI-3K/Akt pathways . It was therefore suggested that TLR-induced proteins could act as restriction factors . We cannot exclude that maturation of DCs may induce HTLV-1 specific restriction factors . However , our results suggest that the first mechanism of HTLV-1 replication restriction relies on a post entry blockade and on specific viral trafficking . DC maturation is a complex process that modifies DC function such as antigen capture , antigen processing and presentation . Pathogen capture mainly occurs via macropinocytosis , a non-receptor mediated endocytic pathway . Macropinocytosis is a constitutive process in immature DCs [46] , while it is switched to an induced process in mature DCs [47] . Indeed , in the absence of pathogen-induced signaling , mature DCs have lower abilities to internalize pathogens compared to immature DCs . Interestingly , we showed here that HTLV-1 entry was blocked by genistein , an inhibitor of induced macropinocytosis but not by cytochalasin D , a drug acting on actin fibers elongation in mature but not in immature DCs . This suggests distinct actin requirements that may be relevant to mechanisms specific to constitutive versus induced macropinocytosis . These entry mechanisms may be consistent with the intracellular localization of HTLV-1 in the different subsets . Interestingly , earlier studies using electron microscopy analyses of MDDCs co-cultured with HTLV-1-infected lymphocytes , showed viral particles internalized in vesicles [6] , although the nature of these vesicles was not identified . Our results showed that in all DCs subsets , viruses were found in large vesicles also positive for tetraspanin but not for early endosome marker . This staining is evocative of MVB , and is reminiscent of the Virus-Containing Compartments ( VCC ) described in HIV-1 infected macrophages [48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56] . Thus , based on all our results , we propose a schematic model for HTLV-1 infection of DC subsets ( Fig 9 ) . When HTLV-1 encounters immature DCs , viral entry through constitutive macropinocytosis would lead to internalization of HTLV-1 in MVB-like compartments from which the neutral pH would authorize viral capsid exit and completion of the viral replication cycle ( Fig 9A , left panel ) . In contrast , in IFN-α DCs , entry through clathrin-mediated endocytosis and not through macropinocytosis would lead to trafficking in vesicles targeted to degradation ( Fig 9B ) . Finally , if DCs are matured before their interaction with HTLV-1 , entry using constitutive macropinocytosis would deliver viruses to vesicles with an acidic pH , that would impair HTLV-1 infection ( Fig 9A , right panel ) . Surprisingly , alkalization of vesicles from mature DCs using chloroquine restored HTLV-1 infection , but only partially with a percentage of infection around 60% lower than that observed in immature DCs . This suggests that in addition to the traffic in acidic compartments , induction of HTLV-1-specific restriction factors induced after DC maturation would insure an efficient blockade of HTLV-1 replication in mature DCs . Are these different outcomes of HTLV-1 infection in the different DC subsets important for the virus transfer to T-cells ? In the HIV-1 model , DCs are resistant to HIV-1 infection due to the presence of several restriction factors [57] , and thus most of the viral transfer from DCs to T-cells occurs by trans-infection , which does not require productive infection [58] . More importantly , DC-mediated trans-infection is more efficient when HIV-1 virions have been captured by mature DCs [31 , 59] . Interestingly , DC maturation with TLR agonists also renders them susceptible to X4-tropic HIV-1 infection , although they are still resistant to infection with R5-tropic HIV-1 [60] . This suggests that DC infection with HIV-1 is controlled by several factors that depend upon the DC maturation status and upon HIV-1 strains . Depending on the viral strain , transfer of HIV-1 from DCs to T-cells would use either trans-infection in the case of R5-tropic viruses or cis-infection in the case of X4-tropic viruses . In any case , mature DCs would be responsible for the efficient transfer to T-cells and rapid spreading of HIV-1 within individuals [32] . During HTLV-1 infection , interaction with DCs is also important for the establishment of the chronic infection of T-cells [15 , 61] . However , since this infection has not been linked to immune activation , HTLV-1 might more likely interact with immature DCs . Thus , our results showing that immature DC are the only subset able to transfer the virus to T-cells , in a process mainly based on their productive infection , is relevant to the in vivo situation . The nature of the DCs encountered by HTLV-1 upon primo-infection , which display a differential susceptibility to HTLV-1 infection , would therefore determine the efficiency of viral transmission to T-cells . Finally , in contrast to the dissemination of HIV-1 by mature DCs , our results strongly suggest that the restriction of HTLV-1 infection in mature DCs and more importantly , their inability to transfer the virus to T-cells might be an efficient way for the host to limit HTLV-1 spread .
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Human T-Lymphotropic Virus type 1 ( HTLV-1 ) is the etiological agent of Adult T-cell Leukemia/Lymphoma ( ATLL ) and HTLV-1-Associated Myelopathy/Tropical Spastic Paraparesis ( HAM/TSP ) . In chronically infected patients , the provirus is mainly detected in the CD4+ T-cell population . However , beside lymphocytes , HTLV-1 infects blood or monocyte-derived dendritic cells ( DCs ) in vitro . Moreover , we previously showed that DCs are more susceptible to HTLV-1 infection than autologous T-cells , suggesting that DCs might be the first cells to be infected upon primo-infection , and important intermediaries for the viral spread to surrounding lymphocytes . Interestingly , different DC subsets are found in the blood or in the mucosa , the two entry routes used by HTLV-1 during infection of new individuals . In this study , we show for the first time that the different DC subsets are not equally susceptible to HTLV-1 infection . Furthermore we demonstrate that DC subsets with an increased ability to capture HTLV-1 virion are restricted to HTLV-1 productive infection . This restriction is linked to DC maturation but not to the antiviral activity of IFN-α produced by resistant DCs . Finally , we demonstrate that efficient viral transmission to T-cells is dependent upon DC productive infection rather than trans-infection . Altogether , our results indicate that the nature of the DCs encountered by HTLV-1 upon primo-infection determines the efficiency of viral transmission to T-cells , which may condition the fate of infection .
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2017
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Dendritic cell maturation, but not type I interferon exposure, restricts infection by HTLV-1, and viral transmission to T-cells
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High-throughput sequencing has enabled genetic screens that can rapidly identify mutations that occur during experimental evolution . The presence of a mutation in an evolved lineage does not , however , constitute proof that the mutation is adaptive , given the well-known and widespread phenomenon of genetic hitchhiking , in which a non-adaptive or even detrimental mutation can co-occur in a genome with a beneficial mutation and the combined genotype is carried to high frequency by selection . We approximated the spectrum of possible beneficial mutations in Saccharomyces cerevisiae using sets of single-gene deletions and amplifications of almost all the genes in the S . cerevisiae genome . We determined the fitness effects of each mutation in three different nutrient-limited conditions using pooled competitions followed by barcode sequencing . Although most of the mutations were neutral or deleterious , ~500 of them increased fitness . We then compared those results to the mutations that actually occurred during experimental evolution in the same three nutrient-limited conditions . On average , ~35% of the mutations that occurred during experimental evolution were predicted by the systematic screen to be beneficial . We found that the distribution of fitness effects depended on the selective conditions . In the phosphate-limited and glucose-limited conditions , a large number of beneficial mutations of nearly equivalent , small effects drove the fitness increases . In the sulfate-limited condition , one type of mutation , the amplification of the high-affinity sulfate transporter , dominated . In the absence of that mutation , evolution in the sulfate-limited condition involved mutations in other genes that were not observed previously—but were predicted by the systematic screen . Thus , gross functional screens have the potential to predict and identify adaptive mutations that occur during experimental evolution .
There is a great need for rapid , high-throughput methods to identify adaptive mutations among the growing list of mutations identified in experimentally evolved populations . Several recent ‘Evolve and Resequence’ studies [1] , in which populations or clones were sequenced after adaptation to a specific condition , have dramatically increased the list of mutations associated with adaptation to different conditions [2–12] . Within that growing dataset , only a few mutations have actually been confirmed experimentally as adaptive . Some large-scale microbial studies have distinguished adaptive mutations from background neutral mutations on the basis of statistical approaches based on the frequency , enrichment , and recurrence of specific mutations [2 , 3 , 9 , 13–17] . Such statistical approaches entail substantial false-positive and false-negative rates . Dissecting the fitness effects of every mutation observed in an evolved population is tedious , although generally straightforward . For example , mutations can be reassorted via a genetic cross , and the fitness of segregants carrying individual mutations or combinations thereof can be assessed . That strategy has been used with a few laboratory-evolved Saccharomyces cerevisiae clones , demonstrating that evolved clones isolated after several hundred generations of propagation in nutrient-limited conditions often carry one or two adaptive mutations [18 , 19] . However , such methods are difficult to scale . An alternative approach is computational models that predict the effects of mutations . A recent study directly compared several popular scoring metrics and found them to be far inferior to experimental testing of fitness [20] . Given its amenability to high-throughput experiments , S . cerevisiae is particularly well suited for genome-wide assessments of the relationship between genetic variation and fitness . As an alternative , we turned to currently available systematic mutant collections . Researchers have created barcoded strain collections in which thousands of genes are systematically deleted or amplified to uncover gene functions ( review in [21] ) . These strain collections have been used to mimic important classes of mutations such as those resulting in loss-of-function ( LOF ) , gene knockdown , gene duplication , or changes in expression level [22–26] . Missing from these collections are mutations that are not mimicked by copy-number changes , such as mutations in coding regions that generate new protein activities or LOF effects more subtle than those of simple knockout or knockdown alleles . Despite the large number of studies that have used the barcoded collections to detect deleterious effects such as haploinsufficiency , dosage sensitivity , synthetic lethality , drug sensitivity , and various other phenotypes [24 , 27–35] , only a few studies have looked at beneficial mutations . One study quantified antagonistic pleiotropy in a variety of laboratory conditions and determined that whereas 32% of deletion strains were less fit than a wild-type reference , only 5 . 1% of the strains were more fit [36] . Another study identified a large number of heterozygous deletions as beneficial but also demonstrated that the haploproficiency was context-dependent [23] . The further application of systematic amplification and deletion collections to study adaptive mutations will expand our understanding of that unique and important class of mutations . Most previous studies used phenotypic data to investigate gene function . The adaptive phenotypes displayed by the systematic amplification and deletion collections can also be used to investigate questions from an evolutionary genetics perspective . The ability to identify beneficial mutations en masse allows us to survey one set of beneficial mutations that could drive adaptation . A greater understanding of adaptive mutations will allow us to begin to address a number of open questions . How does the distribution of fitness effects differ across conditions ? What determines which of the possible beneficial mutations actually reach high frequencies in evolving populations ? Does the hierarchy of fitness among mutations drive those patterns strictly , or do other factors play a role ? How can we better design selective conditions to achieve specific evolutionary outcomes ? We sought to address these questions using a system that combines high-throughput functional genomics and experimental evolution . We first measured the fitness of deletions and amplifications of almost all of the genes in the S . cerevisiae genome , which we refer to as the amplification and deletion ( AD ) set , using pooled competitions of thousands of mutants under selection in nutrient-limited continuous culture in chemostats followed by barcode sequencing . We found that while most of the AD mutations were neutral or decreased fitness , ~500 of them increased fitness in at least one condition and hence represented potential adaptive mutations . We next compared the fitness values from the AD set to a set of mutations identified in experimental evolution studies , which we refer to as the evolutionary ( E ) set . By comparing the E set with the results from the AD set , we recapitulated five of eight previously verified beneficial mutations and predicted that on average at least one third of the mutations present in the evolved strains were likely to positively affect fitness . In sulfate-limited conditions , mutations in one gene dominated the distribution of fitness effects in both the AD set and the E set . In glucose-limited and phosphate-limited conditions , the distributions of fitness effects were characterized by a large number of beneficial mutations of smaller effect . We found that the distribution of fitness effects in the sulfate-limited condition could be modified by precluding the dominant adaptive solution , which allowed the evolving populations to explore alternative beneficial mutations predicted based on the AD set . This study takes an initial step towards determining the fitness effects of candidate adaptive mutations , substantially improving on the throughput of other experimental approaches as well as on the accuracy of purely statistical or computational approaches .
We measured the fitness effects of single-gene changes in copy number for ~80% of the genes in the yeast genome using pooled competitions of five different collections of yeast strains in three different nutrient-limited conditions followed by Illumina-based barcode sequencing ( [22] Fig 1; S1 Table ) . Two of the collections , the deletion collections , consisted of haploid and heterozygous diploid strains , respectively; in each strain , one copy of a single gene was replaced by a selectable marker with a unique DNA barcode [31] . One ( control ) collection consisted of ~2 , 000 otherwise isogenic wild-type strains created by placing unique barcodes at a single , neutral genomic location [32] . The other two collections consisted of diploid strains bearing a low or high copy-number plasmid , respectively; each plasmid contained a single gene , the corresponding native promoter , and a unique barcode [29 , 30] . We conducted a total of 30 continuous-growth competition experiments with phosphate , glucose , and sulfate , respectively , as the limiting nutrient . We screened each yeast collection twice in each condition ( S1 Fig ) . In each screen , we mixed all of the strains from a single collection together at approximately equal proportions in a single culture vessel and measured the proportion of each strain at time points throughout the course of ~20 generations of propagation ( S2 Fig ) . We used large populations ( ~109 cells ) to overcome the stochastic effects of drift [23] . We measured the fitness over a relatively short period of time to limit the effects of de novo mutations , sampling the populations every three generations to maximize the accuracy of the fitness quantification . We measured the frequency of each strain at each time point using barcode sequencing ( barseq; S3 Fig ) [22] . We note that this experiment design does not allow us to control for mutations already present in the strains before the onset of the competition experiment . We made a total of 100 , 853 measurements of relative fitness , ranging from -36 . 5% to 42 . 8% , based on an average of 462 reads per gene per screen . We then created fitness distributions of the AD strains in each of the three selective conditions ( Fig 2; Table 1 and S2 Table ) . We were able to measure the fitness effects of copy-number changes of 2 , 133 genes in all 12 experiments and to measure the fitness effects of copy-number changes of an additional 2 , 953 genes in at least one experiment . To determine the inherent noise originating from the strain construction , pool generation , competition , and sequencing , we quantified the relative fitness of the strains in the control collection . The fitness distribution was tightly centered on 0; 98 . 2% of the control strains had fitness between -10% and +10% ( Fig 2; S3 Table ) . We therefore used fitness values of ±0 . 10 ( corresponding to a 10% change in fitness ) as the cutoffs to identify strains in the other four collections that had a significant fitness benefit or deficit compared with the control strains . Previous analyses showed that a beneficial mutation resulting in a 10% fitness increase will reach 5% of the population in ~200 generations and will fix in ~500 generations [37 , 38] , which suggests that mutations causing a fitness increase of less than 10% would rarely be identified as beneficial in our experimental evolution regime . Most of the deletion and amplification strains displayed wild-type or near wild-type fitness . The fitness distributions of the AD strains were broader than that of the control strains . Based on the 10% cutoff values , the AD collections were enriched for strains with decreased fitness ( n = 1693 ) or increased fitness ( n = 506 ) compared with the control collection ( n = 19 and 80 , respectively; Chi square , p<0 . 001 and p = 0 . 0033 , respectively; Fig 2 ) . Of the strains with increased fitness ( S5 Table ) , 223 had increased fitness in sulfate-limited conditions , 210 in glucose-limited conditions , and 73 in phosphate-limited conditions . Only a small fraction of strains had increased fitness in more than one condition ( n = 25 ) . The 506 strains with increased fitness represented copy-number changes in a total of 458 genes ( S5 Table ) . Seventy three percent of those strains were from the plasmid collections , which comprised just 47% of the total strains tested , suggesting that duplications of single genes are more likely than deletions to produce fitness gains . The AD set only recreates gross dosage changes and not mutations acting via different mechanisms; however , our screen identified five of eight genes in which beneficial mutations were previously identified in evolution experiments ( considering only those known beneficial mutations with matching strains in the AD set ) : the amplification of SUL1 and LOF mutations affecting SGF73 in sulfate-limited conditions and mutations affecting MTH1 , WHI2 , and GPB2 in glucose-limited conditions [18 , 19 , 39] . These results demonstrate that the AD collections were able to replicate the phenotypes caused by some beneficial mutations , although they failed to replicate those caused by others ( e . g . , mutations in PHO84 , IRA1 , and RIM15 ) . Among the genes associated with a fitness increase in the AD set , SUL1 was associated with the greatest fitness ( 42 . 8% in the sulfate-limited condition for a strain carrying the high-copy plasmid ) . In previous experiments , SUL1 amplification was recurrently selected during evolution in sulfate-limited conditions , and increasing the SUL1 copy number via expression on both low-copy and high-copy plasmids increased fitness [39 , 40] . Our screen also identified one gene that was previously identified as the cause of putative secondary adaptive effects: BSD2 , a gene involved in the downregulation of the metal transporter proteins Smf1 and Smf2 [41 , 42] and located 6kb upstream of SUL1 on chromosome 2 . The amplification of BSD2 on a low-copy plasmid increased fitness by 5% and 12 . 4% in the sulfate-limited and glucose-limited conditions , respectively . In previous studies of the SUL1 amplicon [39 , 40] , we detected only three independent clones where the SUL1 amplicon excluded BSD2 . The fitness of each of 13 strains harboring an amplification of both SUL1 and BSD2 was higher than the fitness of three strains harboring an amplification of SUL1 but not of BSD2 [40] , a result that was further supported by a fitness analysis of synthetic amplicons [19] . The reintroduction of BSD2 using a low-copy plasmid into one of the three strains harboring only SUL1 amplification increased the fitness in the sulfate-limited condition by 6 . 1% ( from 37 . 7% to 43 . 8% ) , suggesting that the fitness effects of the two mutations are additive . These results demonstrate that the AD screen is able to detect adaptive mutations even of small effect , although our control experiments suggest that the identification of such mutations is likely subject to a higher false-positive rate than the identification of beneficial mutations of larger effect . A decrease in the cutoff to ±5% resulted in the identification of increased or decreased fitness in 15% of the control strains and increased the number of beneficial mutations identified in the AD collections by six fold ( n = 3143 ) . Although the less stringent cutoff still identified significantly more beneficial mutations in the AD collections than in the control collection ( Chi square , p<0 . 001 ) , we decided to use the more stringent cutoff to focus on the mutations with the highest impact . Next , we sought to apply the knowledge gained from the screen of the AD set to the hundreds of de novo mutations identified in laboratory evolution experiments ( E set ) . Our goal was to determine which of the hundreds of possible adaptive mutations identified in the AD set were actually selected during experimental evolution . To compare the genes in the AD set that we identified as potential sites of adaptive mutations to the genes in which mutations actually occurred during experimental evolution , we first needed to create a comprehensive database of mutations identified in yeast evolution experiments . To do so , we identified and resequenced the mutations that occurred in yeast evolution experiments carried out by our lab [39 , 40] . The experiments involved the propagation of haploid or diploid prototrophic strains of S . cerevisiae for 122 to 328 generations in continuous-culture conditions identical to those in which our AD screens were performed ( six sulfate-limited , six phosphate-limited , and four glucose-limited populations . We detected 150 mutations by whole-genome sequencing of 16 populations and 34 clones ( See Materials and Methods ) . We then collected a large set of mutations from various Evolve and Resequence studies of yeast performed in a variety of conditions [2–4 , 8 , 40 , 43] . Thus , we compiled a total of 1 , 167 mutations in 1 , 088 genes from 106 long-term evolution experiments conducted in 11 different conditions in nine previous studies . We refer to this set of mutations as the E set ( S4 Table ) . The features of the previous studies and the resulting mutations are summarized in Table 2 . The complete list of mutations , their frequencies , and their predicted effects are given in S4 Table . The E set did not include chromosomal rearrangements , because those events were not always reported in the previous studies . Two recent studies showed that LOF mutations were frequently selected in populations of haploid yeast [2 , 3] . Based on a small number of mutations , another study concluded that mutations affecting cis-regulatory regions are co-dominant in heterozygous diploids [44] . Although those results are suggestive , too few Evolve and Resequence studies have been performed in diploid yeast to draw firm conclusions about the effects of ploidy on the distribution of fitness effects . We divided the E set into four groups based on SNPeff , an annotation program that predicts the functional impact of the mutation of a gene , as follows [45]: ( 1 ) high-impact mutations , such as frameshifts or the gain or loss of a start or stop codon; ( 2 ) moderate-impact mutations , such as non-synonymous substitutions or the deletion or insertion of a codon; ( 3 ) low-impact synonymous mutations; and ( 4 ) modifiers , corresponding to mutations upstream of a gene or within intergenic regions . We found that different types of mutations tended to be present in haploid and diploid strains , respectively ( Fisher’s exact test , p<0 . 001 , corrected for multiple tests ) . We confirmed previous findings showing that in haploids , the main category of mutation is LOF mutations involving the gain of a stop codon ( Chi square , p = 0 . 003; Table 3 ) . In contrast , LOF mutations were relatively rare in diploid strains , which were instead enriched for intergenic and upstream mutations ( Chi square , p<0 . 001; Table 3 ) , suggesting that amplifications and gain-of-function ( GOF ) mutations are more important in the diploid background . This result is consistent with our previous observations that evolved diploid strains contain more and larger variations in gene and chromosome copy numbers than evolved haploid strains [39] . Using only the mutations identified in glucose-limited conditions from the E set , we determined that the mutational signature was different between haploids and diploids in glucose-limited conditions ( Fisher’s exact test , p<0 . 001 ) , with an enrichment of LOF mutations among the haploids ( Chi-square , n = 224 , p<0 . 001 ) . Conversely , the mutations identified in phosphate-limited conditions in the E set displayed only marginal enrichment of LOF mutations ( Fisher exact test , n = 54 p = 0 . 053 ) , while those identified in sulfate-limited conditions displayed no enrichment of LOF mutations ( n = 100 ) . The different types of mutations observed between ploidies are likely explained by the tendency of LOF mutations to be recessive [46 , 47] compared with mutations that increase gene expression , which are more likely to have an effect in heterozygotes . Although loss of heterozygosity has been observed in diploid populations [39 , 46] , such cases are relatively rare . To test that directly , we examined the fitness effects of 55 beneficial deletions identified in both the haploid and the diploid AD collections and found that those deletions indeed tended to be recessive , causing on average a 9 . 0% ± 4 . 6 greater fitness increase in haploids than in diploids . Seven of the 55 deletions ( WSC3 , TIM12 , IPT1 , MMS22 , NDL1 , PBS2 , and YLR280C ) had the same fitness effect in haploids and diploids , indicating that a subset of LOF mutations can in fact be dominant . Overall , LOF mutations appeared to provide a greater adaptive benefit in haploid strains than in diploid strains , which is consistent with prior results . Recurrence-based models , which assume that oncogenes are recurrently mutated among independent samples , are one of the most widely used approaches to identify putative driver genes in cancer [48–50] . Recurrent adaptive trajectories have also been frequently observed in microbial evolution [2 , 3] , leading to the discovery of drivers of adaptation such as SUL1 , HXT6/7 , and RIM15 in S . cerevisiae and rpoS in Escherichia coli [3 , 13 , 14 , 39 , 51] . Of the 1 , 088 genes in the E set , 154 were mutated in more than one sample , and 19 were mutated in more than five samples ( Fig 3A , S4 Table ) . The recurrently mutated genes were highly enriched with high-impact mutations ( Fisher’s exact test , p<0 . 001; Fig 3B ) and tended to be longer than genes that were mutated in only one sample ( Wilcoxon rank-sum test , p<0 . 001; S4A Fig ) . There are several tools that correct for gene length to detect true adaptive mutations and discard false-positives [52] . We decided to use a different approach by inferring the fitness effects of mutations using the results from the AD screen . Convergent evolution has been widely used as a predictor of evolutionary outcomes . We decided to compare the list of recurrently mutated genes from the E set to the results of the AD screen , restricting our analysis to experiments performed in the same conditions . In the E set , 36 genes were mutated twice in at least one of the three conditions used in the AD screen . Ten of those genes were associated with a fitness increase of at least 10% in at least one collection in the AD set ( SUL1 and SGF73 in the sulfate-limited condition and GPB2 , PBS2 , AEP3 , MUK1 , HOG1 , ERG5 , SSK2 , and WHI2 in the glucose-limited condition ) . Eight more genes were associated with a fitness increase that did not meet our stringent cutoff of 10% but exceeded 5% . The remaining 18 genes were either absent from the collections ( n = 12 ) or associated with no fitness increase in the corresponding condition ( n = 6 ) . The six genes that showed no fitness effect in the AD set could have been recurrently mutated by chance . Alternatively , the mutations in the E set could have provided fitness increases that were not mimicked by the AD collections , which could be the case for mutations that caused partial LOF or that resulted in a novel function , or due to fitness-changing errors or secondary mutations in the relevant strains . Another possibility is that those mutations only provided a benefit in a specific genetic background or in concert with other mutations . Strains from the AD set could also have accumulated additional mutations that mask the true effect of the query mutation . A large number of genes identified in the E set were mutated only in a single population . Because the number of Evolve and Resequence experiments is relatively small , akin to a non-saturating genetic screen , some adaptive mutations are likely to be found as singletons and would therefore be missed by a recurrence-based detection method . The E set contained 155 genes that were mutated only once in glucose-limited , sulfate-limited , or phosphate-limited conditions . We used the data from the AD set to determine if those singletons might be associated with a fitness increase in the corresponding environment . Of the 155 singletons , only three had a fitness effect of at least 10% when amplified or deleted: amplifications of NMA111 in the sulfate-limited condition and CLN2 and YOR152C in the glucose-limited condition . Thirty-eight more genes had a fitness effect between 10% and 5% ( average fitness = 7 . 2±1 . 1 ) . Cln2 is one of the three G1 cyclins and promotes cell-cycle progression . The expression of G1 cyclins is regulated in response to nutrient limitations; in particular , it is repressed in the presence of glucose [53] . These results show that while convergent evolution is useful for identifying adaptive mutations , some singletons might also have fitness effects and should not be overlooked . Only a small portion of the singleton mutations were predicted by the AD screen to be beneficial , suggesting three possibilities , which are not mutually exclusive: the relevant data are missing from the AD screen ( only 52 of the 202 genes with singleton mutations were represented in all four collections and all three conditions used for the AD screen ) ; the AD screen does not accurately reflect the fitness of these point mutations; or the singletons were increasing in frequency in the evolved populations due to the presence of a beneficial mutation elsewhere in the genome , a phenomenon known as hitchhiking . If the first or second explanation were true , many of the evolved samples should lack mutations predicted to be adaptive by the AD screen , because the AD screen would have a high false-negative rate . If most of the singletons were the result of hitchhiking , all of the evolved samples should carry mutations predicted to be beneficial by the AD screen in addition to the neutral or weakly deleterious hitchhiker mutations . In order to determine the relative contributions of these explanations , we predicted the number of adaptive mutations each population and clone in the E set should carry based on the frequency of recurrence in the E set and the fitness data from the AD set . We determined that each clone or population in the E set carried at least one adaptive mutation predicted by the AD screen , which is consistent with the modest false-negative rate for the AD screen . Each sample in the E set contained on average 1 . 8 ( 2 . 2 per population and 1 . 4 per clone ) adaptive mutations predicted by the AD screen , representing 35% of the total mutations identified in the E set ( Fig 4A–S6 Table ) . There was no difference in the prevalence of predicted adaptive mutations among the three selective conditions ( S4B Fig ) . That result is consistent with previous reports of frequent hitchhiking by neutral or deleterious mutations [2 , 51 , 54 , 55] . Our estimate largely agrees with the results of detailed genetic analyses of mutations carried by evolved strains , which found that one third of the single-gene mutations among a total of five evolved clones were associated with a fitness increase [18 , 19 , 39] . Thus , by combining the data from the AD set and the E set , we were able to generate a more comprehensive list of adaptive mutations in evolved populations as well as estimate the genomic reservoir of beneficial mutations that were not detected . We conclude that evolution is partly predictable based on the repeatability of adaptive mutations among independent populations and reflects , at least in part , the fitness distribution of possible mutations , as mimicked by genome-wide screens of gene deletions and amplifications . The E set defined a set of 28 genes that were the sites of adaptive mutations with large effects ( based on the classification of mutations present in the AD set ) , which we consider to be candidate driver genes . Three of the candidate driver genes were mutated in only one sample , and 25 were mutated repeatedly among different samples . The AD screen identified a large number of potential sites of beneficial mutations that were a single mutational step away from the ancestral genotype [56] . To determine what differentiates the actual mutational spectrum from the pool of potential beneficial mutations , we excluded the genes in the E set that harbored mutations that were predicted to be beneficial based on the AD screen ( n = 28 ) and examined the remaining genes that were associated with fitness increases in the AD screen ( n = 430 ) . Given the population sizes ( 105 to 1010 cells ) and numbers of generations ( 50 to 1000 ) in the evolution experiments and the size of the yeast genome ( ~12 megabases ) , it is likely that every base in the genome was mutated at least once at some point among the ensemble of experiments in the E set . It therefore seems unlikely that mutations in the 430 genes identified in the AD screen as potential sites of adaptive mutations failed to occur at some point in the evolution experiments , although there was a greater likelihood that mutations mimicking the plasmid-based amplifications actually failed to occur , because point mutations that significantly increase gene expression might simply not exist in some promoter regions [57] . Furthermore , gene-amplification rates are generally biased by genomic-architecture constraints , such as proximity to repeat sequences , and the fitness effects of multigenic amplicons are complicated by the contributions of genes linked to the driver gene [19] . In order to better understand those issues , we compared the condition-specific fitness effects of the AD mutations that matched E-set mutations in the same condition with those of the AD mutations that did not match any E-set mutations in the same condition . In the glucose-limited condition , there was no difference on average between the fitness effects of the AD mutations with and without matching E-set mutations ( Fig 5A ) . In the sulfate-limited condition , the AD mutations with matching E-set mutations had greater fitness effects on average than those without matching E-set mutations ( Wilcoxon rank-sum test , p = 0 . 001; Fig 5A ) . Consistent with previous findings , SUL1 dominated the fitness distributions in sulfate-limited conditions in both the AD set and the E set ( Fig 5B ) . When the SUL1 amplifications were excluded from the comparison of AD mutations with and without matching E-set mutations , the AD mutations with matching E-set mutations still had greater fitness effects on average than those without matching E-set mutations ( Wilcoxon rank-sum test , p = 0 . 05 ) . Other highly beneficial mutations ( with >20% fitness increase ) such as amplifications of MAC1 and PHO3; encoding proteins implicated in copper and phosphate-sulfate metabolism , respectively; appear to be potential drivers of evolution but have not been identified in evolved populations ( Fig 5B; [2 , 58] ) . That suggests that , at least under sulfate-limited conditions , adaptation can be predicted based on the fitness effects of potential single-gene mutations , with the mutations providing the largest increase in fitness being the most likely to reach high frequencies . Although fewer clones and populations have been sequenced from phosphate-limited evolution experiments , all of the beneficial mutations in that condition in the E set could be predicted based on recurrence . Conversely , in glucose limitation , a variety of beneficial mutations with smaller fitness effects appear to be possible and were indeed observed in evolved populations . Condition-dependent or genome-wide variation in mutation rates could bias adaptive outcomes relative to the distribution of fitness effects seen in the AD screen [2] . The lack of observed mutations in the E set corresponding to many of the genes identified by the AD screen as potential sites of beneficial mutations likely reflects a combination of many factors , including random chance , epistatic interactions , strain background differences , or a failure of the AD set to adequately recapitulate the fitness of de novo mutations . Clonal interference is also likely to play a role . We asked which mutations would be selected in sulfate-limited conditions if SUL1 amplification were not possible . Alternative adaptive mutations might only rarely reach high frequencies in sulfate-limited conditions because of the strong fitness effects of SUL1 amplification . We hypothesized that in the absence of the SUL1 amplification , a variety of alternative mutations of smaller effect would be selected , an outcome more similar to the pattern observed in glucose limitation . We analyzed two populations that lacked SUL1 amplifications ( Fig 6A , population s611 and s612 S4 Table ) but showed fitness gains after 200 generations of evolution in sulfate-limited conditions . The fitness gains of those populations ( ~30%; Fig 6B ) were near the lower end of the range of fitness gains in previously studied clones harboring SUL1 amplifications ( 37–53% ) [40] . To establish which mutations were responsible for the fitness gains in the absence of SUL1 amplification , we performed whole-genome sequencing of the populations isolated at generation 200 . We detected two independent , non-synonymous mutations ( N263H and N250K ) in the coding region of SUL1 in both populations ( S4 Table ) . We inserted each of those mutations into wild-type strains and found that N250K increased fitness by 23 . 1% ( ±2 . 3% ) and N263H increased fitness by 17 . 7% ( ±1 . 22% ) . In addition , one population ( s611 ) harbored a nonsense mutation in SGF73 , a gene previously identified as the site of an adaptive mutation ( S4 Table ) , and the other population ( s612 ) , harbored a 5 . 1 kb deletion on chromosome IV ( 587839–592999 ) affecting four genes ( FMP16 , PAA1 , IPT1 , and SNF11; Fig 6C ) . In the AD screen , deletions of IPT1 and SNF11 were beneficial in glucose-limited and sulfate-limited conditions ( 10–20% fitness increase ) , but mutations in those genes were not included in the E set ( Fig 5B ) . Because IPT1 and SNF11 are adjacent to one another on the chromosome , we suspected that one of them might be a false positive , resulting from a known artifact called the neighboring gene effect [59] . By employing complementation testing using centromeric plasmids , we found that the deletion of either gene increased fitness ( Fig 6D ) . Snf11 is a subunit of the SWI/SNF chromatin remodeling complex , which is known to act as a tumor suppressor in humans [60] . Ipt1 is implicated in membrane-phospholipid metabolism and nutrient uptake [61] . Thus , our results showed that adaptive mutations predicted by the AD screen can be relevant , even when they are rarely identified in evolution experiments . We predict that additional evolution experiments that preclude the possibility of SUL1 amplification will reveal even more alternative fitness peaks .
The recurrence-based identification of adaptive mutations provides an incomplete picture of the impact of mutations on cellular fitness [62] . In agreement with previous reports [2 , 3 , 9 , 13 , 39 , 51] , we found that experimental evolution resulted in non-uniform selection of mutations across the genome ( Fig 3A ) . It is currently impossible to screen all possible mutations , so we used whole-gene amplifications and deletions as a first step in approximating the spectrum of potential mutations . We believe that this is a reasonable approach given the prevalence of gene copy-number changes and LOF mutations in experimentally evolved populations [2 , 3 , 39] , and our success in identifying genes with previously validated high fitness mutations . Our results can be used to prioritize the experimental validation of potentially adaptive mutations found in evolved strains . The AD screen allowed us to discriminate between adaptive mutations and neutral or passenger mutations in evolved populations . Based on the results of the AD screen combined with the information provided by the E set , we predict that ~35% of the mutations appearing in laboratory-evolved populations are likely beneficial . As expected , that number is higher than previous estimates of the baseline rate of beneficial mutations ( 6–13% ) based on mutation-accumulation experiments with yeast [63] . The frequencies of different categories of adaptive mutations ( e . g . , LOF or altered level of expression ) differed between haploids and diploids . In agreement with previous work [3] , we detected an excess of LOF mutations in haploids and an excess of mutations that likely modify gene expression in diploids . Our results agree with those of several studies showing that mutations have greater fitness effects in haploids than in heterozygous diploids [64] and that the frequency of fixation is higher in diploids [37] . Mutations affecting cis-regulatory regions have often been described as co-dominant , whereas most mutations in coding regions cause LOF and are recessive [44] . Large copy-number variations ( CNVs ) have been shown to be enriched in diploid backgrounds compared with haploid backgrounds [39] , suggesting that a diploid context might buffer the detrimental effects of aneuploidy and CNVs seen in haploids [65 , 66] . These results emphasize the point that evolutionary trajectories are constrained by ploidy and that patterns observed at a particular ploidy are unlikely to act universally . We also observed that the majority of the beneficial mutations from the AD set are from the plasmid collection , further illustrating the importance of gene amplifications in adaptation . Despite our promising results , functional screens using single-gene amplifications and deletions have several limitations . The available yeast collections are based on single-gene copy-number changes and do not allow the study of mutations in protein-coding regions that are not mimicked by dosage changes , mutations in non-genic functional elements , or combinations of mutations . To explore the importance of non-genic regions and small genes that are not present in the yeast collections , billions of individual and combined mutations need to be generated in a comprehensive way , similar to the deep mutational scanning of proteins [67] , the Million Mutation Project [68] , or newly created resources such as the tRNA deletion collection [69] and large telomeric amplicons [19] . Previous studies in microbial and viral systems have provided evidence for both antagonistic and synergistic epistasis among beneficial mutations [36 , 70–73] . Synthetic genetic arrays and similar approaches using the S . cerevisiae deletion collection have been used to characterize negative and positive epistatic relationships , and a nearly complete yeast genetic-interaction network has been generated using double mutants [74 , 75] . Further studies using those resources will allow us to move beyond single-gene effects and begin to understand how interactions among multiple genes in CNVs and combinations of mutations shape the distribution of fitness effects . By expanding and developing these techniques , the increase of studies combining long-term experimental evolution and whole-genome sequencing will likely reveal additional mutational effects .
The MoBY-ORF collection of centromeric ( CEN ) plasmids in E . coli was obtained from Open Biosystems and stored at -80°C as individual strains in 96-well plates . The plates were thawed and robotically replicated onto LB-Lennox ( Tryptone 10g , yeast extract 5g , NaCl 5g ) agar plates containing 5Δg/ml tetracycline , 12 . 5μg/ml chloramphenicol , and 100μg/ml kanamycin and grown at 37°C for 14 h . Colonies were harvested by addition of 5ml LB-Lennox to each plate and subsequently pooled . Glycerol ( 50% ) was added , and 1ml aliquots containing 2×109 cells were frozen at -80°C . Plasmid DNA was prepared from the E . coli pool and then used to transform the S . cerevisiae S288C derivative strain DBY10150 ( ura3-52/ura3-52 ) using a standard lithium acetate protocol . The yeast cells were selected on -URA and 200μg/ml G418 plates , resulting in 88 , 756 transformants , which were then pooled together , giving an average library coverage of ~20× . The MOBY-ORF v2 . 0 collection ( 2 micron plasmid ) was obtained from the Boone lab and crossed for 3 h with YMD1797 ( MATα , leu2Δ1 ) . Clones were selected twice on MSG/B and G418 ( 200μg/ml ) and then pooled together . The MATa/MATα SGA Marker ( MM2N ) collection was obtained already pooled from the Spencer lab . The MATa SGA Marker ( MM1N ) library was obtained frozen from the Caudy lab; the strains were selected on -LYS and -MET and then pooled together . The barcoder collection was obtained frozen from the Nislow lab . The plates were thawed at room temperature , replicated onto YPD and G418 ( 200μg/ml ) , and crossed with FY5 ( MATα , prototrophic strain ) . The strains were then selected twice on MSG/B+G418 ( 200μg/ml ) and pooled together . A list of the strains used in this study can be found in S1 Table . Previously described nutrient-limited media ( sulfate-limited , glucose-limited , and phosphate-limited [13 , 39 , 76] ) were complemented with uracil and histidine ( 20mg/l ) for the SGA Marker pools . For each competition , a 200ml culture was inoculated with 1ml of a single pool ( ~2×107 cells ) . Two competition experiments were performed for each pool . The cultures were grown in chemostat culture at 30°C with a dilution rate of 0 . 17±0 . 01 volumes/h . The cultures were grown in batch for 30h and then switched to continuous culture . The continuous cultures reached steady state after ~10 generations and were maintained for an additional 20 generations ( S2 Fig ) . A sample taken just after the switch to continuous culture was designated generation 0 ( G0 ) . Subsequent samples were harvested every three generations thereafter . Samples for cell counts and DNA extraction were passively collected twice daily . Genomic DNA was extracted from dry , frozen cell pellets using the Smash-and-Grab method [77] . Plasmids from the MoBY collections were extracted with a Qiagen miniprep protocol ( QIAprep Spin mini prep kit; Qiagen , Hilden , Germany ) with the following modification: 0 . 350mg of glass beads were added to a cell pellet with 250μl buffer P1 and vortexed for 5min . Then , 250μl buffer P2 was added to the mix of cells and beads , and 350μl buffer N3 was added to the solution before centrifuging for 10 min . The supernatant was then applied to the Qiagen column following the recommendation of the Qiagen miniprep kit . Plasmid DNA was then eluted in 50μl sterile water . Genomic DNA was extracted from dry cell pellets by the Smash-and-Grab method and used for barcode verification of single strains by PCR amplification and Sanger sequencing as previously described [40] . For each sample , the plasmid copy number was determined using the copy number of KanMX relative to the copy number of DNF2 , a gene located on chromosome 4 and absent from the two MoBY collections ( see S6 Fig ) . The primers used are listed in S8 Table . Microarray assays , whole-genome sequencing , SNP calling , and qPCR analysis were performed as previously described [40] . The microarray data have been deposited in the Gene Expression Omnibus repository under accession GSE58497 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=sjgtsgwmdhajdud&acc=GSE58497 ) . The fastq file for each library is available from the NCBI Short Read Archive with the accession number PRJNA248591 and BioProject accession PRJNA249086 . Amplifications of the barcodes were performed using a modified protocol [22] . Uptag barcodes were amplified using primers containing the sequence of the common barcode primers ( bold ) , a 6-mer tag for Illumina multiplexing ( in italics ) , and the sequence required for attachment to the Illumina flowcell ( underlined; S8 Table ) . PCR amplifications were performed in 100μl , using Roche FastStart DNA polymerase with the following conditions: 94°C for 3min; 25 cycles of 94°C for 30s , 55°C for 30s , and 72°C for 30s; followed by 72°C for 3min . PCR products were then purified using the Qiagen MinElute PCR Purification kit ( cat . No . 28004 ) , quantified using a Qubit fluorometer , and then adjusted to a concentration of 10μg/ml . Equal volumes of normalized DNA were then pooled and gel purified from 6% polyacrylamide TBE gels ( Invitrogen ) using a soak and crush method followed by purification and concentration using Qiagen Qiaquick PCR purification . After quantification using a Qubit fluorimeter , the libraries were sequenced using the standard Illumina protocol as multiplexed , single-read , 36-base cycles on several lanes of an Illumina Genome Analyser IIx ( GAII ) . Thirty multiplexed libraries ( UPTAGS only ) were sequenced on several lanes of an Illumina GAII . An average of 25 , 664 , 072 million reads per library that perfectly matched the molecular barcodes were obtained ( S9 Table ) . The fastq file for each library is available from the NCBI Short Read Archive with the accession number PRJNA248591 and BioProject accession PRJNA249086 ( S10 Table ) . The 6-mer multiplexing tags were reassigned to a particular sample using a custom Perl script ( S1 File ) . Then , each barcode was reassigned to a gene using a standard binary search program ( programmed in C , S2 File ) . Only reads that matched perfectly to the reannotated yeast deletion collection [22] or the MoBY-ORF collection [29] were used . For the barcoder collection , 1885 barcodes were recovered using a compiled list of all barcodes previously published ( 1624 barcodes from the barcode list of the deletion collection and 260 barcodes from the Yeast Barcoders collection; [28 , 32] ) . Multiple genes with the same barcodes were discarded . Strains with less than 20 counts across the different samples were discarded . The numbers of strains identified for the five collections in the three conditions are summarized in S9 Table . To avoid division by zero errors , each barcode count was increased by 10 before being normalized to the total number of reads for each sample . To quantify the relative fitness of each strain during growth in the various conditions , the analysis was restricted to the time during which the populations were in a steady-state phase , defined as generations 6 through 20 . Generation 0 was used as t0 . The linear regression of the log2 ratios of the normalized barcode counts at generations 6–20 to that at generation 0 was used to calculate the fitness of each strain . The two replicate measurements were then averaged . The source code is provided in the Supporting Information ( R script , S3 File ) . The correlation between each pair of replicates was displayed using the R package corrgram . The distribution of the averaged fitness was displayed using the R package beanplot [78] . To ensure that the pooled fitness measurements accurately reflected the fitness of each strain , the relative fitness of 51 strains from the deletion and plasmid collections that had deleterious , neutral , or beneficial changes was measured by pairwise competitions against a control strain marked with a fluorescent protein ( eGFP ) in the three conditions used in the pooled experiments . Fitness measurements of the individual clones were performed as previously described [40] using FY strains in which the HO locus was replaced with eGFP ( MATa: YMD1214 and MATa/MATα: YMD2196; S5 Fig , S7 Table ) . The fitness values were similar in both assays , and there was a strong positive correlation ( R2 = 0 . 83 ) between the fitness values from the large pool screen and the pairwise fitness assays ( S5 Fig and S7 Table ) . To limit artifacts due to preexisting mutations or copy-number changes in the genomes of the pooled strains , most of the barcoded pools were created either by fresh transformation ( in the case of the plasmid collections ) or from a fresh cross of the commercially available collection stocks with a wild-type strain ( see the Materials and Methods ) . To detect the extent of extraneous mutations in the validation panel , 51 strains were screened for the most common secondary mutation detected previously in the deletion collection: mutations in WHI2 , which is involved in the regulation of cell proliferation [79] . Mutations in WHI2 were screened in the 51 strains by PCR using oligo ( YOR043W-for and YPR043W-rev ) and Sanger sequencing ( S7 Table ) . Microarray analysis of the last sample of one of the competitions of the low-copy plasmid collection was used to verify that there were no copy-number changes , other than those due to the plasmids , at the population level; although that approach would only detect CNVs that achieved at least a ~10% frequency in the population . All sequencing data from this study have been submitted to the NCBI Sequence Read Archive ( SRA; http://www . ncbi . nlm . nih . gov/sra ) under accession number PRJNA248591 and BioProject accession PRJNA249086 . Microarray data from this article have been deposited in the Gene Expression Omnibus repository under accession GSE58497 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=sjgtsgwmdhajdud&acc=GSE58497 ) .
|
Experimental evolution allows us to observe evolution in real time . New advances in genome sequencing make it trivial to discover the mutations that have arisen in evolved cultures; however , linking those mutations to particular adaptive traits remains difficult . We evaluated the fitness impacts of thousands of single-gene losses and amplifications in yeast . We discovered that only a fraction of the hundreds of possible beneficial mutations were actually detected in evolution experiments performed previously . Our results provide evidence that 35% of the mutations identified in experimentally evolved populations are advantageous and that the distribution of beneficial fitness effects depends on the genetic background and the selective conditions . Furthermore , we show that it is possible to select for alternative mutations that improve fitness by blocking particularly high-fitness routes to adaptation .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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2016
|
High-Throughput Identification of Adaptive Mutations in Experimentally Evolved Yeast Populations
|
Transposable elements ( TEs ) have the potential to act as controlling elements to influence the expression of genes and are often subject to heterochromatic silencing . The current paradigm suggests that heterochromatic silencing can spread beyond the borders of TEs and influence the chromatin state of neighboring low-copy sequences . This would allow TEs to condition obligatory or facilitated epialleles and act as controlling elements . The maize genome contains numerous families of class I TEs ( retrotransposons ) that are present in moderate to high copy numbers , and many are found in regions near genes , which provides an opportunity to test whether the spreading of heterochromatin from retrotransposons is prevalent . We have investigated the extent of heterochromatin spreading into DNA flanking each family of retrotransposons by profiling DNA methylation and di-methylation of lysine 9 of histone 3 ( H3K9me2 ) in low-copy regions of the maize genome . The effects of different retrotransposon families on local chromatin are highly variable . Some retrotransposon families exhibit enrichment of heterochromatic marks within 800–1 , 200 base pairs of insertion sites , while other families exhibit very little evidence for the spreading of heterochromatic marks . The analysis of chromatin state in genotypes that lack specific insertions suggests that the heterochromatin in low-copy DNA flanking retrotransposons often results from the spreading of silencing marks rather than insertion-site preferences . Genes located near TEs that exhibit spreading of heterochromatin tend to be expressed at lower levels than other genes . Our findings suggest that a subset of retrotransposon families may act as controlling elements influencing neighboring sequences , while the majority of retrotransposons have little effect on flanking sequences .
A substantial fraction of most eukaryotic genomes is composed of transposable elements ( TEs ) [1]–[4] . While these TEs are sometimes referred to as “junk” DNA , there is evidence for potential functional roles in some instances [5] . Indeed , Barbara McClintock used the term “controlling elements” to describe the potential for these sequences to affect the regulation of endogenous genes [6]–[7] . Mobile genetic elements include class I retrotransposons and class II DNA transposons [2] . The class I TEs transpose via an RNA intermediate while class II TEs utilize a DNA intermediate for transposition . There are a variety of sub-families of both types of TEs [2] that differ in structure , activity , and integration patterns . TEs could influence neighboring genes by providing regulatory elements or promoters that would alter expression levels or patterns [8]–[9] . Alternatively , TEs may be targeted for silencing and this silencing could spread to affect neighboring sequences potentially including endogenous genes or regulatory elements [10]–[12] . There are several examples in which heterochromatic silencing of TEs can influence expression of nearby genes , including the agouti and Axin locus in mouse [13]–[15] , FLC [16] , FWA [17] and BNS [18] in Arabidopsis and sex-determination in melons [19] . While there are examples of heterochromatin spreading from retrotransposons to neighboring sequences , it is unclear how general this phenomenon is . Whole genome profiling of DNA methylation in Arabidopsis [20] found that the level of DNA methylation often had sharp boundaries at the edge of repeats although some inverted repeats did exhibit spreading . Another study [21] found limited ( 200–500 bp ) spreading of DNA methylation surrounding some TEs in Arabidopsis . There is evidence that highly methylated TEs are under-represented near genes in Arabidopsis and it has been suggested that the silencing of TEs located near genes might have deleterious consequences [21]–[23] . There is evidence for variation in the spreading of heterochromatin for different families of TEs in mouse [24] and evidence that differences in TE insertions contribute to gene expression variation in other rodents [25] . The complex organization of the maize genome , with interspersed genes and TEs [26]–[28] , provides an excellent system in which to study the effects of retrotransposons on neighboring DNA . Many model organisms have relatively small , compact genomes with relatively few retrotransposons . Since these genomes do not have a number of moderate-high copy retrotransposon families it can be difficult to assess the variation in spreading of heterochromatin to neighboring low-copy sequences . The maize genome is more representative of the organization of sequences observed within most flowering plants and is similar to the organization of many mammalian genomes as well . There are a large number of distinct families of retrotransposons within the maize genome and many of these families are moderate to high copy number [28]–[31] . In addition , haplotypes differ substantially with regard to the presence or absence of specific retrotransposon insertions [31]–[34] . The majority of repetitive sequences , including retrotransposons , in the maize genome are highly methylated [26] , [35]–[38] . The existence of heavily silenced retrotransposons interspersed with genes throughout the maize genome provides ample opportunities for TEs to exert epigenetic regulation on surrounding sequences . We were interested in further documenting the extent of heterochromatin spreading from maize retrotransposons to neighboring sequencings . Genomic profiling of DNA methylation and H3K9me2 found that heterochromatic spreading is only observed for a small number of specific retrotransposon families . These families tend to be enriched in pericentromeric regions of chromosomes . The analysis of haplotypes lacking specific retrotransposon insertions provides evidence that the adjacent heterochromatin is the result of spreading rather than insertion site bias .
DNA methylation and chromatin modifications were profiled for low-copy sequences in the maize genome using methylated DNA immunoprecipitation ( meDIP ) and chromatin-immunoprecipitation ( ChIP ) with antibodies specific for H3K9me2 or H3K27me3 , respectively . The fractions of the genome enriched for DNA or histone modifications were hybridized to a high-density microarray containing ∼2 . 1 million long oligonucleotide probes derived from the unmasked , non-repetitive fraction of the maize genome . The probes are spaced every 200 bp in the low-copy portions of the maize genome and can provide a profile for the chromatin state in these regions [39] . Our analyses focused on a subset of ∼1 . 4 million probes that are single-copy ( no other sequences with at least 90% identity within maize genome sequence ) . While this approach does not provide information on the chromatin state within repetitive sequences it can assess how retrotransposons impact neighboring sequences [39] . An independent whole-genome bisulphite sequencing dataset ( ∼7× coverage ) was used to further confirm the patterns that we observed in the meDIP-chip experiments . This independent approach was able to assess DNA methylation within retrotransposons as well as low-copy sequences . The enrichment for sequences associated with H3K9me2 was validated using a set of known sequences ( Figure S1A ) and several sequences identified by the profiling experiments ( Figure S1B ) . A large number of class I TEs ( retrotransposons ) have been identified within the maize genome [30] . These retrotransposons tend to be highly methylated in CG and CHG sequence contexts ( Figure S2 ) . We assessed whether heterochromatic chromatin modifications would be enriched in the single-copy regions that flank these retrotransposons . The chromatin state of sequences adjacent to any specific insertion of a retrotransposon is influenced by regulatory and insulator sequences as well as any potential effects of nearby retrotransposons . By assessing the average level of chromatin modifications near all of the retrotransposons of the same family it is possible to identify whether retrotransposon families vary in their influence on local chromatin state . Single-copy probes that are located within 4 kb of all retrotransposons were identified and used to assess the level of chromatin modifications in 200 bp bins of low-copy sequences adjacent to superfamilies , such as gypsy or copia ( Figure S3 ) and individual families of retrotransposons ( Figure 1 ) . Many of the retrotransposon families exhibit elevated levels of DNA methylation and H3K9me2 in the 200 bp immediately adjacent to their insertion sites ( Figure S3 ) . Because the meDIP-chip profiling of DNA methylation has a resolution of 300–500 bp it is likely that some of the apparent increase in DNA methylation levels very close to retrotransposons represents DNA methylation within the repeats themselves . A subset of the retrotransposon families also exhibit elevated levels of DNA methylation and H3K9me2 in regions more than 200 bp away from their insertion sites . In general , levels of H3K9me2 and DNA methylation were well correlated , but there were some families with different enrichment for these two marks . As expected , there was no evidence for enrichment ( or depletion ) of the facultative heterochromatin mark , H3K27me3 , in regions flanking the retrotransposons ( Figure 1C ) . To identify retrotransposon families associated with significant levels of spreading of heterochromatic chromatin modifications in adjacent low-copy sequences we compared the distribution of methylation levels in each 200 bp bin with a set of randomly permuted data ( 10 , 000 randomly assigned “insertions” ) and defined whether each 200 bp bin had significantly higher levels of a chromatin modification than random genomic sequences . Retrotransposon families that exhibit significant ( p<0 . 001 ) enrichment for a chromatin modification for each bin up through at least 800 bp were classified as spreading families . There are 39 retrotransposon families that exhibit significant enrichments of DNA methylation and H3K9me2 within each of the first four 200 bp bins adjacent to their insertion sites . These families will hereafter classified as “spreading ( both ) ” families ( Figure 1A–1B , 1E–1F and Figure S4 ) . Another 10 retrotransposon families had significant levels of H3K9me2 but did not have at least 800 bp of significant enrichment for DNA methylation . These families will hereafter be classified as “spreading ( H3K9 ) ” ( Table S1; Figure 1A–1B , 1G and Figure S5 ) . Many of these H3K9 only spreading families have elevated levels of DNA methylation in these same regions ( Figure S4 ) , but do not pass the significance threshold for all bins within the adjacent 800 base pairs . The remaining 95 retrotransposon families did not exhibit significant enrichment for either DNA methylation or H3K9me2 ( example in Figure 1H ) . There was no evidence for significant enrichment of H3K27me3 in regions near any retrotransposon families ( Figure 1C ) . The initial classification of retrotransposon families was based upon chromatin profiles from B73 seedling tissue . However , very similar patterns were observed for other genotypes and tissues . Specifically , the same families have significant enrichments of DNA methylation in Mo17 seedling , B73 endosperm and B73 embryo tissue ( Figure S6 ) . The H3K9me2 patterns are quite similar in both B73 and Mo17 seedlings ( Figure S7A–S7B ) and there was no evidence for enrichment for H3K27me3 in any of the tissues or genotypes assessed ( Figure S7C–S7E ) . The analysis of the whole-genome bisulphite sequencing data supports the classifications of different retrotransposon families ( Figure 1D and Figure S2 ) . Both CG and CHG DNA methylation levels are higher in low-copy regions flanking spreading ( both ) and spreading ( H3K9 ) families ( Figure 1D ) . The level of DNA methylation is higher in sequences flanking spreading ( both ) retrotransposon families than for sequences flanking spreading ( H3K9 ) retrotransposons . The sequences flanking the non-spreading families have DNA methylation levels that are similar to randomly selected genomic regions ( Figure 1D ) . The analysis of internal ( within the repeat itself ) DNA methylation levels ( Figure S2 ) reveals that the levels of CG methylation within retrotransposons with , or without spreading are similar . However , the spreading ( both ) and spreading ( H3K9 ) retrotransposon families have slightly elevated levels of CHG methylation at internal sequences . Interestingly , the non-spreading retrotransposon families tend to have higher levels of internal CHH methylation than do spreading families ( Figure S2 ) . The relative levels of H3K9me2 within retrotransposons was assessed by qPCR for 10 of the families , including six spreading ( both ) and four non-spreading families ( Figure S8 ) . There was no evidence for higher levels of H3K9me2 within the families that exhibit heterochromatic spreading than for those that do not ( Figure S8 ) . The elevated levels of DNA methylation and/or H3K9me2 in low copy sequences flanking the insertion sites observed for a subset of the retrotransposon families are largely confined to the region within 800–1 , 600 bp of the insertion site ( Figure 1A–1B ) . A closer examination of the levels of DNA methylation and H3K9me2 near each spreading family indicates a fairly sharp drop to non-significant levels of the modifications within 2 kb of the insertion site ( Figure 1E–1G; Figures S4 , S5 ) for spreading families . The visualization of individual spreading families ( Figures S4 , S5 ) reveals that the distance of heterochromatin spreading varies for different retrotransposon families . This analysis provides clear evidence for diversity in the prevalence of heterochromatin found in low-copy regions flanking different families of retrotransposons in the maize genome . The mechanistic basis for the spreading of heterochromatin is not well defined . It is possible that the interplay between DNA methylation and histone modifications [40]–[41] would result in spreading of chromatin modifications beyond the specific target . To probe the mechanistic basis of spreading we profiled DNA methylation levels in several maize mutants that are known , or expected , to affect DNA methylation patterns . In plants , one pathway that impacts DNA methylation is RNA-directed , and requires the activity of multiple RNA polymerases ( RNA PolIV and PolV ) , an RNA dependent RNA polymerase ( RDR2 ) , a dicer like protein , and multiple chromatin modifiers [42] . The mop1 mutant of the maize Rdr2 gene [43]–[45] exhibits variable expression of specific retrotransposon families in mutant relative to wild-type tissue [46] . However , we found no evidence for a consistent effect of the mop1 mutation on the expression levels of spreading or non-spreading retrotransposon families . Indeed , spreading retrotransposon families include examples of both up- and down-regulation in mop1 mutant individuals relative to wild-type ( Table S1 ) . In addition , there were examples of non-spreading retrotransposon families that do , and do not , exhibit altered expression in mop1 plants . The levels of DNA methylation in low-copy sequences neighboring retrotransposon families was analyzed in the mop1 mutant to assess whether the spreading of heterochromatin might be affected ( Figure 2 ) . There was no evidence for a reduction in the distance or magnitude of the spreading of DNA methylation in the mop1 mutants relative to wild-type plants . The small RNA profile of spreading and non-spreading retrotransposon families was assessed using a recently published small RNA profile based on B73 shoot tissue [47] . The average count of small RNAs per retrotransposon and coverage of retrotransposon did not vary between spreading ( both ) , spreading ( H3K9 ) or non-spreading retrotransposon families ( Figure S9 ) . Spreading retrotransposons exhibit higher levels of CHG methylation within the retrotransposon themselves ( Figure S2 ) . Spreading levels were assessed in plants that were homozygous for mutations in the maize chromomethylase zmet2 ( GRMZM2G025592 ) gene , which contributes substantially to CHG methylation [48]–[49] . While there were examples of locus-specific alterations in DNA methylation levels in this mutant , there was no evidence for a reduction in the spreading of DNA methylation in low copy sequences flanking spreading retrotransposon families ( Figure 2 ) . The observation that certain families of retrotransposons have high levels of heterochromatic modifications in adjacent regions could reflect insertion site biases for these families or indicate that these families cause local spreading of heterochromatin . Examples of “empty” sites in the Mo17 haplotypes were identified and used to assess whether the high levels of DNA methylation would be observed in these regions when the retrotransposon was absent . Mo17 whole-genome shotgun WGS ) sequences ( generated by the DOE's Joint Genome Institute ( JGI ) and downloaded from ftp://ftp . jgipsf . org/pub/JGI_data/Zea_mays_Mo17/ ) were aligned to the B73 reference genome sequence . Empty sites were defined as being those as which at least three Mo17 sequence reads cover a low-copy sequence flanking an insertion but do not align to the retrotransposon itself and for which no Mo17 reads cover the junction between the low-copy sequence and the retrotransposon . In total , 668 empty sites were identified for the spreading ( both ) retrotransposon families and 29 empty sites for the spreading ( H3K9 ) retrotransposon families for which we had DNA methylation data in the unique regions flanking the insertion . The lack of the specific insertion in Mo17 was confirmed at 13 of the 14 empty sites that were tested using site-specific PCR primers to confirm the presence/absence of specific insertions . This suggests that there is a low false-positive rate in the identification of empty sites in Mo17 . However , given the low coverage of the WGS data and challenges associated with aligning polymorphic sequences it is likely that many of the true empty sites were not identified in this analysis . The level of DNA methylation at the probe nearest to the empty site was used to assess relative DNA methylation levels with ( B73 ) and without ( Mo17 ) each insertion ( Figure 3 ) . The low-copy DNA flanking many of the empty sites showed differences in DNA methylation levels between B73 and Mo17 in 34 . 7% of the empty sites flanking spreading ( both ) retrotransposons and in 43 . 5% of the empty sites flanking spreading ( H3K9 ) retrotransposons ( Figure 3A ) . Over 95% of the empty sites with differential methylation had higher DNA methylation levels in B73 ( the genotype with the insertion ) than in Mo17 . While 35–43% of the probes flanking the empty sites for spreading retrotransposons had variable DNA methylation in B73 and Mo17 , only 3% of genome-wide probes assayed show significantly different levels of DNA methylation in B73 and Mo17 and these differences include equal frequencies of higher methylation levels in each genotype . This suggests that the insertion of the retrotransposon conditioned higher levels of DNA methylation and was responsible for the observed DNA methylation polymorphisms . In contrast , DNA methylation levels were similar ( and frequently quite high ) between B73 and Mo17 when the retrotransposon insertion was present in both genotypes ( Figure 3A ) . Closer inspection of several of the empty sites provides evidence for enrichment of DNA methylation or H3K9me2 in regions flanking the sites in B73 but these modifications were not observed in the Mo17 haplotype that lacks the retrotransposon ( Figure 3B ) . The presence of the insertion as well as the enrichment for DNA methylation was also assessed in five other inbred genotypes of maize ( Figure 3B ) . The presence of insertions was strongly correlated with the presence of high levels of DNA methylation in these other genotypes as well . These results suggest that the high level of heterochromatin observed around these spreading retrotransposon families is an outcome of TE insertion rather than insertion site bias . The finding that only a subset of maize class I retrotransposon families are associated with local spreading of heterochromatin suggested that there might be intrinsic differences among different retrotransposon families that would explain this variation . We proceeded to characterize these families to ascertain whether there were specific common attributes of spreading families . None of the LINE families exhibit evidence for spreading of heterochromatic marks . RLG ( gypsy ) families are over-represented among spreading ( both ) retrotransposon families , while the spreading ( H3K9 ) retrotransposons have more RLC ( copia ) families than expected ( Figure 4A ) . Spreading ( both or H3K9 ) retrotransposons exhibit significantly higher copy number and comprise a greater fraction of the genome ( Table S1 , attributes from [29] ) than do non-spreading retrotransposon families ( Figure S10A–S10B ) . While there are significant differences in copy number and total Mb within the genome there are examples of families with spreading that have lower copy numbers ( Figure S10A ) . In addition , spreading ( both ) retrotransposon families have significantly higher average fragment lengths than do non-spreading families ( Table S1 ) . Spreading families do not have a significant difference in their mean insertion date relative to non-spreading families ( Table S1 ) . However , the analysis of average insertion date for each family ( Figure S10C ) shows that while non-spreading retrotransposon families include both old and young families the spreading ( both ) retrotransposon families only include younger families . The analysis of several characteristics of the retrotransposon families with and without spreading provides evidence for some significant differences but none of these factors are sufficient for predicting whether or not spreading occurs . Previous studies that had assessed expression of some retrotransposons in maize tissues [50]–[51] did not find unusually high or low abundance for transcripts of the families with heterochromatin spreading relative to other families . The relative abundance of spreading ( both ) retrotransposons is higher in the middle of the chromosome than the other families suggesting that these retrotransposons may be enriched in pericentromeric regions ( Figure 4B ) . However , it should be noted that there are other retrotransposon families also preferentially located in pericentromeric regions [29] but that do not show spreading of heterochromatin to low-copy adjacent regions . Hence , the pericentromeric enrichment is insufficient for heterochromatin spreading . The observation that the spreading ( both ) retrotransposon families are enriched in pericentromeric regions suggested the possibility that the higher levels of DNA methylation in flanking sequences may be due to sampling bias . Because pericentromeric regions tend to have higher levels of DNA methylation [39] it is possible that higher sampling of these regions led to the observation of spreading . However , an analysis of the levels of DNA methylation in low-copy flanking regions relative to chromosome position provides evidence that low-copy sequences flanking spreading ( both ) retrotransposons is substantially higher than the corresponding regions flanking non-spreading families throughout the chromosome in both CG and CHG contexts ( Figure S11 ) . The levels of CG and CHG DNA methylation in spreading ( H3K9 ) retrotransposon families are intermediate ( Figure S11 ) . The finding that some retrotransposon families exhibit spreading of heterochromatic marks to surrounding sequences while others do not led us to hypothesize that these families may influence expression of nearby genes . RNAseq was used to estimate transcript abundance in three tissues of B73 and Mo17 including the identical leaf tissue samples used for profiling DNA methylation levels . All maize genes were annotated to identify the first retrotransposon 5′ of the transcription start site and to determine the distance between the retrotransposon and the transcription start site . Genes that are located near retrotransposons that exhibit spreading ( both or H3K9 ) have significantly ( p<0 . 001 ) lower expression levels in all genotypes and tissue examined ( Figure 5; Figure S12A ) . This reduction in expression is most severe when we examine genes with retrotransposons inserted within 500 bp of the transcription start site . As the distance between the insertion site and the transcription start site increases there is less evidence for an effect on expression levels , suggesting a limited range within which retrotransposons can influence gene expression . The genes located near spreading ( both ) and spreading ( H3K9 ) retrotransposons frequently have no detectable expression ( Figure S12B ) . However , even if we exclude genes with no expression , the mean expression of genes near spreading retrotransposons is lower ( p<0 . 001 ) ( Figure S12C ) .
Epigenetic variation in low-copy sequences can be the result of pure epigenetic changes ( no correlation with DNA sequence polymorphisms ) or occur in a facilitated or obligatory fashion such that DNA sequence differences contribute to the epigenetic changes [10] . A handful of examples in which epigenetic differences that impact phenotype has been shown to involve TEs inserted near genes [13]–[19] , [52] and genomic profiling of DNA methylation in Arabidopsis has revealed some examples of heterochromatin spreading from TEs [20] , [21] . However , it has not been clear whether all TEs have similar effects on neighboring chromatin or whether there are family-specific attributes that affect the spreading of heterochromatin . A recent study analyzed several families of retrotransposons in mouse and found that there is variation in the level of heterochromatin spreading [24] and there have been suggestions of variation in the effects of different repetitive elements on nearby gene expression in Arabidopsis [22] , [23] . The complex organization of the maize genome with interspersed TEs and genes provides the opportunity to examine differences among class I retrotransposon families . The chromatin state of any low-copy region of a genome is likely influenced by nearby sequences including regulatory elements and insulator elements . In addition , it is quite likely that TEs will exert an influence on the chromatin state . By examining the average level of chromatin modifications in low-copy sequences neighboring families of retrotransposons we found evidence for heterochromatic spreading from a subset of the moderate to high-copy retrotransposon families in maize . Even in these families the heterochromatic marks spread only 600–1 , 000 base pairs from the retrotransposon . It is worth noting that there may be other mechanisms through which retrotransposons influence flanking regions . Our assessment is based upon only two chromatin marks , H3K9me2 and DNA methylation . These marks are frequently associated with heterochromatin , but there may be other specific types of chromatin marks that spread from these and transposon families . There is also evidence that differences in interspecific variation in transposon insertions contributes to gene expression diversity between related species [22] , [23] . Here we provide evidence that transposon insertions can also contribute to differences in DNA methylation patterns and gene expression levels within a species . Many TE insertions are exhibit presence/absence variation among maize haplotypes [31]–[34] . The retrotransposons that cause spreading of heterochromatin are expected to result in obligatory epigenetic variation in the low-copy sequences that flank insertions . Indeed , we found that the levels of DNA methylation and H3K9me2 were quite different in B73 and Mo17 at regions that exhibit presence/absence variation for an insertion of a retrotransposon from one of the spreading families . Specifically , these retrotransposons with spreading of heterochromatin may contribute to obligatory and facilitated epialleles , as defined by Richards [10] , among different genotypes . Genomic resequencing is often used to identify SNPs as a means to explain phenotypic variation . However , it might be important to also use resequencing data to identify retrotransposon insertion polymorphisms , especially for the retrotransposon families that exhibit spreading of heterochromatic marks . The polymorphism for these insertions may lead to functional variation in the expression of nearby genes . Barbara McClintock proposed the concept that transposons could serve as “controlling” elements that would influence nearby genes [6]–[7] and this could be extended to include the potential for retrotransposons to influence nearby genes as well . There are examples in which transposons contain regulatory elements or cryptic promoters that can influence the expression of nearby genes [9] , [53] . There is also evidence that some transposons can act as controlling elements by “seeding” heterochromatin that spreads to adjacent low copy sequences [10]–[12] . Here we have shown that this activity is not a generic feature of all retrotransposons but is instead limited to a subset of retrotransposons . Hollister and Gaut [22] provide evidence that the presence of heavily silenced TEs near genes may lead to reduced expression and result in fitness consequences . This would suggest that many TEs would evolve to have minimal effects on neighboring genes to reduce their fitness costs . There is evidence that some Drosophila retrotransposons contain insulator elements that reduce the spreading of chromatin states [54] . Alternatively , studies at the bns locus in Arabidopsis have suggested the presence of an active mechanism to prevent the spreading of heterochromatin from retrotransposons [55] . It might be expected that different families of TEs would vary in their ability to limit potential spreading of heterochromatin through the presence of insulators or the recruitment of factors that limit spreading . Hollister and Gaut [22] noted heterogeneity among families of Arabidopsis class I retrotransposons for their distance to the nearest gene and suggested that this may reflect family specific differences in heterochromatin spreading . The analysis of the large families of retrotransposons in maize permitted us to identify several families of retrotransposons with high levels of spreading . These retrotransposon families may be considered as bad “neighbors” for genes . Indeed we find that many genes located near retrotransposons with spreading tend to be silenced or expressed at lower levels . We might predict that insertions of retrotransposons from these families will be more strongly selected against when inserted near genes , especially if they affect gene expression . Therefore , our observed expression differences will only report effects that have been tolerated during natural and artificial selection of maize lines . Consistent with this possibility , our observation that these retrotransposon families are enriched in relatively gene-poor pericentromeric regions may reflect selection against insertions of these retrotransposons when they are near genes . Further research efforts to understand the basis of this difference will be important in providing the ability to predict which retrotransposon families are likely to condition spreading of heterochromatin and understanding the consequences of the spreading of heterochromatin .
DNA methylation profiling on three replicates of 3rd leaf tissue of B73 and Mo17 was performed as described [39 – GSE29099] . Briefly , methylated DNA was immunoprecipitated with an anti-5-methylcytosine monoclonal antibody from 400 ng sonicated DNA using the Methylated DNA IP Kit ( Zymo Research , Orange , CA; Cat # D5101 ) . For each replication and genotype , whole genome amplification was conducted on 50–100 ng IP DNA and also 50–100 ng of sonicated DNA ( input control ) using the Whole Genome Amplification kit ( Sigma Aldrich , St . Louis , MO , Cat # WGA2-50RXN ) . For each amplified IP input sample , 3 ug amplified DNA were labeled using the Dual-Color Labeling Kit ( Roche NimbleGen , Cat # 05223547001 ) according to the array manufacturer's protocol ( Roche NimbleGen Methylation UserGuide v7 . 0 ) . Each IP sample was labeled with Cy5 and each input/control sonicated DNA was labeled with Cy3 . H3K9me2 and H3K27me3 profiling were performed on three replicates of B73 and Mo17 seedlings using antibodies specific for H3K27me3 ( #07-449 ) and H3K9me2 ( #07-441 ) purchased from Millipore ( Billerica , USA ) . For each replicate , 1 g of plant material was harvested on ice , rinsed with water , and crosslinked with 1% formaldehyde for 10 minutes under vacuum . Cross-linking was quenched by adding glycine solution to a final concentration of 0 . 125 M under vacuum infiltration for 5 minutes . Treated tissue was frozen in liquid nitrogen and stored at −800 C until chromatin extraction . Chromatin extractions were performed using EpiQuik Plant ChIP Kit ( Epigentek , Brooklyn , USA ) according to manufacturer's recommendations . Extracted chromatin was sheared in 600 µl of the EpiQuik buffer CP3F with 5 10-second pulses on a sonicator . To test and optimize sonication conditions , cross-linking was reversed in a sample of sheared chromatin and the resulting products were analyzed on agarose gel . Sonication conditions were optimized to yield predominantly 200–500 bp DNA samples . Chromatin immunoprecipitations , reverse cross-linking , and DNA cleanup was performed using EpiQuik Plant ChIP Kit ( Epigentek ) according to manufacturer's recommendations . For each genotype , antibody , and replicate , 50–100 ng of input and immunoprecipitated ( IP ) DNA was amplified with a whole genome amplification kit ( WGA2 , Sigma , St . Louis , USA ) . The amplification of no antibody control ( negative control ) was always 5–10 fold less efficient confirming specificity of immunoprecipitation . For each amplified IP and input sample , 3 ug of amplified DNA were labeled using the Dual-Color Labeling Kit ( Roche NimbleGen , Cat # 05223547001 ) according to the array manufacturer's protocol ( Roche NimbleGen Methylation User- Guide v7 . 0 ) . Each IP sample was labeled with Cy5 and each input/control sonicated DNA was labeled with Cy3 . Samples were hybridized to the custom 2 . 1 M probe array ( GEO Platform GPL13499 ) for 16–20 hrs at 42 C . Slides were washed and scanned according to NimbleGen's protocol . Images were aligned and quantified using NimbleScan software ( Roche NimbleGen ) producing raw data reports for each probe on the array . The histone modification and methylation mutants array data can be obtained from GEO accession ( GSE39460 ) . The resulting microarray data were imported into the Bioconductor statistical environment ( http://bioconductor . org/ ) . Microarray data channels were assigned the following factors: B73 immunoprecipitation , Mo17 immunoprecipitation , B73 input , or Mo17 input depending on sample derivation . Non-maize probes and vendor-supplied process control probes were configured to have analytical weights of zero . Variance-stabilizing normalization was used to account for array-specific effects . Factor-specific hybridization coefficients were estimated by fitting fixed linear model accounting for dye and sample effects to the data using the limma package [56] . The probes were each annotated with respect to their location relative to repeats from the ZmB73_5a_MTEC_repeats file available from www . maizesequence . org . Each probe was only associated with the closest repeat and all probes located within 5 kb of a repeat were retained for further analyses . The probes were assigned based on distance to the retrotransposon and include both upstream ( 5′ ) and downstream ( 3′ ) sequences together . The distribution of retrotransposons along the length of the chromosome was performed as described in [57] . Data formatted for the Integrative Genomics Viewer ( IGV ) can be downloaded from http://genomics . tacc . utexas . edu/data/rte_methylation_spreading/ . DNA was extracted from the outer tissues of B73 ears whose silks had emerged but had not been fertilized . Sodium bisulfite-treated Illumina sequencing libraries were prepared using a method similar to that of Lister et al [58] . Alignment to the genome ( AGPv2 ) and identification of methylated cytosines was performed using BS Seeker [59] . A total of 198 , 333 , 982 single-end reads with unique alignments specifically on the ten chromosomes were obtained , with an average genome-matching read length of 72 . 8 bases ( 7 . 0× coverage , SRA accession SRA050144 . 1 ) . The level of methylation in CG , CHG and CHH contexts and the total proportion of DNA methylation was calculated for non-repeat masked sequences ( as annotated within ZmB73_5a_MTEC_repeats ) located within 1 kb of each retrotransposon family . Percent methylation is defined as the number of methylated Cs per total number of Cs for a region . BEDTools [60] was used to identify low-copy sequences flanking retrotransposons . Approximately 63M Mo17 454 whole-genome shotgun sequencing reads generated by the DOE's Joint Genome Institute ( JGI ) were trimmed and aligned to Maize B73 reference genome ( AGPv2 ) and reads aligned uniquely ( single loci ) were filtered for subsequent analysis . A retrotransposon insertion site was classified as empty if we identified at least 3 WGS reads supporting the site that aligned to the insertion site that included>50 bp of aligned sequence outside of the repeat region in B73 with similarity of ≥94% , relatively short unaligned tails ( ≤20 bp ) , and contained a long overhang of >20 bp that begins ±3 bp from the annotated retrotransposon insertion site . PCR primers were designed to amplify the sequence at the “empty” sites using the B73 sequence ( which contained the insertion ) and the Mo17 sequence ( which lacks the insertion ) ( Table S2 ) . These same primers were also used to assess the presence or absence of the insertion in several other maize genotypes including CML228 , CML277 , Hp301 , Tx303 and Oh7b . Seeds for these genotypes were obtained from the USDA North Central Regional Plant Introduction Station . PCR and gel electrophoresis was conducted as described [61] . RNA–seq was performed on three biological replicates of four tissues ( 3rd leaf , embryo , endosperm , and immature ear ) for both B73 and Mo17 . Samples were prepared at the University of Minnesota BioMedical Genomics Center in accordance with the TruSeq library creation protocol ( Illumina ) . Samples were sequenced on the HiSeq 2000 developing 6–17 million reads per replicate . Raw reads were filtered to eliminate poor quality reads using CASAVA ( Illumina ) . Transcript abundance was calculated by mapping reads to the maize reference genome ( AGPv2 ) using TopHat under standard parameters [62] . Counts of mapped reads across the exon space of the maize genome reference working gene set ( ZmB73_5a ) were developed using ‘BAM to Counts’ within the iPlant Discovery Environment ( www . iplantcollaborative . org ) . RPKM values were calculated per gene . All genes within 500 , 1000 , 2500 , and 5000 bases of the closest upstream annotated transposable element ( ZmB73_5a ) using BEDtools [60] were grouped by the spreading class of the nearest TE: spreading ( 5mc/H3K9 ) , spreading ( H3K9 only ) , non-spreading , and no TE within distance . Genes were also classified as expressed for any RPKM value >0 . The proportions of genes showing expression for each distance and spreading class combination were calculated . Average RPKM values for each distance and spreading class combination were also calculated . Significance testing was performed non-parametrically through Wilcox rank-sum tests . Sequencing data is available from the NCBI short read archive under studies SRP013432 and SRP009313 .
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Transposable elements comprise a substantial portion of many eukaryotic genomes . These mobile fragments of DNA can directly mutate genes through insertions into coding regions but may also affect the gene regulation through nearby insertions . There is evidence that the majority of transposable elements are epigenetically silenced , and in some cases this silencing may spread to neighboring sequences . This spreading of heterochromatin could create a significant fitness tradeoff between transposon silencing and gene expression . The maize genome has a complex organization with many genes flanked by retrotransposons , providing an opportunity to study the interaction of retrotransposons and genes . To survey the prevalence of heterochromatin spreading associated with different retrotransposon families , we profiled the spread of heterochromatin into nearby low copy sequences for 150 high copy retrotransposon families . While many retrotransposons exhibit little to no spreading of heterochromatin , there are some retrotransposon families that do exhibit spreading . Genes located near retrotransposons that spread heterochromatin have lower expression levels . The families of retrotransposons that spread heterochromatin marks to nearby low-copy sequences may have increased fitness costs for the host genome due to their suppression of genes located near insertions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"epigenetics",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
Spreading of Heterochromatin Is Limited to Specific Families of Maize Retrotransposons
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One of the striking findings of comparative developmental genetics was that expression patterns of core transcription factors are extraordinarily conserved in bilaterians . However , it remains unclear whether cis-regulatory elements of their target genes also exhibit common signatures associated with conserved embryonic fields . To address this question , we focused on genes that are active in the anterior neuroectoderm and non-neural ectoderm of the ascidian Ciona intestinalis . Following the dissection of a prototypic anterior placodal enhancer , we searched all genomic conserved non-coding elements for duplicated motifs around genes showing anterior neuroectodermal expression . Strikingly , we identified an over-represented pentamer motif corresponding to the binding site of the homeodomain protein OTX , which plays a pivotal role in the anterior development of all bilaterian species . Using an in vivo reporter gene assay , we observed that 10 of 23 candidate cis-regulatory elements containing duplicated OTX motifs are active in the anterior neuroectoderm , thus showing that this cis-regulatory signature is predictive of neuroectodermal enhancers . These results show that a common cis-regulatory signature corresponding to K50-Paired homeodomain transcription factors is found in non-coding sequences flanking anterior neuroectodermal genes in chordate embryos . Thus , field-specific selector genes impose architectural constraints in the form of combinations of short tags on their target enhancers . This could account for the strong evolutionary conservation of the regulatory elements controlling field-specific selector genes responsible for body plan formation .
The concept of “selector genes” was introduced 30 years ago by Garcia-Bellido to define genes that interpret a transient regulatory state and specify the identity of a given developmental field [1] . The question of how embryos execute distinct and unique differentiation programs using these selector genes can be tackled by focusing on how gene expression is encoded in cis-regulatory elements and their field-specific trans-acting factors ( TF ) . This concept was more recently extended to terminal selector genes that coordinate the expression of differentiation genes to determine a given cell type [2] . In vertebrates , examples include the Crx TF that interacts with another TF to control the expression of target genes in rod photoreceptors [3]–[5] . In vertebrates as well as in flies , Crx and its Drosophila homolog Otd act through a small cis-regulatory motif overrepresented in the elements flanking the target genes [6]–[10] . In addition to this evolutionary conserved network , many others in Caenorhabditis elegans and Drosophila melanogaster have shown that cell specific enhancers contain a common “tag” corresponding to a specific cis-regulatory motif , and that this motif is linked to one or a few terminal selector genes [11] , [12] . In contrast , during early development , very few studies have reported how a set of region-specific cis-regulatory elements responds to field-specific selector genes . In insects , one of the best characterized sets of functionally related cis-regulatory elements responds to the gradient of nuclearized dorsal TF in the early Drosophila embryo [13] , [14] . However , the regulatory mechanism of dorsal-ventral patterning is not enough conserved in chordates to allow comparative studies of the regulatory network . A more general character of bilaterians is the tripartite organization of the nervous system along the antero-posterior axis [15] . In the posterior part ( hindbrain and nerve cord ) , Hox genes are expressed in a colinear order . In the domain anterior to the Hox genes , several striking similarities in the relative expression patterns of other transcription factors have been noted in bilaterians [16]–[18] . The OTX-like homeobox transcription factors ( otd in insects ) are expressed in the anteriormost part of animals as diverse as cnidarians , insects , annelids , urochordates and vertebrates [19]–[21] . In chordates , OTX has a sustained expression in the anterior neuroectoderm and in derivatives of anterior ectoderm such as placodes , stomodeum [20] , [22] . In mice , null-mutants of this gene lack various head structures [23] . These results suggest that OTX-like proteins belong to a conserved developmental control system operating in the anterior parts of the brain , different from the one encoded by the Hox complexes [24] . Many homeodomain proteins bind to the core DNA sequence ATTA , but several subfamilies have longer binding specificities around this core [25] , [26] . OTX homeodomain proteins contain a lysine at position 50 which confers them additional specificity to guanines 5′ of the ATTA motif , resulting in a core recognition sequence of GATTA/TAATC [27] . The DNA binding domains of homeobox gene families are highly similar over large evolutionary distances and cross-species experiments have demonstrated that the OTX proteins can be exchanged between flies , mice and human without major developmental defects [28] , [29] , and more recently between ascidians and mice [24] , [30] . For studies of anterior nervous system development , the ascidian Ciona intestinalis offers the advantage of a simple chordate body plan with the canonical tripartite brain along the antero-posterior axis [31] . In addition , the genome is small , with short intergenic regions which can be aligned with another ascidian species , thus simplifying the identification of cis-regulatory elements [32] . Moreover , complete expression patterns have been determined for thousands of genes and are readily available in public databases [33]–[35] . Therefore , Ciona intestinalis constitutes an ideal model system for combining whole genome bioinformatics and experimental cis-regulatory analyses . Here , we first focus on one single anterior ectodermal enhancer in Ciona intestinalis . Its detailed analysis points to an internal tandem-like structure and underscores the key role of the selector gene Otx . We then examine if other duplicated putative binding sites for OTX preferentially flank anteriorly expressed genes in the genome .
We have previously described an enhancer sequence ( called “D1” , 323bp ) that controls expression of the Ciona intestinalis Pitx gene in a sub-region overlapping the neural and the non neural ectoderm called the anterior neural boundary ( ANB ) [36] . For the sake of simplicity , and although ANB has a dual origin , we label it as a derivative of the neuroectoderm and call the region composed of anterior epidermis , ventro-anterior sensory vesicle and ANB , the “anterior neuroectoderm” . For this study , we used a minimal 206 bp fragment of D1 that is sufficient to drive reporter gene expression in the ANB and divided it into five parts ( D1a-e , Figure 1A and Figure 2A ) for further analysis . Deletion of the first 16pb ( D1a , Figure 2A ) resulted in the D1bcde fragment ( Figure 1A ) and led to ectopic reporter gene expression in the anterior epidermis ( ae ) and ventro-anterior sensory vesicle ( vasv ) in addition to the expected expression in the ANB . All these elements indicate that D1 responds to neuroectodermal trans-activating factors that are not restricted to the ANB and that D1a contains motifs bound by a repressor factor that restricts D1 expression to the sole ANB . We tested whether D1bcde controls the onset of Ci-pitx expression in the ANB . Endogenous Ci-Pitx-gene expression was not detected in ANB cells before the initial tailbud stage [37] , [38] , suggesting that it starts at this stage . To test whether D1bcde recapitulates the temporal pattern of Ci-Pitx expression , we assayed reporter gene expression by either X-gal staining or lacZ in situ hybridization on the same batch of electroporated embryos fixed at successive stages . The rationale is to take advantage of the delay in β-galactosidase protein synthesis ( e . g . [39] ) , which should produce a marked difference between X-gal and in situ staining shortly after the onset of reporter gene expression . We could detect neither lacZ RNAs nor β-galactosidase activity before the initial tailbud stage . At this stage , however , lacZ transcripts could be detected in 55 . 4% ( n = 46 of N = 71 ) of the embryos while only 7% ( n = 5 of N = 83 ) showed positive ANB cells after X-gal staining ( Table S1 ) . Hence , D1bcde-driven transcription starts at the same time as the endogenous pitx gene , which indicates that the D1bcde enhancer element triggers the initiation of Ci-pitx expression in ANB cells . Conservation between Ciona intestinalis and savignyi genomic sequences is not uniformly distributed throughout conserved non coding elements ( CNEs ) but rather concentrated in short blocks of identical nucleotides , which point to candidate transcription factor binding sites ( TF-BS; Figure 1A , Figure 2A ) . We identified four classes of putative TF-BS based on nucleotide composition and by querying binding site databases [40] , [41] . One of them matches the OTX/K-50 paired homeodomain consensus sequence ( sites O1 and O2 , Figure 1A and Figure 2A ) . Other sites , called T ( T/A-rich ) , G ( G/C-rich ) and M , bear resemblance to Forkhead , Smad and Meis family factors , respectively ( Figure 1A ) . Some of them ( P , T1 , T2 were not completely conserved in the genome alignment . But each class of these candidate binding sites was represented at least twice in the minimal D1bcde element . The function of these candidate TF-BS was tested by introducing point mutations in the corresponding blocks of conserved sequences , followed by reporter gene expression assays . With the exception of mutations disrupting the “M” sites , each one of the individual modifications of O , T and G sequences reduced reporter gene expression in the anterior neuroectoderm derivatives ( Figure 1B ) . Taken together , these observations indicate that D1 enhancer activity requires at least two copies of each one of three distinct classes of conserved putative TF-BS ( Figure 1 ) . The aforementioned observation that the essential putative binding sites occur several times in the enhancer led us to investigate whether the structure of D1 bears functional significance to its enhancer activity . Notably , the 54-bp D1 ( ab ) element ( Figure 2A ) contains the three previously mentioned conserved motifs O , T and G in addition to a putative Pax binding site ( P ) , but D1 ( ab ) is not sufficient to enhance reporter gene transcription ( Figure 2C ) . Since each of the critical sites is represented at least twice in the full length enhancer , we asked whether D1 enhancer activity relies on this tandem-like repetition of essential binding sites . We created artificial enhancers containing multiple copies of D1 ( ab ) and found that as little as two copies of D1 ( ab ) were sufficient to drive strong lacZ expression in the anterior neuroectoderm ( 88% of 167 tailbud embryos ( Figure 2D and 2E ) ) . To test whether enhancer activity of the D1 ( ab ) dimer relies specifically on the duplication of O , T and G sites , we introduced point mutations in the second D1 ( ab ) copy . Each of these mutations strongly reduced enhancer activity ( Figure 2D ) . These observations are reminiscent of the requirement for multiple copies of bicoid binding sites for target gene activation during Drosophila head development [42] and the general tendency of binding sites to occur in clusters [43] . Our results demonstrate that duplications of critical binding sites are essential for D1 enhancer activity and do not constitute mere redundancy . We next asked whether the distance between the duplicated 54bp elements influenced the activity of the artificial D1 ( ab ) dimer . To this aim , we designed sequences that are not predicted to bind any characterized transcription factors from the Uniprobe database ( see Materials and Methods ) and inserted 25 , 50 , 75 and 150bp spacers between the D1 ( ab ) duplicates . Overall , enhancer activity of these constructs is reduced compared to the original D1 ( ab ) dimer and almost completely abolished with the 75bp and 150bp spacers ( Figure 2F ) . Similar structural constraints were reported in the Drosophila knirps enhancer , which was shown to require a specific arrangement of duplicated bicoid binding sites for activation [44] , [45] . Similarly , even-skipped enhancers contain a conserved structure of paired binding sites [46] and duplicated and relatively distant ( 30–200bp ) TFBS are necessary for a correct activity of the SV40 enhancer [47] and the lac operon [48] . Taken together , our observations demonstrate that D1 enhancer activity relies on the clustering of duplicate short conserved sequences . Among D1 ( ab ) essential putative binding sites , the GATTA/TAATC “O” sequences correspond to the consensus for K50-Paired homeodomain proteins . In ascidians , this family includes Goosecoid , Pitx and Otx . Only Otx , is expressed in the right time and place to account for D1 enhancer activation in the anterior neuroectoderm in Ciona [20] and there is only one Otx gene in the Ciona intestinalis genome . A functional study using morpholino antisense oligonucleotides in Halocynthia roretzi - another ascidian species - showed that the Hr-Otx knockdown strongly perturbs anterior neuroectoderm development , mostly because it is required for early specification events in the gastrula [49] . To avoid this early effect , we used targeted expression of dominant-negative and hyper-active versions of the Ci-OTX protein to interfere with its endogenous activity specifically after gastrulation . We thus engineered protein chimeras between the Ci-OTX homeodomain and the Drosophila engrailed repressor peptide or the VP16 trans-activation domain to create dominant-negative ( OTX:EnR ) or hyper-active ( OTX:VP16 ) forms , respectively . We then used the Ci-Six3 cis-regulatory DNA to drive expression of these fusion proteins in a region that encompasses the ANB ( Figure S1 ) . These constructs were co-electroporated with the Ci-Distal-Pitx reporter plasmid , which contains the D1 enhancer with the two essential O1 and O2 K50-Paired binding sites [36] , and the number of anterior neuroectodermal cells expressing the reporter gene was scored at the mid-tailbud stage ( Figure 3 ) . In control embryos expressing a Ci-Six3:Venus construct , an average of 2 . 78 anterior neuroectodermal cells per embryo activated the Ci-Pitx reporter construct , which can be accounted for by the mosaic incorporation of the transgene in the four ANB cells ( Figure 3A and 3C ) . In contrast , targeted expression of Ci-OTX fusion proteins significantly altered Ci-Pitx reporter gene expression in the anterior neuroectoderm: the engrailed fusion inhibited ANB expression , while OTX:VP16 produced ectopic activation in surrounding neuroectodermal cells ( Figure 3B–3D ) . Notably , OTX:VP16 also boosts expression of the ab dimer construct , and is not sufficient to induce overexpression when coelectroporated with one dimer construct bearing one O mutation ( data not shown ) . This indicates that OTX:VP16 indeed binds to the GATTA binding sites . These observations strongly suggest that Ci-OTX trans-activating inputs are required for D1 enhancer activity in the anterior neuroectoderm . In addition , widespread expression of Ci-Otx in the anterior neuroectoderm contributes to the broad D1 trans-activation potential that encompasses the ANB , anterior epidermis and anterior sensory vesicle and is probably defined in D1 by the conserved GATTA/TAATC duplicated sequences . We cannot exclude the possibility that endogenous Ci-Pitx maintains its own expression through the same GATTA/TAATC BS , which binds PITX as well as OTX proteins . However , Otx is the best candidate for the onset of D1 activity , which begins exactly at the same time as the onset of the endogenous Ci-Pitx expression . Of the three different duplicated BS that we identified for the ANB expression domain of Pitx and that we suppose to be specific to this restricted area of the anterior neuroectoderm , we concentrated our effort only on the binding sites for OTX as these are the only ones assignable to a well-characterized transcription factor . The observation that the transcriptional response to the broadly expressed head field-selector gene Otx is mediated by duplicated GATTA motifs led us to investigate whether this regulatory architecture was overrepresented in candidate Otx target genes in early tailbud embryos . At this stage , Ci-Otx expression extends over a broad domain referred to as in the anterior neuroectoderm , which derives from the a-line blastomeres and encompasses the ANB as well as other specific neurectodermal territories such as the anterior sensory vesicle , palps , a-line epidermis and rostral trunk epidermal neurons ( RTEN ) . Therefore , we reasoned that candidate Otx target genes could , in principle , be expressed in all or part of the anterior neurectoderm . Hence , we asked whether duplicated GATTA motifs –the candidate signature for Otx binding- were enriched in the conserved noncoding sequences flanking genes with conspicuous expression in the anterior neurectoderm . To this end , we obtained whole mount in situ hybridization data for 1518 genes showing tissue-specific expression from the model organism database ANISEED ( December 2007 , http://crfb . univ-mrs . fr/aniseed , see also Protocol S1 ) . From these , we selected genes that are expressed in the central nervous system ( CNS ) and the ANB and classified them into different territories according to their expression along the antero-posterior axis: following previous reports [49]–[51] , the ascidian visceral ganglion and the nerve cord were considered as “posterior” CNS whereas the whole sensory vesicle , including the ANB , constitute the “anterior” nervous system . This lead to a detailed annotation of nervous system expression patterns for 258 genes ( Table S2 ) . From this list we retained only those 100 genes that are specifically expressed in the anterior and not the posterior parts of the CNS . Finally , we obtained annotations for additional genes expressed in tissues like muscle , epidermis or notochord , from the database ANISEED . This latter set of genes was used as negative controls , which allowed for background definition for further statistical analyses . In total , our set includes annotations for 904 genes . We then aimed at studying the distribution of duplicated short DNA motifs around these 904 genes to find those that show a bias towards genes expressed in the anterior or posterior nervous system , muscle , epidermis or notochord . We concentrated on conserved non-coding elements ( CNEs ) , as these have been shown to be enriched in developmental enhancers [52] , [53] . To obtain these elements for the genome of Ciona intestinalis , we created a whole-genome alignment with Ciona savignyi [54] and removed aligned positions in transcribed regions from it . This results in 168306 CNEs with an average length of 143 bp . Then , we searched for duplicate matches to all 512 possible pentamers within 125 bp of all CNEs in the Ciona intestinalis genome and subsequently calculated the number of tissue-specific neighboring genes associated to each duplicated conserved pentamer and tissue . The rationale for using consensus and not matrix-based searches was that all subclasses of homeodomain proteins have well characterized binding sites that resemble pentamer motifs without degenerate positions [25] , [26] . For the window size parameter , we observed from our case study that the sites had to occur in duplicates with a maximum distance of about 125bp , which was the total length of the fragment between both OTX-sites in the 75bp spacer construct . The score we chose was inspired by [55]; it does not require a sequence background model . This “motif-tissue-score” is the negative logarithm of the binomial probability to obtain a certain number of annotated genes from a given tissue by chance and therefore reflects the association of individual pentamer motifs with specific tissues . Our first observation was that a duplicated OTX ( GATTA ) motif within 125 basepairs appears among the motifs with the highest score in the anterior CNS region ( Table S3 ) . For instance , genes containing duplicated GATTA motifs within 125bp in their flanking conserved genomic DNA are more likely to be expressed in the anterior nervous system than in any of the other tissues used in this analysis , including the posterior CNS ( 26% versus 12% or less , Table 1 ) . We then set out to assess the robustness of this analysis to variations of all three parameters: copy-number , window size and gene annotation . We varied the number of motif-duplicates from one to four and still obtained the highest motif-tissue scores in the anterior region with two copies . Increasing the window size from 25bp to 300bp did not change the scores to a large extent and the relative order between the anterior nervous system and other tissues always remained the same ( Figure S3 ) . The influence of errors in the manual annotation process was investigated by a simulation: we randomized 10% of all gene annotations and repeated this procedure 100 times . The 95% confidence intervals from these are small compared to the total differences between the tissues ( Figure 4 ) . These results indicate that a biased distribution of GATTA motifs in CNEs supports the model of anterior ectodermal expression based on D1 enhancer analysis . We conclude that the presence of duplicated and conserved OTX binding sites in a cis-regulatory element is a signature for anterior neuroectoderm enhancer activity . We then sought to test whether conserved sequences containing duplicated GATTA motifs act as enhancers in the anterior neuroectoderm . Out of all 53 CNEs with at least two conserved GATTAs in a 125 bp window that flank genes expressed in the anterior nervous system , we selected 30 CNEs . We succeeded in cloning 23 of them into a lacZ expression vector . After electroporation , we observed that ten of them are active enhancers in various domains of the anterior neuroectoderm derivatives , where Otx is expressed at the tailbud stage ( Figure 5 , Figure S2 , and Table S4 ) . The remaining non-coding regions were inactive or drove non-specific expression in the mesenchyme , as is often observed in electroporated ascidian embryos [56] , [57] . This ratio of positive elements is high compared to a previously published enhancer screen of random DNA fragments ( 5 active enhancers out of 138 tested fragments ) [57] and similar to a prediction based on binding site occurrences in Drosophila muscle founder cells ( 6 out of 12 tested elements ) [58] . We were unable to identify additional motifs that would be predictive of enhancer activity in the anterior neurectoderm ( Figure S4 ) . However , additional motifs are required in natural enhancers , as we showed that pentamers of GATTA alone were unable to drive reporter gene activity in the Otx expression domain ( data not shown ) . The diversity of expression patterns obtained with the ten active enhancers rather suggests that different transcription factors , each specific for a subdomain of the anterior neuroectoderm , might be implied in the activity of these elements . Thus , while there might be additional motifs necessary for anterior neuroectoderm expression , this study shows the importance of the duplicated GATTA regulatory architecture as a predictive tag for the identification of anterior enhancers in chordates . Could a signature based on GATTA-sites also be predictive in vertebrates ? [52] reported that GATTA is over-represented in forebrain enhancers and used it as one of six motifs to predict forebrain enhancers in the mouse genome . We also found other overrepresented motifs in anteriorly expressed genes ( see Table S3 ) . Therefore , as determined experimentally with the D1 element , additional complexity must supplement the duplicated GATTA sites to achieve a cell-specific expression . Similar approaches performed in Drosophila and Caenorhabditis have identified several binding sites , which correspond to factors that specify a particular fate or behaviour in a combinatorial fashion , such as the myogenic factors [58] , [59] . However , our study identifies for the first time a cis-regulatory signature that determines the transcriptional response to a “master” homeobox gene in a simple chordate and establishes a model for genome-wide predictions of tissue-specific enhancers .
Adult Ciona intestinalis were purchased at the Station de Biologie Marine de Roscoff ( France ) and maintained in artificial sea water at 15°C under constant illumination . Eggs and sperm were collected from dissected gonads and used in cross fertilizations . Electroporations , using 70 µg of DNA , and LacZ stainings were performed as previously described [36] . Embryo staging at 13°C were done according to [60] , [61] . Images were taken on a Leica DMR microscope . For the mutational analysis of the enhancer D1bcde ( Figure 1 ) , we omitted the first 16 bp ( AAACGCGACGACCTCC ) of D1abcde that were not conserved between Ciona intestinalis and savignyi . Each of the mutations was designed to perturb DNA-binding of the candidate trans-acting factors following various reports in the literature . Mutations were performed using the Stratagene QuickChange Kit . Seven new constructs called m0 , m1 , m2 , m3 , m4 , m5/6 , m7/8/9 were generated . After each electroporation , we observed LacZ expression in the tissues of the anterior neural boundary , anterior epidermis , ventro-anterior sensory vesicle and mesenchyme . We obtained a semi-quantitative estimation of the promoter activity by calculating the percentage of positive embryos . Plasmids with artificial enhancers were designed by cloning inserts into the pCES2::lacZ vector that contains the basal Ci-Fkh/FoxA promoter [57] . Insert D1 ( ab ) was generated by cloning two long complementary primers with XhoI/XbaI cohesive ends into pCES2 . Inserts ( abde ) , ( abd ) , ( ab ) ( ab-Pdel ) , ( ab ) ( ab-Omut ) , ( ab ) ( ab-Tmut ) , ( ab ) ( ab-Gmut ) were generated by cloning a second insert consisting of another couple of long complementary primers into the XbaI/BamHI site of D1 ( ab ) . The insert of D1 ( ab ) ×5 was designed in silico , synthetized by Genecust Europe ( Luxembourg ) and cloned into pCES2::LacZ between XhoI and BamHI . To obtain D1 ( ab ) ( ab ) , we cut out the first two parts of D1 ( ab ) ×5 with SalI/XhoI and ligated them into pCES2 . The spacer sequence between both ( ab ) parts of D1 ( ab ) -xx- ( ab ) constructs was created in silico by avoiding all octamers bound by homeodomain factors from a large-scale DNA-protein binding assay [25] . We recursively added random nucleotides to an unbound sequence and backtracked if the new sequence contained an octamer with PBM enrichment score >0 . 3 from the UniProbe database [62] . These constructs , D1 ( ab ) -xx- ( ab ) are also derived from D1 ( ab ) , but the insert was synthesized by GeneScript Corporation ( Piscatway , NJ , USA ) . We amplified spacers of the appropriate length by PCR from the longer fragment and cloned them between the two duplicated ( ab ) fragment by restriction/ligation . A pSix3:Venus plasmid was digested by BamHI/EcoRI to eliminate the Venus/YFP reporter . Plasmids containing non-coding elements were created with the Gateway Technology System ( Invitrogen Carlsbad , CA , USA ) . We cloned an AttR3/AttR4 Gateway Cassette from [63] into the XhoI/XbaI-site of pCES2 and called the resulting construct AttR3R4-pCES2 . Predicted fragments were first amplified by primers including part of the flanking AttB3/AttB4-sequences and then extended by a subsequent PCR to the full length sequences of AttB3/AttB4 . These fragments were recombined with BP clonase into the P3/P4-donor Vector [63] and the resulting entry vectors recombined with LR clonase into AttR3R4-pCES2 producing expression vectors . Computational methods are described in Protocol S1 . Programs that were used for whole-genome analyses are accessible at http://genome . ciona . cnrs-gif . fr/scripts/ .
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Regional identity in embryos is defined by a few specific transcription factors that activate a large number of target genes through binding to common tags in regulatory sequences . In chordates it is unclear if such tags can be identified in the cis-regulatory regions of regionally expressed genes . To address this question we focused on the anterior nervous system where Otx codes for a transcription factor that triggers expression of many other head-specific genes . We analyzed an element that is active in the region bordering the anterior nervous system in the marine invertebrate Ciona intestinalis . We found that the crucial binding sites have to be duplicated and close enough . One of the pairs is bound by OTX . We showed that anterior nervous system genes are often flanked by duplicated OTX binding sites . We confirmed by transgenic assays that about half of these genomic sequences are active and drive expression anteriorly . This study unravels a simple regulatory logic in the anterior enhancers . It indicates that although there are major changes in the organization of the binding sites at short evolutionary range , conserved expression patterns are partly generated by a duplicated organization of conserved binding sites for region-specific transcription factors .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/comparative",
"genomics",
"computational",
"biology/sequence",
"motif",
"analysis",
"computational",
"biology/transcriptional",
"regulation",
"developmental",
"biology/developmental",
"evolution",
"developmental",
"biology/pattern",
"formation",
"developmental",
"biology/neurodevelopment",
"computational",
"biology/genomics",
"evolutionary",
"biology/developmental",
"evolution"
] |
2010
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A cis-Regulatory Signature for Chordate Anterior Neuroectodermal Genes
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Bluetongue virus ( BTV ) causes hemorrhagic disease in economically important livestock . The BTV genome is organized into ten discrete double-stranded RNA molecules ( S1-S10 ) which have been suggested to follow a sequential packaging pathway from smallest to largest segment during virus capsid assembly . To substantiate and extend these studies , we have investigated the RNA sorting and packaging mechanisms with a new experimental approach using inhibitory oligonucleotides . Putative packaging signals present in the 3’untranslated regions of BTV segments were targeted by a number of nuclease resistant oligoribonucleotides ( ORNs ) and their effects on virus replication in cell culture were assessed . ORNs complementary to the 3’ UTR of BTV RNAs significantly inhibited virus replication without affecting protein synthesis . Same ORNs were found to inhibit complex formation when added to a novel RNA-RNA interaction assay which measured the formation of supramolecular complexes between and among different RNA segments . ORNs targeting the 3’UTR of BTV segment 10 , the smallest RNA segment , were shown to be the most potent and deletions or substitution mutations of the targeted sequences diminished the RNA complexes and abolished the recovery of viable viruses using reverse genetics . Cell-free capsid assembly/RNA packaging assay also confirmed that the inhibitory ORNs could interfere with RNA packaging and further substitution mutations within the putative RNA packaging sequence have identified the recognition sequence concerned . Exchange of 3’UTR between segments have further demonstrated that RNA recognition was segment specific , most likely acting as part of the secondary structure of the entire genomic segment . Our data confirm that genome packaging in this segmented dsRNA virus occurs via the formation of supramolecular complexes formed by the interaction of specific sequences located in the 3’ UTRs . Additionally , the inhibition of packaging in-trans with inhibitory ORNs suggests this that interaction is a bona fide target for the design of compounds with antiviral activity .
Bluetongue is a vector-borne hemorrhagic disease of livestock and is responsible for considerable economic losses to international livestock industries [1 , 2] . The disease is caused by Bluetongue virus ( BTV ) a non-enveloped virus ( a member of Reoviridae family ) with a double-capsid icosahedral particle and a double-stranded 10-segmented ( S1-S10 ) RNA genome . During virus entry into the cells , the outer capsid of all members of the family including BTV , disassembles from the inner capsid ( termed the “core” ) , which remains intact . The core synthesizes transcripts that are translated into viral proteins , and act as templates for synthesis of genomic dsRNAs [3–5] . However , recent data demonstrated that the ssRNA templates are packaged prior to synthesis of genomic dsRNA [6] . Each BTV RNA segment encodes for one protein except S9 and S10 , which encode for two proteins [7 , 8] . Based on their size , the 10 segments are classified as large ( S1-S3 ) , medium ( S4-S6 ) and small ( S7-S10 ) . The 5’ untranslated region ( UTR ) of each of the ten segments of BTV varies in length from 9 nucleotides for S4 to 35 nucleotides for S6 . The 3’ UTRs of each segment also vary in length , being generally longer than the 5’ UTRs and contain a highly conserved hexanucleotide sequence [9] . Due to this , the 3’UTR of each segment have long been thought to contribute to the complex process of RNA sorting and encapsidation and evidence has recently been obtained suggesting that the process of individual recruitment of RNA is likely to be initiated by S10 which then recruits other RNA segments in sequential order , from smaller to larger [6 , 10] . It has also been hypothesized that the 3’ and 5’ UTR stem loop and hairpin loop structures interact and mediate a conformational change that also relate to packaging [11] . However , direct evidence for RNA-RNA interactions and the involvement of the 3’UTR in sorting and packaging of the BTV genomes have not been demonstrated to date . To investigate the mechanism of BTV genome packaging , a series of short single-stranded synthetic oligoribonucleotides ( ORNs ) complementary to specific RNA motifs of different genomic segments was used as competitive agents based on predicted RNA secondary structure . Designed ORNs were found to be inhibitory for virus replication in cell culture but did not inhibit in vitro protein synthesis . The inhibitory effects were further investigated using novel in vitro assay systems able to detect supramolecular complex formation via specific RNA-RNA interactions . The data is consistent with inhibitory ORNs targeting regions in the 3’ UTR and leading to inhibition of virus replication by competition with RNA complex formation and packaging . The study revealed RNA-RNA interactions driven by the smallest segment , S10 but also by S7 suggesting that specific multi-site interactions between different segments are required to trigger the packaging of BTV RNA segments . Interchanging 3’ UTRs among segments prevented virus recovery , indicating that the newly mapped packaging/ RNA interaction signals on each BTV segments are specific to their resident segment .
Our previous data suggested that the 3’ UTRs are essential for packaging of positive sense ssRNAs during BTV assembly and that the packaging is initiated by the smallest segment S10 [10] . We sought to investigate whether small specific antisense oligoribonucleotides ( ORNs ) targeting the 3’ terminal sequences of these smaller segments would interfere with BTV growth . A set of ORNs complementary to the UTRs of positive sense ssRNA of S9 and S10 were designed based on the predicted RNA secondary structure as no RNA probing data is available for BTV , to date ( Fig 1 , S1and S2 Figs ) . For stability and to avoid the cellular immune response , the 2’OH of the ribose of each ORN was modified to 2’O-methyl . The sequences of each ORN are presented in Table 1 . Six ORNs complementary to different regions including the 3’ conserved terminus of the S10 ( Fig 1C ) were designed to interfere with the RNA structures ( shown in S1–S3 Figs ) , and three of which encompassed the entire length of the S10 3’ UTR . S10 . 1 was complementary to the 3’ terminal 41 nt ( nt822-782 ) including the conserved sequence , 39 nt of S10 . 2 was complementary to nt737-699 , including the stop codon , and the 34 nt of S10 . 5 complimentary to nt781-748 , the region between S10 . 1 and S10 . 2 . The other ORNs targeted the structure outside of the 3’UTR; S10 . 3 to the terminal 35 nucleotides of the coding region ( ORF ) , S10 . 4 in the ORF ( nt595-561 ) and S10AUG , the initiation codon . For segment 9 ( S9 ) , the 3’ UTR consists of 44 nts ( nt1049-1006 ) , and thus , three ORNs encompassed part of the UTR and part of the 3’ ORF ( Fig 1B ) . One ORN ( S9 . 1 ) was complementary to the 3’ terminal 33 nt ( nt1049-1017 ) , while ORNs S9 . 2 and S9 . 3 were complementary to the last 40 nucleotides of the coding region including the stop codon ( nt1005-966 ) or the middle section of the coding region ( nt427-391 ) , respectively . In addition , for positive controls , ORNs complementary to the 5’ UTR regions including the AUG codons of both S9 ( S9 AUG ) and S10 ( S10 AUG ) ( Fig 1B & 1C; Table 1 ) and a SCR sequence of 30 nucleotides were also synthesized . The secondary structures of S9 and S10 and position of ORNs are shown in S1–S3 Figs . For in vivo assay , the concentration of ORNs was first optimized and subsequently BSR cells were transfected with 1 . 5 μM of each ORNs and Scr ORNs . At 3 hours post-transfection ( hpt ) , cells were infected with BTV-1 of MOI of 0 . 1 and virus titres were monitored 16 hpi . Analysis of each ORN-transfected BSR cells followed by infection with BTV-1 showed S10 ORNs had a negative effect on virus yield albeit to a varying degree . Specifically , ORN S10 . 2 was the most inhibitory where virus yield was reduced by ~90% while S10 . 3 had also a significant effect on virus replication with ~70% reduction in comparison to that of the control ( Fig 1D ) . These ORNs were complementary to the 3’ end of the coding region ( S10 . 3 ) and beginning of the 3’ UTR ( S10 . 2 ) . Secondary structure prediction of S10 revealed the S10 . 2 ORN was complementary to a GC rich hairpin loop , a bulge and a double-stranded region ( S1 Fig ) . S10 . 1 ORN , which covered the terminal 41 nts of 3’UTR , also had a significant inhibitory effect on virus yield ( ~70% reduction ) , consistent with our previous report [11] . In contrast , ORN S10 . 4 , which targeted part of the coding region ( nt595-561 ) was less inhibitory . That all S10 antisense ORNs had some interference activity on virus replication is consistent with the smallest BTV RNA segment playing a crucial role in virus replication , as reported [10] . In contrary to S10 , S9 . 1 ORN , complementary to the last 33nt of S9 3’ UTR , had only a marginal effect on virus recovery ( Fig 1D ) . However , virus growth was reduced by ~80% in the presence of S9 . 2 , which encompasses the 40 terminal nucleotides ( UTR+ORF ) and to a lesser extent , ~50% , by S9 . 3 ORN ( ORF only ) . As expected , the presence of the control ORNs , S10 AUG or S9 AUG , virus growth was severely reduced . On the contrary , parallel assays with scrambled sequences showed no inhibitory effect on virus replication . Further , no cell toxicity was observed up to 48 hrs of incubation of BSR cells with different concentrations of Scr ORNs ( 0 . 1–2 . 5μM ) followed by staining the viable cells ( S4 Fig ) , indicating that the effects of ORNs observed on BTV infected cells were specific to BTV replication . Based on the inhibitory results of the ORN targeting the 3’UTR , we also investigated the effect of an ORN that encompasses an entire 3’UTR . We selected S1 as it possesses the shortest 3’UTR ( 24 nt ) of all BTV RNA segments . To this end , we designed an ORN complementary to the entire length of the 3’UTR and , as positive control , another to the 5’UTR including the AUG codon ( Fig 1A ) . Virus titer was reduced to ~20% in the presence of the S1 3’ ORN as compared to control without ORN and was similar to that of the 3’ UTR ORNs of S10 ( Fig 1D ) . Antisense oligonucleotides could trigger steric blocking of viral mRNA and thereby perturb the translation of viral mRNAs , therefore we examined if the inhibition of virus growth was due to the interfering effect of ORNs on the efficiency of virus protein expression . To validate this , we performed a cell-free translation in the presence or absence of ORNs complementary to the initiation codons of S1 ( VP1 ) , S9 ( VP6 ) and S10 ( NS3/NS3A ) or the 3’ UTR region . Analysis of translated products showed that VP1 , VP6 , NS3/NS3a viral proteins were efficiently translated in the presence of ORNs complementary to the 3’UTR regions ( Fig 2A–2D ) . In contrast , a marked reduction of encoded protein levels were observed in the presence of S1 , S9 and S10 AUG ORNs , respectively ( Fig 2A–2D ) , consistent with the in vivo data ( Fig 2D ) . Conversely , scrambled ORN control did not inhibit the translation of S9 and S10 mRNAs ( Fig 2B–2D ) , indicating sequence specificity of the ORNs to block their target regions . The significant inhibition of virus replication in the presence of 3’UTR ORNs in vivo in contrast to the efficient BTV protein synthesis in vitro suggests a mechanism of action whereby 3’UTRs of BTV RNA segments are important in virus replication . Since ORNs inhibited virus replication but did not affect protein translation , ORNs have most likely interrupted the RNA-RNA interactions and packaging during virus replication . To investigate these it was necessary to visualize the formation of RNA complexes in absence of ORNs . We modified an electrophoretic mobility shift assay ( EMSA ) for visualization of RNA complexes from RNA segments of dsRNA virus , which allowed us to visualize RNA interactions and large complex formation following two different experimental approaches: ( 1 ) Co-incubation of two purified ssRNA segments for hybridization assay and ( 2 ) Co-transcription of T7 cDNA copies of segments in pairs or in combinations of 3 or 4 . The EMSA analysis of co-incubation products exhibited shifted weak bands for combinations of S7+S8 , S7+S9 and S7+S10 ( Fig 3A , lanes 5 to 7 ) indicating that S7 interacts with each of the other three small segments to form a complex . Other RNA segment combinations did not show any distinct retarded bands ( Fig 3A , lanes 8 , 9 , 10 ) even in the presence of S10 . In contrast to co-incubation , distinct retarded bands appeared when two segments were co-transcribed from T7 cDNAs ( Fig 3B , lanes 5 to 10 ) , except S8+S9 ( Fig 3B , lane 8 ) , suggesting that RNA segments were interacting during or soon after they were synthesized and that the presence of either S7 or S10 stimulated the complex formation . In three or four co-transcribed RNA segments , stronger intermolecular interactions were detected with additional shifted bands in each case and the amount of free , unbound RNA was also less than when only two segments were co-transcribed ( Fig 3 ) . Further , the appearance of additional RNA complex were noticeable when S7 and S10 were present in the reaction ( Fig 3B , compare lanes 5 to 10 and 11 to 14 ) suggesting that although S10 plays a key role in bringing the smaller segments together , S7 is also necessary to form a RNA network of all four segments . The addition of S10 in a reaction of S7 , S8 and S9 also led to stronger retarded bands ( Fig 3B , compare lanes 11 and 15 ) which strengthens the role of S10 in the intermolecular interaction . It was evident that the presence of S7 , which has the second longest 3’ UTR after S10 , ( Fig 3B , compare lanes 8 to 10 and 11 to 13 , also compare lanes 14 to 15 ) is crucial for strong complex formation . Table 2 summarizes the results obtained from the RNA-RNA interaction studies of purified and co-transcribed segments . The specificity of RNA-RNA interactions was tested in the presence of non-specific competitor yeast tRNA at 20 to 50 fold molar mass excess and the level of complex formation was not significantly reduced ( Fig 3C ) indicating that interactions between RNA segments were sequence specific . To determine if the RNA complexes following co-transcription of multiple segments could be disrupted by ORNs targeting the S10 3’UTR , all four small RNA segments or different combinations of three ( S7+S8+S9 , S7+S8+S10 , S7+S9+S10 , S8+S9+S10 ) were co-transcribed in the presence or absence of 20 pmol of either S10 . 2 and S10 . 5 ORNs ( most inhibitory ORNs in virus replication ) or S10 . 4 ORN ( non-inhibitory ORNs targeting the ORF ) ( see Fig 1A , 1B & 1C ) . EMSA data showed that RNA complexes in the presence of S10 . 2 and S10 . 5 were reduced up to four fold when compared to the control RNA complexes ( Fig 4A & 4C ) but not with S10 . 4 . When the same reaction was performed in the absence of target RNA S10 ( i . e . S7+S8+S9 only ) the RNA complexes were not affected by the presence of S10 . 2 or S10 . 5 ORNs ( Fig 4A & 4B , lanes 5–6 ) . The RNA complex formed by S8 , S9 and S10 ( but not S7 ) in the presence or absence of S10 . 5 ORN was too weak to ascertain the inhibition activity ( Fig 4B , lanes 11–12 ) . These data suggest that the intermolecular interactions among the four smaller segments requires both S10 and S7 and interactions initiated by the S10 and S7 could be specifically disrupted by S10 . 2 ( 39 nt ) or S10 . 5 ( 34 nt ) . These results emphasize that sequences encompassing by these two ORNs at the 3’UTR downstream of the S10 stop codon are involved in intermolecular RNA-RNA interaction . The S10 . 2 ORN was designed to target the GC rich hairpin loop , bulges and duplex while S10 . 5 targeted a duplex and hairpin loop ( S1 Fig ) . Results also suggested that the terminal 41 nt of S10 3’ UTR ( S10 . 1 ) or the last 35 nt in the S10 coding region ( S10 . 4 ) are not essential for interactions . The specificity of the ORN to inhibit RNA-RNA interactions was further demonstrated by Scr ORN , which had no effect on RNA complexes ( Fig 4B , lane 16 ) . The integrity of the transcribed RNAs was confirmed by denaturing gel analysis of the co-transcribed ssRNA segments which showed the position of the transcribed RNAs of each segment ( Fig 4D ) . The presence of distinct bands of complexes and unbound RNAs as detected by EMSA demonstrating the RNAs were transcribed by these plasmids in presence of ORNs . Hybridization assay also showed that ORN S9 AUG and ORN S9 . 2 hybridized with S9 mRNA , while ORN S10 AUG and ORNs S10 . 2 , S10 . 3 , S10 . 5 annealed to S10 mRNA . No hybridization with Scr control was detected when incubated with S10 and S9 mRNAs ( S10 Fig ) . The decreased RNA complex formation in the presence of S10 3’UTR ORNs prompted us to explore the key regions in S10 RNA responsible for recruiting other segments and complex formation . Deletion mutants in S10 which spanned the sequence of inhibitory ORN were constructed and used in the RNA-RNA interactions with other segments ( Fig 5A ) . The regions of deletion mutations are shown in S5 Fig . Up to four fold reductions in RNA complex formation were observed with each of S10 . 2 and S10 . 5 deletion mutants in combination with S7+S8 , S7+S9 and S7+S8+S9 when compared with the reactions with wild-type S10 ( Fig 5B ) . As previously , in the absence of S7 , no complex was detectable when S8 and S9 , were used with either S10 or S10 mutants . The RNA structures of deletion mutants showed that when target regions of S10 . 2 and S10 . 5 were deleted , the hairpin loops and bulges were either significantly altered or absent compared with the wild-type structure ( S5 Fig ) . This was consistent with the results obtained when using ORNs to inhibit RNA interactions ( see Fig 4A & 4B ) suggesting multiple sites in S10 are necessary for sorting and recruitment of other segments . The reduction of RNA complex formation in a reaction with deletion mutants S10 . 2 and S10 . 5 suggests the key role of S10 in recruiting other segments for complex formation and the importance of the sequence in the S10 3’UTR for intermolecular interactions which become more evident in the presence of S7 in the interaction reaction . The integrity of transcribed RNAs was confirmed by denaturing gel electrophoresis analysis of the co-transcribed wild-type and mutant RNA segments ( Fig 5C ) . The results obtained from RNA-RNA interaction studies in the presence or absence of ORNs and S10 deletion mutants are summarized in table 3 . To understand further the mechanism of action of S10 . 2 and S10 . 5 ORNs and to determine if the inhibitory effects of ORNs on virus growth and RNA-RNA interactions were directly related to BTV RNA packaging during capsid assembly , we utilized a unique cell-free core assembly system that has been successfully used to understand the order of BTV capsid assembly and the genomic segment packaging previously [6 , 10] . For this study , S10 . 1 , S10 . 2 , S10 . 5 , S10 . 4 , S10 AUG and Scr ORNs were annealed to S10 transcripts prior to mixing with the remaining 9 BTV ssRNA segments and subsequently incubated with pre-translated transcription complex ( VP1 , VP4 and VP6 ) before adding two major core proteins , VP3 and VP7 sequentially . After removing the unpackaged ssRNAs by RNase treatment , the putative in vitro assembled cores were purified by centrifugation on a sucrose gradient followed by fractionation , ssRNAs isolation and analysis as described in Methods and Materials . Only S10 . 2 or S10 . 5 ORNs , ( in fraction 6 ) inhibited the packaging of 10 BTV ssRNA with ~80% and ~60% reduction respectively ( Fig 6 , lanes 4–6 & 8 ) . The inhibition of packaged RNAs was not detected in presence of S10 . 4 and Scr ORNs ( Fig 6 , lanes 7&9 ) or with S10 . 1 and S10 AUG ORNs ( S7 Fig ) . This indicates that by base pairing to the complementary sequences in the S10 , both ORNs were capable of inhibition of recruitment and packaging of the not only S10 but all the other 9 segments , possibly due to disruption of RNA-RNA interactions . To confirm that core proteins were still synthesized efficiently in the cell-free assembly assay , each protein was 35S-labeled and the fractionated complex was analyzed by SDS-PAGE . The 35S-labelled reconstituted protein products showed the complete set of core proteins , the three proteins of transcription complex ( VP1 , VP4 and VP6 ) and the two major core proteins ( VP3 and VP7 ) from fraction no . 6 in the presence or absence of S10 . 2 ORN ( S8 Fig ) which demonstrated that the transcription complex ( TC ) and the subcore proteins were efficiently synthesized and assembled and were not hindered in the presence of S10 . 2 ORN . The effects of different ORNs in RNA packaging by in vitro assembly , in vivo virus replication , in vitro protein synthesis and RNA-RNA interactions are summarized in Table 4 . To confirm if the sequences within the identified 3’UTR regions in S10 RNA are important for RNA packaging in vivo , four substitution mutations were introduced by targeting five or six nucleotides in the putative binding sites of S10 . 2 and S10 . 5 regions at the S10 3’UTR ( S6 Fig & Fig 7A ) . Each mutant S10 ssRNA was used to recover mutant viruses using RG system as described in Materials & Methods [12] . Among the mutants tested , only S10713-718 ( sequence encompassed by ORN S10 . 2 ) and S10743-748 ( sequence encompassed by ORN S10 . 5 ) ( see S5 & S6 Figs ) were successfully recovered but exhibited significantly less cytopathic effects ( CPE ) . Further , ~1000 fold less virus particles were detected by qRT PCR in comparison to that of the wild-type at 72 hours post-transfection ( Fig 7B & S11 Fig ) . The nucleotide substitutions in these two mutants were located in the double stranded region of the stem loop structure ( S6 Fig ) . Mutants S10725-730 and S10728-732 , which encompasses the hairpin loop of the S10 . 2 region , could not be rescued , consistent with a lethal phenotype . To investigate further if the identified packaging signals in S10 3’UTR are interchangeable with other segments , 3’ UTR of S8 ( see S9 Fig ) and S10 ( see S1 Fig ) were exchanged ( S8-UTR10 and S10-UTR8 ) and chimeric ssRNAs were synthesized . When BSR cells were transfected with each of the chimeric RNA segments together with 9 WT ssRNA segments or all 10 WT ssRNAs as control , only control WT virus was recovered while both chimeric segments failed to recover virus . These data suggest that the packaging signals in the UTRs were not functional when interchanged between different segments .
The exact mechanism by which BTV selects its ten genomic RNA segments among the multitude of other RNAs in the host cytoplasm and packages one copy of each into an assembling capsid to generate an infectious virus particle is not well understood . Recently we have suggested that the 10 RNA segments are packaged through a sequential process by RNA interactions involving the 3’UTRs [10] . In influenza A virus , with a genome of eight discrete negative strand segments , specific interactions have been suggested among the ribonucleoprotein complexes or the eight genomic RNA segments are selected and packaged as an organized supramolecular complex [13 , 14] . In Reoviridae , with multiple dsRNA segmented genomes and a complex capsid assembly process , the process is challenging although there have been suggestions that genomic RNAs utilize RNA-RNA interactions in the 3’UTRs for assortment and packaging despite no direct evidence being reported to date [15–17] . Current study was therefore aimed to investigate the specific RNA-RNA interactions among the BTV transcripts , which lead to the formation of supramolecular RNA networks and RNA packaging using a range of in vivo and in vitro experiments sequentially . Our initial approach in this study , was to utilize short complementary ORNs to assess their effects on virus replication . Several of these ORNs , notably ORNs targeting the 3’ UTRs of S1 and S10 , had inhibitory effects on virus growth but not on protein synthesis , suggesting that the inhibition is not at the level of translation and prior to genome encapsidation , but possibly at the stage of genome segment sorting and packaging , consistent with our previous findings [10 , 11] . The UTR regions are also thought to be crucial for forming the higher order RNA structure of BTV ssRNA segments . For other segmented ssRNA viruses , such as influenza virus [18–22] and the phi6 bacteriophage [23] , the hierarchical intermolecular interactions between segment structures have been implicated in facilitating the efficient packaging of the viral genome . Based on this , we performed subsequent in vitro studies targeting predominantly S10 ssRNA and other three smaller ssRNA segments ( S7 , S8 & S9 ) in order to facilitate the identification of supramolecular complexes and their disruption by antisense ORNs by EMSA . In particular , we examined co-transcription reaction products of ssRNAs S7-S10 in different combinations since it would allow de novo interactions between different transcripts as they were transcribed . Complexes with four segments were readily formed and were detectable in EMSA , indicating that such complexes possibly mimicked the phenomena of nascent BTV RNA interacting together through the RNA sorting and packaging signals prior to encapsidation . However , when various combinations of two or three segments were used , it was evident that both S7 and S10 RNAs not only interacted with each other but each also interacted with the other two small segments , S8 and S9 . These data indicated that both S7 and S10 are important for formation of stable RNA complex and that the RNA complexes are formed through multi-segment interactions and not solely controlled by S10 as previously proposed . Further , two ORNs that targeted S10 3’ UTR ( S10 . 2 and S10 . 5 ) , could inhibit complex formation significantly between the S10 RNA and three other segments . Thus , these results indicated that when both ORNs bind in the S10 3’ UTR of S10 , the predicted structures which consisted of hairpin loops , bulges and GC rich motifs were altered and affected RNA interactions . Further confirmation of importance of these ORNs regions were obtained by using two deletion mutants , ΔS10 . 2 and ΔS10 . 5 that lacked the corresponding ORN binding regions . Both mutants S10 exhibited significant reduction in RNA complex formation , which suggested that either the deleted sequences may form a part of the interaction site of other segments or the deletions might have perturbed the secondary structure of these regions , both of which are located in the hairpin loop and double-stranded region of hairpin stem . The importance of these structured motifs at the 3’ UTR was then demonstrated by using substitution mutations of five or six nucleotides to recover viable virus by reverse genetics , and the results showed mutations were highly lethal to virus viability . Changes in these sequences might have triggered conformational changes resulting in the loss of ssRNA recruiting function of S10 during capsid assembly . Interestingly , the interchange of 3’UTRs between S10 and S8 RNA segments found to be non-functional and had abrogated virus recovery . This may signify the need for segment specific sequences to trigger intramolecular interactions in individual segments itself and conformational changes on the RNA structure prior to interactions and base-pairing between segments which was abolished when the 3’UTR was removed . Most likely , 3’UTRs act as part of the secondary structure presented by the entire genomic segment , rather than as a linear sequence . This is consistent with data obtained on interchanging packaging signals in the 3’UTR of influenza A virus [24] . The data obtained from a series of in vitro and in vivo studies confirmed that small RNA nucleotides interfere in the recruitment and packaging of the ssRNA genomic segments and that genome packaging in this segmented dsRNA virus occurs via the formation of supramolecular complexes generated by the interaction of specific sequences located in the 3’ UTRs . Our data also indicate that RNA segment sorting occurs via specific interactions among the different segments followed by the supramolecular complex formation and packaging by the assembling core . Reverse complementary or “antisense” oligonucleotides have been used extensively in recent years to study virus life cycles , including insight into RNA packaging signals , in addition their potential as antiviral molecules has also been demonstrated for a number of viral targets [13 , 19 , 25–31] . Our study , however , is the first to use ORNs as a tool for understanding dsRNA virus packaging and this has potential as a therapeutic strategy . Furthermore , the approaches used here to identify the possible location of an RNA packaging signal in the smallest segment of BTV can be applied to packaging signal analysis of related dsRNA viruses . These signals are a potential target for future research of BTV antivirals and could pave the way for the development of a small molecule based therapeutics to control this economically important virus .
Bluetongue virus serotype 1 ( BTV-1 ) South African reference strain was plaque purified and amplified in BSR cells , a BHK 21 clone derivative of baby hamster kidney cells ( American Type Culture Collection ) grown in Dulbecco modified Eagle medium containing 5% fetal calf serum ( FCS ) penicillin , streptomycin and amphotericin B at 35°C with 5% CO2 . Virus stocks were maintained by infecting BSR cells at multiplicity of infection ( MOI ) of 0 . 1 and harvested at 48–72 hpi . T7 transcripts were generated from exact cDNA copies of BTV-1 genome segments 7 , 8 , 9 and 10 ( GenBank accession numbers FJ969719–FJ969728 ) , flanked by T7 promoter and specific restriction enzyme sites [12] . For the generation of S10 RNA deletion mutants , two S10 deletion constructs corresponding to the target sequences of S10 . 2 ( 39 nts ) and S10 . 5 ( 34 nts ) ORNs were generated by polymerase chain reaction ( PCR ) through site-directed mutagenesis [32] . Amplicons were then treated with DpnI to digest the parental plasmid prior to transformation into competent cells . For the generation of four S10 RNA substitution mutants S10 . 2713–718 , S10 . 2725–730 , S10728-732 and S10 . 5743–748 site directed mutagenesis was performed by overlapping PCR using S10 specific primers . Deletion and interchanging 3’UTRs of S8 and S10 were also generated by overlapping PCR followed by Dpn 1 treatment . Capped BTV RNA transcripts for in vitro translation assay were generated using mMESSAGE mMACHINE Kit ( Ambion ) as described previously [12] . For generation of uncapped ssRNA for cell-free assembly , linearized DNA were incubated at 37°C for 2 h with 40 U of T7 RNA polymerase ( Thermo Scientific ) , 50 mM DTT , 0 . 5 mM each rNTP and 10 U RNase inhibitor ( Thermo Scientific ) . A series of thirteen antisense oligoribonucleotides ( ORNs ) were designed to hybridize either the 5’UTR including the AUG initiating codon , the internal coding region or the 3’ UTR of segments S1 , S9 and S10 ( Table 1 ) . These ORNs were modified at the ribose with 2’O-methyl group ( Integrated DNA Technologies ) and named by their target position in each segment ( Fig 1 ) . A scrambled ( SCR ) sequence of 30 nt , was included as specificity control . The scrambled sequence was verified by NCBI-BLAST software ( http://blast . ncbi . nlm . nih . gov/ ) to prevent any possible match in the BTV genome or the host cellular RNAs . For the design of the ORN target sites the software Mfold ( http://rna . tbi . univie . ac . at/ ) and RNAfold ( http://rna . tbi . univie . ac . at/cgi-bin/RNAfold . cgi ) were used to predict the secondary structure and folding pattern of each RNA segments in the context of a full-length segment . OligoAnalyzer ( http://eu . idtdna . com/calc/analyzer ) was used to analyse each ORNs to avoid structures that might prevent its base-pairing to target RNA ( perfect hairpin , self-dimerization and melting temperatures ) . To determine the optimal inhibitory condition for each ORNs , a concentration range ( 0 . 5 , 1 . 5 and 2 . 5 μM ) of S10 AUG , S10 3’ UTR and SCR were transfected to BSR cells using Lipofectamine 2000 ( Life Technologies ) . After 3 h incubation , the cells were infected with BTV-1 at MOI 0 . 1 for 1 h . The inoculum was removed by 3 washes with low pH medium ( DMEM-HCl , pH 6 ) to inactivate free virus , twice with normal medium to restore pH and incubated with DMEM supplemented with 1% FCS and the appropriate ORNs for one virus replication cycle of 16–18 h . Cells were harvested and the virus titre was analysed by plaque assay . The virus yield was calculated as the mean of plaque forming units per ml ( PFU/ml ) of three independent transfection assays with each 2′OMe ORNs and expressed as the relative PFU/ml of BTV1 transfected without ORNs , consider as 100% . Cytotoxicity was determined by cell staining at the end of the treatment . The optimal concentration for the ORNs was 1 . 5 μM . Different concentration range ( 0 . 5 , 2 and 4 μM ) of ORNs S1 AUG , S1 . 3’ , S9 AUG , S9 . 1 , S9 . 2 , S10 . 1 , S10 . 2 , S10 . 3 , S10 . 5 , S10 AUG or Scr were incubated with BTV transcripts ( 300 ng ) for 20 min at 37°C and added to a reaction mix containing 7 . 5 μl of nuclease-treated rabbit reticulocyte lysate ( RRL , Promega ) , 1 mM amino acid mix minus methionine and 6 μCi 35S-methionine . Translation reactions were incubated at 30°C for 90 min and treated with 1 μl of 1μg/μl RNase A for 10 min at room temperature . Labelled proteins were quantified by densitometry using PhosphorImager screen . The inhibition of BTV protein expression was calculated relative to the control lacking ORNs . The experiment was repeated at least three times . For RNA-RNA interactions of individual RNA segments , 1 μg of linearized plasmid was transcribed in a buffer containing 40 mM Tris–HCl pH 7 . 5 , 10 mM MgCl2 , 20 mM NaCl2 , 3 mM spermidine , 50 mM DTT , 5 mM each rNTPs , 10 U RNase inhibitor and 40 U of T7 RNA polymerase ( Thermo Scientific ) for 3 h at 37°C followed by RNase free DNase 1 treatment . Transcribed RNAs were extracted by standard phenol-chloroform method and re-suspended in RNase free water . RNAs were individually heated at 80°C for 1 min , ice chilled and mixed in pairs in folding buffer ( 50 mM Na cacodylate pH 7 . 5 , 300 mM KCl and 10 mM MgCl2 ) [33] and RNA–RNA complexes were allowed to form for 90 min at 30°C and immediately analysed by electrophoresis in 1% agarose gel supplemented with 0 . 1mM MgCl2 . Electrophoresis gel was run for 180 min at 150 V in TBM buffer ( 45 mM Tris , pH 8 . 3 , 43 mM boric acid , 0 . 1 mM MgCl2 ) and stained with 0 . 01% ( w/v ) ethidium bromide . The integrity of transcribed RNA was checked by denaturing gel electrophoresis . For co-transcription experiments , 150 ng linearized plasmid of each segments ( S7-S10 ) were transcribed either in pairs or combinations of 3 to 4 plasmids ( S7 , S8 , S9 and S10 or S10 mutants ) . RNA transcription was carried out in the same condition as individual RNA segments . Immediately after transcription and DNase 1 treatment , the reaction was analysed on a 1% agarose gel as described above . The percentage of the retarded RNA in each lane was determined against the total mass of input RNA ( % ) by densitometry ( Gene Tools , Syngene ) . For RNA complex inhibition assay with ORNs , the simultaneous transcription of S7-S10 ( combination of 3 or 4 ) was performed in the presence or absence of 20 pmol of S10 . 1 , S10 . 2 , S10 . 4 , S10 . 5 and Scr ORNs and analysed as described above . Non-specific yeast tRNA ( 20 and 50 pmol ) was incorporated in the co-transcription reaction as a control . Quantification of intermolecular RNA complex was performed as described above . For RNA-ORN hybridization assay , 10pmol of S9 AUG , S9 . 2 , S10 AUG , S10 . 2 , S10 . 3 , S10 . 5 and Scr ORNs were 3’ end labelled with 10 μCi [32P]pCp ( Perkin Elmer ) with T4 RNA ligase ( Thermo Scientific ) in T4 RNA ligase buffer and incubated at 4°C overnight . Unincorporated 32P was removed by exclusion chromatography ( Illustra Microspin G-25 column , GE Healthcare ) . Prior to hybridization , unlabelled S10 RNA was denatured at 80°C for 1 min , immediately chilled and then mixed with folding buffer ( 50 mM sodium cacodylate pH 7 . 5 , 100 mM KCl and 10 mM MgCl2 ) . RNA-ORN hybridization was performed with 0 . 5pmol of pre-folded S10 RNA annealed with 32P labelled ORNs ( 1 , 2 and 5 pmol of S9 AUG , S9 . 2 , S10 AUG , S10 . 2 , S10 . 3 , S10 . 5 and Scr ORNs ) in folding buffer in 10 μl final volume [34] . The complex was allowed to form for 30 min at 30°C followed by electrophoresis in 4% native acrylamide gel at 4°C for 50 min at 150V in TBM buffer , dried and exposed by autoradiography . The cell-free system for BTV was carried out as described [6] with some modifications . Briefly , VP1 , VP4 and VP6 were synthesized from RRL system followed by incubation with the complete set of 10 full-length ( 300ng each ) uncapped ssRNAs with or without 20 pmol S10 . 1 , S10 . 2 , S10 . 4 , S10 . 5 , S10 AUG and Scr ORNs . In vitro synthesized VP3 and VP7 were then added to the mixture and further incubated to allow viral core assembly . After eliminating unpackaged RNA by RNase One ( Promega ) digestion , the assembled particles in the reaction mixture were isolated by a 15% to 65% continuous sucrose gradient followed by fractionation as described previously [6] . For positive control , S10 . 2 and S10 . 5 ORN gradients , packaged RNAs were extracted from fractions 5 , 6 and 7 and analysed by denaturing 1% agarose gel electrophoresis to identify the packaged 10 ssRNAs [6] . Only fraction 6 was collected for samples with S10 . 1 , S10 . 4 , S10 . 5 , S10 AUG and Scr ( packaged ssRNAs are previously shown to be present at this fraction ) [6] . For analysis of in vitro incorporated proteins , the in vitro synthesized viral proteins were radio labelled with 35S-methionine , analysed in 9% SDS-PAGE and detected by autoradiography . To generate the virus with S10 mutants ( S10 . 2713–718 , S10 . 2725–730 , S10728-732 and S10 . 5743–748 , and chimeric S10 and S8 ) BSR cells were transfected with mutated S10 ssRNA together with the remaining 9 BTV-1 ssRNAs as described previously [12 , 35] . For combined chimeric S10 and S8 , BSR cells were transfected with mutated S10 ssRNA together with the remaining 8 BTV-1 ssRNAs . Replication of recovered viruses was visualised by crystal violet staining . Virus recovery was quantified by qRT-PCR using specific BTV genomic primers as previously described [10] . To confirm the recovery of mutant virus , genomic dsRNAs were purified from the infected cells , reverse transcribed and the mutated sequences of S10 was confirmed by nucleotide sequencing ( Source Bioscience ) .
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Bluetongue virus ( BTV ) is an economically important pathogen of ruminants that belongs to a group of viruses whose genome consists of multiple segments of double-stranded RNA . In order for the virus to synthesize viable and infectious progeny , a precise set of the 10 newly replicated BTV segments must be selected for packaging into each new virus particle . How the virus is able to select its own genomic strands from the vast array of cellular RNAs is not clearly understood . One possibility is that that BTV segments harbours an interaction signal that allows them to be sorted and packaged as a set . Correct identification of these signals has basic and applied implications for a possible target of antiviral therapeutics through inhibition of genome sorting and packaging process . Here we showed that a series of short oligonucleotides ( ORNs ) complementary to multiple sites on the BTV RNA prevented the growth of viable virus in infected cells . ORNs positive for inhibition in virus growth also prevented the genomic RNA to be packaged in an in vitro packaging assay . Moreover , when these same targeted sequences were deleted or mutated in viral genome , viable virus recovery was abolished . Exchanging the terminal sequences between segments failed to recover virus confirming that such changes are deleterious to virus viability . These studies have identified specific regions and sequences key to genome packaging in dsRNA viruses and viability . The specific genome packaging sequences targeted by inhibitory activities of ORNs are bona fide drug target which , as a mechanism common amongst all serotypes , may represent an Achilles’ heel for the development of virus therapeutics .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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Disruption of Specific RNA-RNA Interactions in a Double-Stranded RNA Virus Inhibits Genome Packaging and Virus Infectivity
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Enteropathogenic E . coli ( EPEC ) is a human pathogen that causes acute and chronic pediatric diarrhea . The hallmark of EPEC infection is the formation of attaching and effacing ( A/E ) lesions in the intestinal epithelium . Formation of A/E lesions is mediated by genes located on the pathogenicity island locus of enterocyte effacement ( LEE ) , which encode the adhesin intimin , a type III secretion system ( T3SS ) and six effectors , including the essential translocated intimin receptor ( Tir ) . Seventeen additional effectors are encoded by genes located outside the LEE , in insertion elements and prophages . Here , using a stepwise approach , we generated an EPEC mutant lacking the entire effector genes ( EPEC0 ) and intermediate mutants . We show that EPEC0 contains a functional T3SS . An EPEC mutant expressing intimin but lacking all the LEE effectors but Tir ( EPEC1 ) was able to trigger robust actin polymerization in HeLa cells and mucin-producing intestinal LS174T cells . However , EPEC1 was unable to form A/E lesions on human intestinal in vitro organ cultures ( IVOC ) . Screening the intermediate mutants for genes involved in A/E lesion formation on IVOC revealed that strains lacking non-LEE effector/s have a marginal ability to form A/E lesions . Furthermore , we found that Efa1/LifA proteins are important for A/E lesion formation efficiency in EPEC strains lacking multiple effectors . Taken together , these results demonstrate the intricate relationships between T3SS effectors and the essential role non-LEE effectors play in A/E lesion formation on mucosal surfaces .
The gastrointestinal epithelium is an important defense barrier against infections [1] . Enteric pathogens have acquired virulence traits that enable them to colonize and break this barrier , by adhering to the epithelium , delivering toxins and invading intestinal epithelial cells . To this end , several important human and animal pathogens employ type III secretion systems ( T3SS ) to inject virulence factors into infected eukaryotic cells , where they take control of cell signaling [2] . Enteropathogenic E . coli ( EPEC ) and enterohemorrahgic E . coli ( EHEC ) are important human pathogens that colonize the gut mucosa through attaching and effacing ( A/E ) lesions [3] , characterized by intimate bacterial attachment to the apical plasma membrane , localized accumulation of F-actin and effacement of the brush border microvilli [4] . The ability to induce A/E lesions requires the pathogenicity island the locus of enterocyte effacement ( LEE ) [5 , 6] . The LEE encodes gene regulators , the adhesin intimin , chaperones , a filamentous T3SS composed of the translocators proteins ( EspA , EspB and EspD ) , and six effectors ( Tir , EspF , Map , EspG , EspH , and EspZ ) [7] . In HeLa cells , clustering of intimin with its receptor Tir [8] triggers robust actin polymerization leading to formation of pedestal-like structures [4 , 9] . On mucosal surfaces , intimin–Tir interaction is necessary for A/E lesion formation , but it is not currently known if this binding is sufficient [10] . Most LEE effectors , except EspZ , are strong inducers of cytotoxicity , cytoskeleton reorganization , and electrolyte imbalance leading to diarrhea [11 , 12] . Map functions as a Cdc42 GEF ( Guanine nucleotide exchange factor ) , leading to filopodia formation on HeLa cells within minutes after infection [13 , 14]; EspH inhibits the activity of endogenous DH-PH RhoGEFs causing disassembly of focal adhesions ( FAs ) and cell detachment [15 , 16] and EspG interferes with recycling endosomes [17 , 18] . EspZ , which like Tir integrates into the plasma membrane , regulates effector translocation , thus protecting infected cells form cytotoxicity [19] . The prototype EPEC strain E2348/69 also contains 17 effector genes located in integrative elements ( IEs ) and prophages ( PPs ) . These effectors are frequently found in gene clusters , with some effectors having duplicated gene copies and/or paralogs in different clusters [20] . A large proportion of the non-LEE effectors ( e . g . NleB , C , D , E , F and H ) inhibits host inflammation ( [e . g . nuclear factor kappa B ( NF-κB ) ; mitogen-activated protein kinase ( MAPK ) and the non-canonical inflammasome] [12 , 21 , 22] and apoptosis ( e . g . NleB , D and H ) [23] . In particular , NleC is a zinc metalloprotease that degrades the p65 subunit of NF-κB [24] . Deng et al . reported two additional non-LEE effectors , NleJ and LifA/Efa1 [25] . While the function of NleJ is not known , LifA/Efa1 , also called lymphostatin [26] , has a putative glycosyltransferase activity and an important role in intestinal colonization of cattle by EHEC serogroup O5 , O111 , and O26 strains [27–29] , as efa1 mutations dramatically reduced the number of mucosal associated bacteria and fecal shedding . The reason for this apparent attenuation is not known . EPEC is a human restricted pathogen; for this reason human intestinal in vitro organ cultures ( IVOC ) have been used to study early interactions of EPEC with mucosal surfaces [30–33] . Following IVOC infection EPEC triggers A/E lesions that are indistinguishable from those observed in intestinal biopsies of patients with EPEC diarrhea . Using this model it has been shown that while intimin and Tir are essential for colonization , Tir tyrosine phosphorylation is dispensable for A/E lesion formation [10] . However , this infection model has not yet been used to investigate if intimin-Tir interaction is sufficient for A/E lesion formation . The aim of our study is to determine whehter intimin-Tir interaction is sufficient for A/E lesion formation in human IVOC identify further effector ( s ) required for their formation . To this end , we generated an effector-less mutant of E2348/69 strain and a library of intermediate deletion mutants lacking effectors and preserving the correct assembly and function of the T3SS injectisome . This unveiled that EPEC mutants expressing only Tir were unable to produce A/E lesions on IVOC , while able to produce typical actin pedestals on epithelial cells in vitro . In addition , we found that an EPEC mutant lacking all the non-LEE effector genes shows a marginal ability to form A/E lesions on human intestinal IVOC .
We employed a marker-less deletion/replacement strategy to generate a library of EPEC effector mutants , which allow multiple deletions and/or integrations while leaving neither an antibiotic gene cassette nor short heterologous DNA sequences ( "scars" ) in the chromosome [34] . The mutant alleles were designed to delete the coding sequences of effector genes from the start to the stop codon , or in the case of gene clusters and operons , from the start codon of the first open reading frame ( ORF ) to the stop codon of the last ORF , maintaining upstream and downstream sequences containing endogenous regulatory elements ( e . g . promoters , transcriptional terminators ) intact ( S1 Fig ) . We first tested this marker-less deletion strategy by generating an EPEC mutant in escN , encoding the ATPase of the T3SS , whose deletion abrogated secretion of EspA , EspB and EspD in DMEM , but not of EspC autotransporter ( S2 Fig ) . Next , we generated a set of suicide vectors ( pGE and pGETS derivatives ) for the deletion of all the known effectors in E2348/69 ( Table A of S1 Text ) [20 , 25] . We sequentially deleted LEE effector genes map , espG , espF and espH ( Fig 1A ) to obtain the mutant strain called EPEC9 ( Table 1 ) . The LEE effectors espZ and tir were not deleted at this stage , as EspZ , by regulating effector translocation , protects cells from cytotoxicity [19] and Tir , by mediating intimate attachment , enhances protein translocation [35] . Next , we deleted the genes encoding the non-LEE effectors ( Fig 1B ) . The order of deletion followed was: IE5 ( espG2 and espC ) , IE6 ( espL , nleB1 , nleE1 , efa1/lifA ) and IE2 ( espL* , nleB* , nleE2 , efa1/lifA-like ) . Although EspC is not a T3SS effector , we deleted espC together with espG2 in the IE5 because EspC has been reported to be internalized into the host cell in a T3SS-dependent manner [36 , 37] , can interact with translocon proteins [38] , and is known to induce severe cytopathic effects and cell death on epithelial cells [39 , 40] . The resulting effector mutant strains were called EPEC8 , EPEC7 and EPEC6 , respectively ( Table 1 ) . We continued by sequential deletion of the effector genes in PPs: PP2 ( nleH1 , cif* , espJ ) , PP3 ( nleJ ) , PP4 ( nleG , nleB , nleC , nleH* , nleD ) and PP6 ( nleA/espI , nleH2 , nleF , espO* ) ( Fig 1B ) , resulting in a strain we named EPEC2 , which contains EspZ and Tir as the only effectors . We then proceed with the deletion of espZ . However , we found that deletion of the coding sequence of espZ ( ΔespZ-1 , S3A Fig ) , which is the first gene of the LEE2 operon , reduced the secretion of the translocators ( EspA , EspB and EspD ) of the T3SS ( S3B Fig ) . We speculated that abortive translation initiation induced by the RBS of espZ could potentially affect translation of downstream genes in the LEE2 operon . Then , we generated a second mutant allele of espZ that included deletion of its RBS , called ΔespZ-2 ( S3A Fig ) , which did not affect secretion of the translocators ( S3B Fig ) . Using this mutant allele on EPEC2 , we generated the EPEC1 strain that only carries tir . Lastly , we deleted tir in EPEC1 generating the effector-less strain EPEC0 . The steps followed to delete T3 effectors in WT EPEC are summarized in Table B of S1 Text . During generation of each mutant strain , we confirmed the expected deletion by PCR using specific primers ( Table C ) . Confirmation of all deletions in EPEC0 is shown in S4 Fig . In addition , we performed whole-genome sequencing of the parental WT EPEC and EPEC1 . Sequencing reads were assembled both using the reference genomes of EPEC E2349/69 and the in silico designed sequence of EPEC1 , as well as fully assembled de novo from the sequencing reads . Genome comparison between WT EPEC and EPEC1 showed that the only differences between both strains were the designed deletions ( Table D in S1 Text ) . WT EPEC and the effector mutants EPEC2 , EPEC1 and EPEC0 showed identical growth and viability at 37°C in LB and DMEM media ( S5A and S5B Fig , respectively ) . In addition , microscopic analysis of bacteria from these cultures did not show changes in bacterial size or morphology ( S5C Fig ) . To test the functionality of the T3SS , we analyzed the proteins secreted by WT EPEC , EPECΔescN ( negative control ) , and the effector mutant EPEC strains , after 4 h growth in DMEM at 37°C . We found that the translocators EspA , EspB and EspD , which are secreted by the T3SS [41] , accumulated at roughly similar levels in the extracellular media of cultures of WT EPEC and the effector mutant strains ( Fig 2A , top panel ) , but not in the ΔescN negative control . As expected , the autotransporter EspC was absent in the media of strains with deletion of IE5 ( from EPEC8 to EPEC0 ) . The expression of the structural proteins EscC , EscJ , EscD , and the translocator protein EspB , was evaluated by Western blotting in protein extracts of whole bacteria from these cultures . All the effectors mutant strains showed equal expression of the analyzed injectisome proteins compared to WT EPEC ( Fig 2A , bottom panels ) . Detection of cytoplasmic E . coli chaperonin GroEL was used as an internal loading control . Altogether , these experiments demonstrate that the effector mutant EPEC strains are not affected in bacterial growth and express normal levels of T3SS injectisomes able to secrete the translocators . We investigated whether the effector mutants were able to translocate Tir and trigger actin-pedestal formation upon infection of cultured mammalian cells . HeLa cells were infected with WT EPEC and the effector mutants for 1 . 5 h , fixed and stained for immunofluorescence microscopy . All the effector mutant strains , but EPEC0 , triggered actin polymerization upon infection ( Fig 2B ) and form typical microcolonies , indicating the correct expression of bundle forming pili ( BFP ) in these strains [42] . Quantification of the number of cells with actin pedestals in these infections shows similar values for WT , EPEC2 and EPEC1 , with no actin pedestals found in EPEC0 ( Fig 2C ) . To confirm that actin accumulation induced by EPEC2 and EPEC1 was due to intimin-Tir interaction we generated eae ( encoding intimin ) deletion mutants in both strains . Whereas EPEC2Δeae and EPEC1Δeae secreted normal levels of T3SS translocators ( S6A Fig ) , they did not induce actin-pedestals in HeLa cells ( S6B Fig ) . This demonstrates that the actin accumulations observed in HeLa cells infected by EPEC2 and EPEC1 are actual actin-pedestals caused by the specific intimin-mediated clustering of translocated Tir . We quantified the protein translocation levels of the EPEC effector mutants in HeLa cells using β-lactamase ( Bla ) fusions [43] . WT EPEC , EPEC2 , EPEC1 , EPEC0 , and EPECΔescN as negative control , were transformed with plasmid pEspF1-20-Bla , which encodes a fusion between Bla and the N-terminal 20 amino acid signal of the EspF to drive its T3SS-dependent translocation [43 , 44] . WT EPEC harboring pCX340 , encoding Bla without T3 signal , was used as an additional negative control . Whereas no translocation was observed with the control strains , no significant difference in the level of protein translocation was observed from WT and EPEC-2 ( Fig 2D ) . However , EPEC1 , which is devoid of espZ , translocated higher levels of EspF1-20-Bla than WT EPEC and EPEC2 . Conversely , EPEC0 , which lacks intimate adhesion , translocate lower levels of Bla ( Fig 2D ) . These observations are consistent with the reported activities of EspZ and Tir [19 , 35] . We investigated the phenotypes following translocation of selected effectors from the different EPEC mutants . In order to maintain physiological expression levels , we integrated a single copy of the effector gene of interest in its native chromosomal location . We followed the marker-less strategy for gene integration , using suicide vectors with the effector gene and flanking homology regions that preserve genome context and native regulatory elements ( i . e . , promoters , RBS , terminators ) . We integrated individually the effector genes map and nleC , into the chromosome of EPEC2 , EPEC1 and EPEC0 ( Table 1 ) . We tested whether EPEC2 , EPEC1 and EPEC0 expressing Map could produce filopodia early during infection . Swiss 3T3 cells were infected for 10 min with EPEC2 , EPEC1 and EPEC0 and isogenic strains with an integrated copy of map . Actin staining of infected cells revealed the induction of filopodia by the effector mutant EPEC strains carrying map in the vast majority of infected cells , but not in cells infected by their parental strains ( Fig 3 ) . EPEC1+map showed the strongest phenotype of filopodia formation , whereas EPEC0+map induced the weakest phenotype . We next tested whether EPEC2 , EPEC1 and EPEC0 expressing NleC could degrade p65 . HeLa cells were infected for 4h with WT EPEC and effector mutant strains , with or without reintegrated nleC . Western blots of the cell lysates with anti-p65 ( N-terminal ) antibodies revealed that p65 was proteolysed in cells infected with all the EPEC strains expressing nleC ( Fig 4 ) . Proteolysis of p65 in cells infected with the effector mutant strains carrying nleC was higher than that induced by WT EPEC , likely caused by the presence of other effectors in EPEC ( e . g . NleE , NleB ) that inhibit NF-kB activation and the release of free p65 subunit , which is the preferential substrate of NleC [45–47] . Taken together , these results show that the different strains in the effector mutant library contain a functional T3SS , thus allowing us to employ them for infection of human intestinal IVOC . We aimed to determine if intimin–Tir interaction is sufficient for A/E lesion formation on mucosal surfaces . With this in mind , we infected human duodenal biopsies with EPEC1 or EPEC2 . WT EPEC and EPEC0 were used as positive and negative controls , respectively . After 7 h of infection , biopsies were washed , fixed and analyzed by scanning electron microscopy ( SEM ) . Inspection of the mucosal surface revealed A/E lesions in ca . 77% of the biopsies infected with WT EPEC , whereas no A/E lesions were seen in IVOC infected with EPEC2 , EPEC1 or EPEC0 ( Fig 5 and Table 2 ) . In order to control that mucus does not affect the interaction of EPEC2 and EPEC1 with the cells and therefore the formation of A/E lesions , mucus-producing human colonic cells LS174T ( S7 Fig ) were infected with EPEC2 , EPEC1 and EPEC0 . WT EPEC was used as a positive control . This revealed similar adhesion of bacterial microcolonies and formation of actin pedestals by WT , EPEC2 and EPEC1 ( S8 Fig ) . Hence , despite inducing actin pedestals in cultured intestinal epithelial cells in vitro , EPEC1 and EPEC2 could not induce A/E lesions in human intestinal tissue ex vivo , indicating that intimin-Tir interaction is necessary but not sufficient and that additional effectors are needed . As the LEE is universally conserved in clinical EPEC isolates we investigated if effectors encoded on the LEE , other than Tir and EspZ , were required for A/E lesions in human intestinal biopsies . For this , we infected IVOC with a derivative strain of EPEC2 , called EPEC2LEE , in which the genes espG , map , espF , and espH were reintegrated into their original locus on the LEE . Infection with WT EPEC was used as a control . This revealed that EPEC2LEE was severely impaired in its ability to form A/E lesions ( Fig 5 and Table 2 ) , as a single A/E lesion was found in only one of the eleven biopsies infected by EPEC2LEE ( Table 2 ) . No adherent bacteria were seen in all the other 10 IVOCs infected with EPEC2LEE ( Fig 5 ) . In addition , we tested whether EPEC9 , which expresses all non-LEE effectors and misses all LEE effectors except EspZ and Tir , can trigger A/E lesions in IVOC . This revealed that EPEC9 induced A/E lesions in biopsies at a comparable efficiency to WT EPEC ( Fig 5 and Table 2 ) . Taken together , these results indicate that non-LEE effectors are important for A/E lesion formation by EPEC in human intestinal tissue . To investigate the contribution of the non-LEE effectors to A/E lesion formation , we first analyzed the outcome of IVOC infection with EPEC8 , EPEC7 and EPEC6 . These strains are derivatives of EPEC9 having sequential deletions of effectors genes present in IE5 ( espG2 , espC ) , IE6 ( espL , nleB , nleE , efa1/lifA ) and IE2 ( espL* , nleB* , nleE2 , efa1/lifA-like ) ( Table 1; Fig 1 ) . EPEC8 formed A/E lesions at the same frequency as WT and EPEC9 ( Tables 2 and 3 ) . Deletion of IE6 alone ( EPEC7 ) caused a reduction in the frequency of A/E lesions ( 54% ) , however this did not reach significance . Moreover the proportion of biopsies with A/E lesions decreased significantly 23% following infections with EPEC6 ( Table 3 ) . Together this suggest that IE6 and IE2 contribute to A/E lesion formation . Therefore , we investigated the contribution of individual effectors found within IE2 . IE2 carries the pseudogenes espL* and nleB* , as well as the effector genes nleE2 and efa1/lifA-like . Therefore , we generated individual deletions of efa1/lifA-like and nleE2 in EPEC7 ( Table 1 and S9A Fig ) . We confirmed by RT-PCR that deletion of nleE2 has no polar effects on the expression of efa1/lifA-like in IE2 , and viceversa ( S9B Fig ) . Infection of IVOC revealed that EPEC7ΔnleE2 ( carrying a functional copy of efa1/lifA-like ) induced A/E lesion in 64% of the infected biopsies , similar to the parental EPEC7 strain ( Table 3 ) . In contrast , EPEC7Δefa1/lifA-like triggered A/E lesions in 33% of the infected biopsies ( Table 3 ) , similar to EPEC6 . These results show that deletion of efa1/lifA-like in EPEC7 has a significant impact on A/E lesion formation . To further investigate the potential role of LifA-like and LifA in EPEC A/E lesion formation , we generated single ( EPECΔlifA-like and EPECΔlifA ) and double ( EPECΔlifA-like ΔlifA ) deletion mutants in WT EPEC ( Table 1 ) . These mutants secreted normal levels of EspA , EspB and EspD ( S10A Fig ) and produced microcolonies and actin pedestals in HeLa cells similar to the WT strain ( S10B Fig ) . Interestingly , infection of human biopsies showed that A/E lesions were formed at efficiencies similar to the WT strain by ΔlifA-like and ΔlifA single and double mutant strains ( Table 3 and S11 Fig ) . Collectively , these results indicate that non-LEE effectors play a major role for A/E lesion formation on human intestinal tissue ex vivo , and suggest an accessory role of LifA-like and LifA proteins in this process , which is masked in the presence of the entire repertoire of T3SS effectors .
EPEC is a major etiological agent of infant diarrhea [48 , 49] . With the aim of defining the T3SS effectors implicated in A/E lesion formation , we generated a library of mutants missing part or the whole arsenal of effectors present in the prototypical strain E2348/69 . We have demonstrated that the marker-less genome edition strategy generated precise deletions and gene integrations in EPEC . We have built the effector-less EPEC strain ( EPEC0 ) devoid of all known T3SS effectors through 13 deletions , 326 bp was the smallest deletion ( espZ ) and 18260 bp the largest deletion ( IE6 ) . The effector genes were deleted from the start to the stop codon , maintaining their original transcriptional promoters and terminator signals . The only exception was the deletion of espZ , in which deletion of its RBS was necessary to maintain correct expression of the T3SS apparatus . The LEE effector genes espZ and tir were deleted last as they are important to control protein translocation and bacterial attachment to host cells [19 , 35 , 50] . Infection of cultured epithelial cells with the WT EPEC and the effector mutant strains demonstrated the functionality of the T3SS . Infection of HeLa and mucin-producing LS174T cells with EPEC2 ( espZ and tir ) and EPEC1 ( tir ) showed accumulation of F-actin underneath the attached bacteria , confirming that EPEC only needs the effector Tir to induce the actin-pedestals during infection of epithelial cells in vitro . As expected , no pedestals were seen in cells infected with EPEC0 . Protein translocation assays indicated that all effector mutant strains , including EPEC0 , translocate EspF1-20-Bla fusion into HeLa cells , albeit at different efficiency . EPEC1 showed the highest protein translocation level , likely due to absence of EspZ , which limits protein translocation [19] . In contrast , EPEC0 showed the lowest level of protein translocation owing to the absence of intimate adhesion [35 , 50] . We have demonstrated that the EPEC effector mutants provide an excellent tool to study the function of individual effectors , under physiological expression levels , in an infection context as chromosomal single-copy integrations reproduce phenotypes previously reported for Map ( filopodia formation ) and NleC ( NF-kB ) degradation . Importantly , using IVOC our study revealed that intimin–Tir interaction is not sufficient for A/E lesion formation and that other effector ( s ) are needed , as no A/E lesions were observed in biopsies infected with EPEC2 and EPEC1 . Furthermore , infections of IVOC with EPEC2LEE ( lacking all non-LEE effectors ) and EPEC9 ( expressing the whole non-LEE repertoire of effectors plus EspZ and Tir ) , showed that A/E lesion formation requires Tir and EspZ and non-LEE effectors . The difference in effector requirement for intimate adhesion of bacteria in cultured cells ( Tir ) and A/E lesion in intestinal tissue ( Tir+non-LEE ) might be due to a more stringent requirement for a productive interaction of bacteria with a complex tissue surface and/or for the hijack of cellular functions in intestinal tissue . We further characterized the contribution of specific non-LEE effectors to A/E lesion formation by performing IVOC with mutant strains having sequential deletion of non-LEE effector genes . These experiments showed a dramatic reduction of A/E lesion formation when IE6 and , especially , IE2 are deleted ( EPEC6 ) . IE6 ( espL , nleB1 , nleE1 and efa1/lifA ) and IE2 ( espL* , nleB* , nleE2 and efa/lifA-like ) encode a similar set of effectors . We reasoned that either nleE2 or efa1/lifA-like effectors of IE2 should play a role in A/E lesion formation . Albeit NleE2 in IE2 has an internal deletion of 56 residues that could impede its translocation or function [51] , we generated EPEC7ΔlifA-like and EPEC7ΔnleE2 mutants . We found that EPEC7ΔlifA-like strain , but not the EPEC7ΔnleE2 strain , exhibited a reduced efficiency of A/E lesion formation to values close to those of EPEC6 , suggesting that Efa1/LifA-like protein plays a role in A/E lesion formation ex vivo in the effector mutants . Efa-1/LifA-like protein was identified in the genome of EPEC E2348/69 as a homolog with aprox . 30% amino acid identity with Lymphostatin ( LifA ) , encoded in IE6 [20] . The lifA gene , for lymphocyte inhibitory factor A , was first described in EPEC as a chromosomally encoded protein of 365 kDa that inhibits proliferation of lymphocytes and the synthesis of proinflammatory cytokines [26 , 29] . LifA was later shown to be secreted and translocated into mammalian cells in a T3SS-dependent manner [25] . Efa-1/LifA-like homolog is also secreted in a T3-dependent manner by EPEC , but there is no evidence of its translocation into mammalian cells [25] . LifA homologs are found exclusively in the genomes of A/E pathogens[27–29] . Interestingly , efa1/lifA has been found physically linked to the LEE in some EHEC and EPEC strains [52] . In EHEC , EPEC , and C . rodentium , LifA/Efa-1 has been associated to cell adhesion and tissue colonization [28 , 53–55] . In addition , LifA/Efa-1 proteins have been implicated in the induction of intestinal barrier disruption by manipulation of cellular Rho GTPases [56] . While playing a role in A/E lesion formation efficiency , our data show that these proteins are not essential for this process . EPEC6 and EPEC7ΔlifA-like strains still induce A/E lesion formation in 23–33% of infected biopsies ( Table 3 ) . Moreover , EPECΔlifA-likeΔlifA behaves as the WT strain forming actin-pedestals on epithelial cells in vitro and A/E lesions on human intestinal tissue ex vivo . Thus , the efa1/lifA-like proteins have an accessory role in A/E lesion formation , which is masked by other T3SS effectors found in the repertoire of the WT strain . These evidences suggest that Ea1/lifA-like protein could act in the subversion of some cellular functions needed for the establishment of the A/E lesion , but its activity can be exerted by alternative EPEC effectors found in the wild type repertoire . This fact also strengthens our experimental approach in which the role of effectors should be better analyzed in the context of infection with strains expressing a reduced and defined set of effectors , since the WT EPEC strain may have multiple effectors with overlapping , synergistic and/or antagonistic effects . The role and molecular mechanism of Efa/LifA homologs in A/E lesion formation requires further investigation . In summary , our study shows that intimin–Tir is not sufficient for A/E lesion formation in human intestinal mucosal tissue and other effectors are needed . EPEC expressing only the LEE effectors rarely produces A/E lesions , indicating that non-LEE effectors play a major role in this process , having an additive role the effectors encoded in the IE2 , IE6 and PPs .
The EPEC strains used in this work are listed in Table 1 . E . coli K-12 strains used for cloning are listed in Table A of S1 Text . Bacteria were grown in Luria-Bertani ( LB ) liquid medium and agar-plates ( 1 . 5% w/v ) or in Dulbecco's Modified Eagle Medium ( DMEM ) , at 37 oC , unless otherwise indicated . When needed for plasmid or strain selection , antibiotics were added at the following concentrations: ampicillin ( Amp ) at 150 μg/ml for plasmid selection , and at 75 μg/ml for selection of Amp resistance cassette in the chromosome; chloramphenicol ( Cm ) 30 μg/ml; kanamycin ( Km ) 50 μg/ml; tetracycline ( Tc ) 10 μg/ml; spectinomycin ( Sp ) 50 μg/ml . See S1 Text for details . The plasmids employed in this study are listed in Table A of S1 Text . PCRs were performed with the Taq DNA polymerase ( Roche , NZyTech ) for standard amplifications in screenings or with the proof-reading DNA polymerases Herculase II Fusion ( Agilent Technologies ) or Vent DNA polymerase ( NEB ) for cloning purposes . When indicated , DNA was synthesized by GeneArt ( Life Technologies ) . All DNA constructs were confirmed by DNA sequencing ( Secugen and Macrogen ) . Oligonucleotides used in this work were obtained from Sigma and are described in Table C of S1 Text . A summary of genome modifications and construction of EPEC strains are listed in Table B of S1 Text . Site-specific deletions and insertions in the chromosome of EPEC were originated using a marker-less genome edition strategy with I-SceI [34] . The EPEC strain to be modified was initially transformed with a plasmid pACBSR ( CmR ) or its SpR-variant pACBSR-Sp [57] , both expressing the I-SceI and λ-Red proteins under the control of the PBAD promoter . Subsequently , these bacteria were electroporated with the corresponding pGE-based or pGETS- vector ( KmR ) and plated on LB-Km- ( Cm or Sp ) . Selection of individual KmR-cointegrants and their resolution upon induction with L-arabinose for isolation of the strains with mutant alleles are described in detail in the S1 Text . All EPEC strains generated were cured of pACBSR before their analysis by serial passages on LB media lacking antibiotics and selection of Cm- or Sp-sensitive colonies . All EPEC strains were confirmed by PCR with specific primers ( Tables B and C of S1 Text ) . The genomes of EPEC1 and the parental EPEC WT strains were sequenced on an Illumina Miseq platform . Average reads length between 150 and 174 bases and the global coverage was >100X . Genomes were assembled de novo and using reference-guided assemblies with the genome sequence of EPEC O127:H6 strain E2348/69 ( nc_011601 ) and the in silico-designed reference sequence of EPEC1 , as described in the S1 Text . The accession number of the genome sequence of EPEC1 effector mutant strain is <PRJEB18717> , and that of the parental EPEC WT strain E2348/69 is <PRJEB18716> . These genome sequences are available at the 'European Nucleotide Archive' http://www . ebi . ac . uk/ena/data/view/<ACCESSION . NUMBERS> . Sodium Dodecyl Sulfate–Polyacrylamide gel electrophoresis ( SDS-PAGE ) and Western blot were performed as reported previously [58] . Preparation of EPEC protein extracts are described in the S1 Text . For detection of EPEC proteins by Western blotting , membranes were incubated with primary rabbit antibodies anti-EspB ( 1:2000 ) , anti-EscC ( 1:1000 ) , anti-EscJ ( 1:5000 ) , anti-EscD ( 1:1000 ) and anti-Intimin280 ( 1:5000 ) . Use of polyclonal rabbit sera against EPEC Intimin-280 , EscC and EscD were described previously [57 , 59] . Rabbit polyclonal serum against EscJ and EspB was a kind gift of Dr . Bertha González-Pedrajo ( UNAM , Mexico ) . Bound rabbit antibodies were detected with secondary Protein A-peroxidase ( POD ) conjugate ( Life Technologies , 1:5000 ) . GroEL was detected with mAb anti-GroEL-POD conjugate ( 1:5000; Sigma ) . Membranes were developed by chemiluminiscence using the Clarity Western ECL Substrate kit ( Bio-Rad ) . The membranes were then developed by exposure to X-ray films ( Agfa ) or with a Fuji LAS 3000 image when the signal was quantified . Complete description of infection conditions and microscopy is described in the S1 Text . Human HeLa cervix carcinoma cells ( ATCC , CCL-2 ) were grown in DMEM supplemented with 10% heat-inactivated fetal bovine serum ( FBS; Sigma ) and 2 mM glutamine , at 37 oC with 5% CO2 . HeLa cells were washed once with pre-heated serum-free DMEM 2 h before the infection , and infected with EPEC strains for 90 min using a multiplicity of infection ( MOI ) of 200:1 , unless indicated otherwise . Infections were stopped by three washes of sterile PBS ( sigma ) , fixed with 4% ( w/v ) paraformaldehyde ( in PBS , 20 min , RT ) and washed again with PBS . Cells were permeabilized by incubation in a solution of 0 . 1% ( v/v ) of saponin ( Sigma ) in PBS for 10 min and washed with PBS . To stain EPEC strains , bacteria were incubated with polyclonal rabbit anti-intimin280 ( 1:500 ) , or anti-O127 ( 1:100 ) for Δeae mutants , and goat anti-rabbit secondary antibodies conjugated to Alexa488 ( 1:500 , Life technologies ) in PBS with 10% goat serum; along with Phalloidin TRITC ( 1:500; Sigma ) and 4' , 6-diamidino-2-phenylindole DAPI ( 1:1000; Sigma ) to label F-actin and DNA and mounted with 4 μl of ProLong Gold anti-fade reagent ( Life technologies ) . To analyze translocation of NleC , HeLa cells were infected 1 h and then washed three times with PBS and incubated for additional 3 h with 200 μg/ml of gentamicin in DMEM . Cells were washed with PBS to remove unbound bacteria and cellular protein extracts were prepared analyzed by Western blot . To analyze filopodia formation , infection with EPEC strain was done in Swiss 3T3 mouse fibroblasts ( ATCC; CCL-92 ) cells grown in DMEM-high glucose ( D5671; Sigma ) supplemented with 10% of heat-inactivated fetal calf serum ( FCS; Sigma ) , 2 mM glutamine and 1X of MEM non-essential amino acid solution 100X ( Sigma ) . Swiss 3T3 cells were washed three times with sterile pre-warmed PBS ( Sigma ) and serum-free DMEM 2 h previous to the infection . Infections were done with 500 μl of EPEC cultures grown in DMEM during 3 h ( aprox . MOI 500:1 ) . The plates were centrifuged to synchronize the infection ( 500xg , 5 min , in a rotor pre-warmed at 37 oC ) and the infection was continued for additional 5 min . Infections were stopped by three washes with sterile PBS ( Sigma ) , fixed with 4% ( w/v ) paraformaldehyde ( in PBS , 20 min , RT ) and washed with PBS . Fixed monolayers were incubated with polyclonal rabbit anti-O127 ( 1:100 ) and secondary donkey anti-rabbit-Alexa488 ( Jackson ImmunoResearch , 1:100 ) , together with Oregon-green Phalloidin ( 1:100 , Invitrogen ) to label bacteria and actin respectively . Coverslips were washed 3 times with PBS after incubation and mounted with ProLong Gold anti-fade reagent ( Life technologies ) . LS174T colon adenocarcinoma cells ( ECACC 87060401 ) were grown in DMEM supplemented with 10% heat-inactivated fetal bovine serum ( FBS; Sigma ) , 2 mM glutamine and 1X of non-essential amino acids ( Sigma ) at 37 oC with 5% CO2 . LS174T cells were washed three times with pre-heated PBS ( Sigma ) 2 h before the infection . The cells were infected with 200 μl ( MOI ca . 200:1 ) for 90 min with EPEC grown in DMEM at 37 oC as described for HeLa cells infections . Following washes with PBS ( Sigma ) , the cells were fixed with 4% ( w/v ) paraformaldehyde ( in PBS , 20 min , RT ) , washed again with PBS and permeabilized with 0 . 1% of Triton X-100 ( Sigma ) in PBS for 10 min . The immunofluorescence staining of intimin , F-actin and cell nuclei was done described previously for infections of HeLa cells . Mucin produced by LS174T cells was stained with an anti-MUC2 rabbit-polyclonal antibody ( 1:250 Santa Cruz biotechnology ) and goat anti-rabbit IgG conjugated to Alexa488 ( 1:500 , Life technologies ) as secondary Ab . β-lactamase ( Bla ) translocation was quantified as reported previously [43 , 44] using LiveBLAzer FRET-B/G Loading Kit with CCF2-AM ( ThermoFisher Scientific ) . Plates were read in a SpectraMax M2 fluorometer ( Molecular Devices ) with a filter set 450/520 nm . See S1 Text for details . This study was performed with approval from the University of East Anglia Faculty of Medicine and Health Ethics Committee ( ref 2010/11-030 ) . All samples were registered with the Norwich Biorepository ( NRES ref 08/h0304/85+5 ) . Biopsy samples from the second part of the duodenum were obtained with informed consent during upper endoscopy of adult patients at the Norfolk and Norwich University Hospital . All samples were anonymized . Up to 6 biopsy samples per donor were taken from macroscopically normal areas . Samples were cut in half and infected with EPEC wildtype and mutant strains in duplicate . Each bacterial strain was examined in human IVOC on at least three occasions using tissues from different donors . IVOC was performed as described previously [30 , 60] . Briefly , biopsies were mounted on foam supports in 12 well plates and incubated with 25 μl standing overnight culture ( approximately 107 bacteria ) . Samples were incubated for 7 h on a rocking platform at 37°C in a 5% CO2 atmosphere . At the end of the experiment , tissues were fixed in 2 . 5% glutaraldehyde in PBS , dehydrated through a graded acetone series , and dried using tetramethylsilane ( Sigma ) . Samples were blinded and examined in a scanning electron microscope ( Jeol JSM-6390 ) . Biopsies showing at least one A/E lesion were scored as positive . RNA was extracted from the EPEC strains and reversed transcribed by RT-PCR as described previously [61] . The primers used for the RT-PCR of lifA-like , nleE2 and tir are listed in Table C of S1 Text as 108 to 113 . Mean and standard errors of experimental values were calculated with using Prism 5 . 0 ( GraphPad software Inc ) . Statistical analyses comparing the mean of paired experimental groups were conducted with Student's t-test using Prism 5 . 0 ( GraphPad software Inc ) . Statistical analyses comparing the number of A/E-positive and negative biopsies after infection with the indicated EPEC strains were conducted with Fisher's exact test to determine two-tailed P values using Prism 5 . 0 ( GraphPad software Inc ) . Data were considered significantly different when p-values <0 . 05 .
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Enteropathogenic E . coli ( EPEC ) causes diarrhea and generates the attaching and effacing ( A/E ) lesion in human gut epithelium . A/E lesion formation requires the locus of enterocyte effacement ( LEE ) in the bacterial genome , which encodes a protein injection system delivering the translocated intimin receptor ( Tir ) , which binds to intimin on the bacterial surface . Intimin-Tir interaction is sufficient for bacterial attachment to epithelial cells in vitro but additional effectors may be needed for A/E lesion formation in the human gut . By generating deletion mutants lacking combinations or the whole repertoire of protein effectors encoded by EPEC , we show that intimin-Tir interaction is not sufficient and reveal an additive role of non-LEE effectors for A/E lesion formation in human intestinal tissue .
|
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2017
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Attaching and effacing (A/E) lesion formation by enteropathogenic E. coli on human intestinal mucosa is dependent on non-LEE effectors
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Identifying regulatory mechanisms that influence inflammation in metabolic tissues is critical for developing novel metabolic disease treatments . Here , we investigated the role of microRNA-146a ( miR-146a ) during diet-induced obesity in mice . miR-146a is reduced in obese and type 2 diabetic patients and our results reveal that miR-146a-/- mice fed a high-fat diet ( HFD ) have exaggerated weight gain , increased adiposity , hepatosteatosis , and dysregulated blood glucose levels compared to wild-type controls . Pro-inflammatory genes and NF-κB activation increase in miR-146a-/- mice , indicating a role for this miRNA in regulating inflammatory pathways . RNA-sequencing of adipose tissue macrophages demonstrated a role for miR-146a in regulating both inflammation and cellular metabolism , including the mTOR pathway , during obesity . Further , we demonstrate that miR-146a regulates inflammation , cellular respiration and glycolysis in macrophages through a mechanism involving its direct target Traf6 . Finally , we found that administration of rapamycin , an inhibitor of mTOR , was able to rescue the obesity phenotype in miR-146a-/- mice . Altogether , our study provides evidence that miR-146a represses inflammation and diet-induced obesity and regulates metabolic processes at the cellular and organismal levels , demonstrating how the combination of diet and miRNA genetics influences obesity and diabetic phenotypes .
Obesity incidence has reached epidemic rates in recent years , now affecting over one-third of the adult population in the United States and rising in incidence around the world [1] . Obesity and its comorbidities , including diabetes , heart disease , stroke , cancer , and infections , account for the second leading cause of preventable death in the United States , placing a large burden on the healthcare system and diminishing the health and life expectancy of affected individuals [2 , 3] . Chronic , low-grade inflammation and metabolic dysregulation are at the center of obesity pathogenesis and progression , but the mechanisms underlying this dysregulation are not fully understood . Insights into how inflammation and metabolism contribute to obesity could aid in developing therapies and combating further spread of this condition . Among regulators of metabolic pathways and inflammation are microRNAs ( miRNAs ) . miRNAs are ~22 nucleotide noncoding RNAs that post-transcriptionally regulate mRNA targets , thus inhibiting protein expression . Roles for miRNAs in inflammation and immunity have been widely studied , and many particular miRNAs are essential for proper immune system function [4–6] . One such miRNA , miR-146a , is induced during inflammatory responses and acts to dampen inflammation in multiple contexts , including cancer [7–9] , autoimmunity [10 , 11] , immunization [12] , and intestinal homeostasis [13] . Importantly , miR-146a has been shown to function in human adipose tissue during inflammation [14] , and its expression is reduced in obese and type 2 diabetic ( T2D ) patients [15–19] , suggesting a role for miR-146a in obesity . Here , we show that genetic deletion of miR-146a in mice results in obesity , fatty liver disease , and dysregulated glucose levels during diet-induced obesity ( DIO ) . These mice exhibit elevated inflammation in metabolic tissues both before and during metabolic disease . We found that miR-146a is expressed within adipose tissue and regulates the accumulation of inflammatory foci that resemble crown-like structures during a high-fat diet ( HFD ) . Further , miR-146a is highly expressed in the stromal vascular fraction ( SVF ) of the adipose tissue , suggesting a role for miR-146a within immune cells within this compartment . RNA-sequencing analysis revealed that miR-146a regulates not only inflammatory pathways , but also metabolic pathways including mTOR in adipose tissue macrophages ( ATMs ) , a role not previously identified . Further assessment found that miR-146a regulates both the inflammatory response and metabolic state of macrophages through a mechanism involving repression of its target , Traf6 . Finally , we found that inhibition of mTOR using rapamycin was able to reverse the obesity phenotype in miR-146a-/- mice . Altogether , these data indicate that miR-146a functions to limit diet-induced metabolic disease in mice .
In order to study the role of miR-146a in obesity and metabolic disease , we fed miR-146a-/- and C57BL/6 WT mice HFD . Over time , miR-146a-/- mice gained significantly more body weight than WT controls as a measure of percent initial weight ( Fig 1a , S1a Fig ) as well as weight in grams ( Fig 1b ) . NMR body composition analysis revealed that miR-146a-/- mice gained significantly more body fat than WT mice , with a body composition of ~40% body fat by week 12 of the HFD compared to ~20% fat for WT ( Fig 1c ) . We observed no significant difference in weight gain or fat accumulation between WT and miR-146a-/- mice fed a normal chow diet ( NCD ) ( S1b–S1d Fig ) . Further indicative of obesity and metabolic dysregulation , miR-146a-/- mice on HFD developed elevated serum Leptin protein levels ( S1e Fig ) . Increased weight gain was seen in both male and female miR-146a-/- mice ( S1f Fig ) . To account for possible colony-specific microbiota differences and as further confirmation , miR-146a-/- mice from Jackson Labs also showed increased weight gain compared to WT Jackson C57BL/6 controls ( S1g and S1h Fig ) . The significant weight and fat gain by miR-146a-/- HFD mice was not caused by an increase in food consumption , as the groups had similar food intake over the course of the diet ( Fig 1d and S1i Fig ) . Upon histological examination of the adipose tissue , miR-146a-/- mice fed HFD displayed varying degrees of hypertrophied adipocytes compared to WT mice [20] ( Fig 1e , left ) . Liver histological analysis showed steatosis in miR-146a-/- HFD mice that was not apparent in WT mice ( Fig 1e , right ) . Further , liver histology scores , which measure portal inflammation and steatosis as previously outlined [21] , revealed that miR-146a-/- HFD mice had significantly increased liver histology scores compared with WT controls ( Fig 1f ) , indicating miR-146a-/- HFD mice have worsened metabolic disease in the liver . Visceral white adipose tissue ( VAT ) pads from miR-146a-/- mice on HFD weighed significantly more than WT VAT pads ( Fig 1g ) . Mice were placed in metabolic chambers at 0 , 3 , or 18 weeks during HFD to measure phenotypic parameters before and during onset of obesity . At 18 weeks HFD , miR-146a-/- mice showed a decreased respiratory exchange ratio compared with WT mice , while minimal differences were observed at earlier time points ( Fig 1h ) , indicating that miR-146a-/- mice began using more fat for energy later in the time course . No difference in movement was observed at 0 or 3 weeks HFD , but by 18 weeks HFD miR-146a-/- mice displayed significantly decreased movement compared to WT controls likely a result of their increased adiposity and weight gain ( Fig 1i ) . Although subtle , altered energy expenditure further suggested metabolic dysregulation ( Fig 1j ) . To determine whether brown adipose tissue ( BAT ) played a role , we removed BAT from young , untreated animals and determined gene expression for BAT activation genes ( S2a Fig ) , lipogenesis genes ( S2b Fig ) , and inflammatory immune genes ( S2c Fig ) . We did not find significant differences between WT and miR-146a-/- BAT tissue in terms of gene expression , while the removed BAT tissue weighed the same between the two groups ( S2d Fig ) . We also determined that miR-146a was expressed in BAT tissue from WT mice and absent in BAT from miR-146a-/- mice ( S2e Fig ) . Following HFD , we again assessed expression of relevant genes and saw no significant differences between WT and miR-146a-/- ( S2f Fig ) . These data suggest that mouse BAT function is not playing a major role in the phenotype . Taken together , these data indicate that the combination of a miR-146a deficiency and HFD work together to exacerbate weight gain and metabolic disease in this model of DIO . Obesity is closely associated with glucose dysregulation , and recent reports found decreased miR-146a levels in PBMCs and serum of patients with T2D [15 , 16 , 18] . Thus , we examined glucose homeostasis in miR-146a-/- mice during DIO . miR-146a-/- mice fed HFD displayed higher blood glucose during glucose tolerance tests ( GTTs ) than WT controls , or than either group on NCD ( S3a and S3b Fig ) . GTTs were performed on fasted WT and miR-146a-/- mice at 0 , 3 , or 18 weeks HFD to determine if glucose phenotypes precede or succeed obesity in miR-146a-/- mice . Results indicate little difference between WT and miR-146a-/- mice in blood glucose at 0 and 3 weeks of HFD ( Fig 2a and 2b ) . However , after 18 weeks , miR-146a-/- mice had significantly higher blood glucose levels than WT mice at resting and during GTT , with an increase in the GTT area under the curve ( Fig 2a and 2b ) . These data indicate that miR-146a-/- mice are glucose tolerant when on NCD and during early stages of DIO but lose the ability to properly regulate glucose after adiposity has begun . miR-146a-/- and WT mice had comparable fasting serum insulin levels at 0 and 3 weeks HFD , yet miR-146a-/- mice had notably higher levels of serum insulin at 18 weeks HFD ( Fig 2c ) , revealing that miR-146a-/- mice still make insulin . Increased insulin levels can indicate insulin resistance , as hyperinsulinemia is a common symptom in T2D patients [22] . HOMA-β and HOMA-IR , which are based on fasting blood glucose and insulin levels and measure pancreatic beta cell function and insulin resistance , respectively , were calculated . Consistent with our observation that miR-146a-/- mice can still produce insulin , we saw no difference in HOMA-β function between WT and miR-146a-/- mice ( Fig 2d ) ; additionally , pancreatic architecture of HFD-fed WT and miR-146a-/- mice appeared normal upon H&E staining ( S3c Fig ) . On the other hand , HOMA-IR was increased in the miR-146a-/- HFD group at 18 weeks , demonstrating that miR-146a-/- mice develop insulin resistance following DIO ( Fig 2e ) . Taken together , these data show that miR-146a is required in mice to prevent development of a T2D phenotype during DIO . miR-146a has previously been shown to reduce inflammation in several contexts [7 , 14]; therefore , we hypothesized that it may regulate inflammatory gene expression within metabolic tissues , including VAT and liver . To determine whether gene expression changes were due to HFD , inflammatory gene expression was measured via qPCR in VAT and liver tissue from WT and miR-146a-/- mice on NCD or HFD . Prior to HFD , miR-146a-/- mice had increased pro-inflammatory gene expression in VAT ( Fig 3a ) and liver ( Fig 3d ) compared to WT controls . These differences were also observed in miR-146a-/- controls on NCD for 14 weeks , where inflammatory and fatty acid transporter genes were higher in miR-146a-/- adipose tissue ( Fig 3b ) and liver ( Fig 3e ) . In miR-146a-/- mice fed HFD for 14 weeks , we observed greater increases in a variety of inflammatory genes ( Fig 3c and 3f ) from miR-146a-/- compared with WT mice . Additionally , VAT from miR-146a-/- mice had increased NFκB activation , as measured by phosphorylated IKBα which serves as a surrogate for measuring NF-κB activation [23] , and this was further increased by HFD ( Fig 3g ) . These data indicate that inflammation in miR-146a-/- metabolic tissues occurs in the absence of HFD and likely predisposes mice to obesity and glucose dysregulation in response to elevated fat in the diet . DIO further activates inflammation in miR-146a-/- mice , as the fold change in pro-inflammatory gene expression was further increased during HFD . Blinded histological examination of H&E-stained VAT revealed greater accumulation of inflammatory foci resembling crown-like structures in miR-146a-/- than in WT mice following HFD ( Fig 3h ) , indicating increased immune cell activation in VAT during DIO . miR-146a has known roles within immune cells , and immune cells in VAT cause inflammation that drives metabolic disease; thus , we hypothesized that the absence of miR-146a from immune cells may exacerbates metabolic disease . To assess expression of miR-146a within certain adipose-associated cell types , we separated the SVF , which contains ATMs , fibroblasts , preadipocytes , and other leukocytes , from the adipocyte fraction of WT and miR-146a-/- VAT . qPCR was performed on RNA from these fractions , revealing mature miR-146a more highly expressed in SVF than in the adipocyte fraction of WT mice as hypothesized ( Fig 3i ) . As a control , we measured mature miR-146a in the SVF and adipocyte fractions of miR-146a-/- samples where it was not detected ( Fig 3i ) . Specific expression of Ptprc ( CD45 ) in the SVF and Leptin in the adipocyte fraction ensured proper separation of these fractions ( Fig 3j and 3k ) . These results indicate that miR-146a is more abundant within the SVF of VAT than adipocytes , suggesting that miR-146a exerts its regulatory function within adipose tissue immune cells . However , miR-146a might also be expressed and function in adipocyte progenitors or other non-immune cells and this could also contribute to the observed phenotypes . We previously showed that some age-related chronic inflammatory phenotypes exhibited by loss of miR-146a are dependent upon miR-155 , an inflammation-promoting miRNA [8 , 10] whose deletion in mice has been shown to reduce DIO [24] . We thus wanted to assess whether the metabolic disease and inflammatory phenotypes in miR-146a-/- mice during DIO were also dependent on miR-155 . To test this , we fed HFD to WT , miR-155-/- , miR-146a-/- , and miR-155-/- miR-146a-/- ( double knockout or DKO ) mice for a period of 12 weeks and measured weight gain over time . miR-155-/- and WT mice gained similar weight in response to HFD , while miR-146a-/- and DKO mice both gained significantly more weight than either WT or miR-155-/- ( S4a and S4b Fig ) . Fasting blood glucose followed this pattern , with DKO and 146a-/- mice having similar levels to each other , both higher than WT or miR-155-/- ( S4c Fig ) . Further , miR-146a-/- and DKO mice had larger VAT pads ( S4d Fig ) , a higher percent body fat ( S4e Fig ) , and reduced lean body composition ( S4f Fig ) compared to WT or miR-155-/- mice . As evidenced by DKO mice showing no significant differences from the miR-146a-/- mice , these results demonstrate that miR-155 does not play a significant role in driving the obesity and metabolic disease phenotypes observed in miR-146a-/- mice on HFD . Because macrophage hallmark genes are at higher levels in VAT of miR-146a-/- mice fed HFD ( Fig 3a–3c and 3h ) , and macrophages have been shown to regulate metabolic disease in adipose tissue [25] , we next determined which genes and pathways are affected by miR-146a in ATMs . To identify the genes regulated by miR-146a in adipose tissue in vivo , we isolated ATMs from adult WT and miR-146a-/- mice fed NCD or HFD . VAT was digested , and CD45+ CD11b+ F4/80+ cells were sorted using fluorescence activated cell sorting ( FACS ) . A comparable proportion of macrophages was sorted from NCD animals of both genotypes , while a trending increase in ATM percentage was measured in miR-146a-/- mice on HFD compared to WT controls ( Fig 4a and S5a Fig ) . The total number of macrophages sorted was marginally increased in miR-146a-/- compared to WT mice on NCD , but not different between mice on HFD ( S5b Fig ) . We also measured VAT B and T cells and saw no difference between the genotypes ( S5c Fig ) . Next , RNA-sequencing using ATM RNA was performed and we saw that gene expression clustered by genotypes and diet treatments , indicating that genes are regulated by miR-146a on its own and in combination with specific diets ( Fig 4b ) . To determine the types of genes regulated by miR-146a in ATMs during NCD and HFD we performed both an Ingenuity Pathway Analysis and a Gene Set Enrichment Analysis using our ATM RNA-Seq data . Among significant hits for gene sets enriched in miR-146a-/- macrophages were inflammatory pathways including interferon gamma and alpha , glycolysis and hypoxia , IL6/Jak/Stat3 signaling , and complement ( S5d and S5e Fig ) . Of note , the top two pathways enriched in miR-146a-/- HFD macrophages were ‘TNFa signaling via NFkB’ and ‘Inflammatory Response’ ( Fig 4d and S5d Fig ) ; interestingly , ‘Inflammatory Response’ was also enhanced in miR-146a-/- NCD cells ( Fig 4c and S5e Fig ) . Inflammatory response genes upregulated in miR-146a-/- HFD ATMs ( Fig 4g ) indicate that miR-146a regulates inflammatory ATM pathways in both lean ( NCD ) and obese ( HFD ) adipose tissue , corroborating our finding that inflammatory cytokine expression is elevated in miR-146a-/- mice regardless of diet . The PI3K/AKT/mTOR and mTORC1 signaling gene sets were enriched in miR-146a-/- HFD macrophages , which was not seen in ATMs from mice on NCD ( Fig 4e and 4f and S5d Fig ) . Genes from these enriched gene sets ( Fig 4h ) indicate a novel role for miR-146a in regulating the mTOR/AKT pathway which has previously been linked to cellular metabolism and obesity . Interestingly , the Reactive Oxygen Species gene set was upregulated in miR-146a-/- macrophages during HFD ( Fig 4i and S5d Fig ) . Increased reactive oxygen species are important for effective inflammatory responses and are produced in LPS-activated macrophages which undergo a metabolic switch to downregulate oxidative phosphorylation and increase aerobic glycolysis [26] . Consistent with this , the glycolysis gene set was also enriched ( S5d Fig ) . Altogether , these data suggest that miR-146a regulates gene expression in ATMs , particularly of the inflammatory response as well as a unique subset of metabolic pathways during DIO . To further understand the mechanism by which miR-146a regulates metabolism and inflammation in ATMs , we analyzed the expression levels of predicted miR-146a targets from Targetscan in our RNA-seq dataset . Among the relevant predicted targets , Traf6 mRNA expression was significantly elevated in the miR-146a-/- HFD ATMs compared to WT ( Fig 5a ) . Traf6 is a bona fide miR-146a target in several inflammatory contexts [7 , 27] making it a viable option for further analysis in our system . Traf6 protein levels were increased in miR-146a-/- bone marrow-derived macrophages ( BMDMs ) stimulated with LPS compared to WT controls ( Fig 5b ) . To further investigate Traf6 as the miR-146a target regulating inflammation in our model , we developed Traf6 mutant Raw264 . 7 macrophages using Crispr-Cas9 ( Traf6 Cr ) to disrupt expression of this protein ( Fig 5c ) . Upon deletion of Traf6 , we observed reduced LPS-mediated inflammatory cytokine expression , consistent with miR-146a dampening these inflammatory genes by downregulating Traf6 ( Fig 5d ) . Additionally , we used a previously characterized Traf6 peptide inhibitor ( Traf6i ) to reduce Traf6 activity in primary miR-146a-/- BMDMs and observed reduced inflammatory gene expression in response to LPS ( Fig 5e ) [28] . These data support a model whereby miR-146a regulates adipose inflammation at least in part by targeting Traf6 in ATMs . RNA-seq also suggested an altered metabolic state in miR-146a-/- ATMs specifically when placed on HFD . This was indicated by increases in gene sets for PI3K/AKT/mTOR signaling , glycolysis , and reactive oxygen species ( Fig 4f and 4h–4i and S5d Fig ) . Therefore , we examined the role of miR-146a in regulating metabolism in activated BMDMs . First , we assessed expression of glycolysis genes including HK2 and HIF1a and found that they were induced to higher levels in LPS-treated miR-146a-/- vs . WT control BMDMs , but not when miR-146a-/- BMDMs were treated with a Traf6i ( Fig 5f ) . We also measured oxidative phosphorylation in LPS-activated miR-146a-/- BMDMs . Throughout a Mito Stress Test , miR-146a-/- BMDMs stimulated with LPS had low oxygen consumption rates ( OCR ) as compared with WT controls and media-treated cells , indicating lower oxidative respiration and little responsiveness to added glucose or to mitochondrial respiration inhibitors oligomycin ( OM ) , FCCP , or Rotenone/Antimycin A ( Fig 5g ) . Additionally , Traf6 mutant cells exhibited greater OCR when compared with WT empty vector controls ( Fig 5h ) . These data are consistent with previous studies showing that pro-inflammatory M1 macrophages undergo a metabolic switch involving increased aerobic glycolysis and decreased oxidative phosphorylation . These results suggest a role for miR-146a in restricting this metabolic alteration in activated macrophages . Altogether , these data suggest that miR-146a regulates macrophage activation by influencing both inflammatory response genes and metabolic state through targeting of Traf6 . Having identified a novel role for miR-146a in regulating macrophage metabolism , including evidence for increased activation of the Akt/mTOR pathway , we tested whether administration of rapamycin , an inhibitor of mTOR activation , would reverse the miR-146a-/- DIO phenotypes . WT and miR-146a-/- mice on HFD were injected intraperitoneally with rapamycin 3 times per week for the last 10 weeks of a 14 week HFD experiment . Consistent with a role for elevated mTOR as a driver of DIO , miR-146a-/- mice given rapamycin put on significantly less weight than miR-146a-/- mice given the vehicle control , and resembled WT control mice in terms of their weight , percent body fat and VAT pad size ( Fig 6a , 6c and 6d ) , with no differences in food consumption ( Fig 6b ) . These results support a model whereby increased mTOR activation in miR-146a-/- macrophages , and possibly other cell types , supports increased adiposity in response to a high fat diet .
Obesity is a global problem , with an estimated 600 million individuals affected worldwide [29] . Understanding its pathogenesis is crucial for developing therapies and finding a cure . Our study demonstrates a physiologically relevant role for miR-146a in this context , where miR-146a is required for protection from obesity and metabolic disease during HFD and loss of miR-146a causes underlying inflammation that acts as a predisposing factor for DIO . This underlying inflammation , however , does not trigger metabolic disease on its own in our model . Although we cannot rule out a possible role for miR-146a in regulating certain aspects of metabolism in the presence of a normal diet , most metabolic phenotypes analyzed in this study only showed robust differences in the context of HFD . Thus , the combination of a miR-146a deficiency together with HFD triggers the onset of obesity and metabolic disease . This is likely caused by pro-inflammatory adipokines expressed during caloric excess that act in concert with inflammatory pathways to signal a need for increased fat storage . Importantly , our results suggest that obesity onset can be driven by a combination of miRNA genetics and diet , a finding which could have clinical relevance . miR-146a is highly expressed within the SVF of adipose tissue which is rich in leukocytes , suggesting a role for this miRNA within immune cells . Based on the miR-155 data presented above , we speculate that our miR-146a-/- mouse obesity and metabolic disease phenotype might be T cell-independent as our previous model revealed a T cell-intrinsic role for miR155 during age-dependent chronic inflammation or antitumor immunity upon loss of miR-146a [8 , 10] . Rather , miR-146a has been shown in previous studies to be important within myeloid cells [7] , and our findings suggest that ATMs are at least one cell type where this miRNA may act to protect against inflammation and DIO . However , other cell types also express miR-146a and could play a role , such as B or T lymphocytes or preadipocytes , which are also found in the SVF . miR-146a has been shown to function in each of these cell types in other contexts [8 , 10–12] , and future work utilizing conditional knockout animals to characterize the cell-specific roles of miR-146a will further contribute to our understanding of miR-146a’s role in obesity development and progression . miR-146a is well-known for regulating genes that are part of the inflammatory response , which we have shown to be dysregulated during DIO; however , other regulatory functions of miR-146a are not well-studied and could be key to understanding its role in disease as well as its therapeutic potential . Here , we found that miR-146a regulates the metabolism of activated macrophages , a previously unrecognized function for miR-146a . We saw that activated miR-146a-/- macrophages undergo metabolic reprogramming characterized by decreased oxidative phosphorylation , and this phenotype could be rescued in miR-146-/- macrophages using a Traf6 peptide inhibitor and was the opposite in Traf6-deficient macrophages . In addition to decreased OCRs observed in miR-146a-/- macrophages , the glycolysis gene set was significantly enriched in our RNA sequencing data , and we saw increased expression of glycolysis genes by qPCR which could again be rescued with the Traf6 inhibitor . This suggests that miR-146a represses Traf6 not only to control inflammatory gene expression , but also to limit the switch from oxidative phosphorylation to glycolytic metabolism during inflammation [30 , 31] . Our GSEA also indicated increased mTOR activation , which has been shown to regulate both oxidative phosphorylation and glycolysis [32] . Of significance , we found that blocking mTOR in vivo using rapamycin could reverse the increased adiposity observed in miR-146a-/- mice on HFD . Although miR-146a has been shown to have many direct targets , we found Traf6 to be de-repressed in miR-146a-/- ATMs . There is evidence that Traf6 is involved in activating Akt and mTOR [33 , 34] which may explain how these pathways are impacted by miR-146a . This may also be a more complex mechanism , as Traf6 itself localizes to the mitochondria during TLR activation , a process that is required for activation of inflammation and ROS production by macrophages [35] . Further work will be needed to understand the pathways and mechanisms through which miR-146a and Traf6 regulate macrophage metabolism during DIO . Altogether , our results point to a physiologically relevant role for miR-146a in protecting against DIO . Recently , direct targeting of miRNAs has been shown to have therapeutic success in disease treatment [36] , and miRNA mimics are currently being investigated for their therapeutic potential [37] . Human studies have shown that miR-146a expression is decreased in individuals with obesity and T2D [15 , 16] . This observation , combined with our current study , suggests that utilizing miRNA mimics to enhance miR-146a levels may have therapeutic potential for treating obesity , diabetes , and other forms of metabolic inflammation and disease [15] . Overall , our study provides evidence that the combination of diet and miRNA genetics can have a substantial impact on obesity and diabetic phenotypes in mammals .
Experimental procedures were performed with the approval of the Institutional Animal Care and Use Committee ( IACUC ) of University of Utah , USA , approval number: 17–03006 . Group sizes of five to ten mice were used consistent with previous publications carrying out similar experiments . Data collection was stopped according to previous studies , which measure DIO over the course of several months . Experiments were carried out beyond preliminary results from initial experiments , which showed that miR-146a-/- mice develop statistically significant weight changes and other metabolic differences after 5–6 weeks of HFD . Data were included given that experiments were carried out accurately according to the design . Mice were excluded from studies if they showed signs of illness or dishevelment during experimentation; however , none of these issues were observed . Outliers were removed according to the “identify outliers” tool on Graphpad Prism 7 software , in which data points at least two standard deviations from the mean are removed . Outliers removed in the study have been reported . Endpoints for HFD were selected prospectively , in that data were collected from mice before treatment , during an early timepoint , and following induction of obesity . Diabetes and other metabolic measurements were taken following induction of DIO at later timepoints of disease . HFD experiments were performed in female mice four times , each with at least five experimental replicates to account for biological variability . Additionally , experiments were performed in male mice to account for possible sex differences . Tissues were collected from mice for various downstream assays in three of the four experiments with females . In vitro studies were performed at least 2 independent times , or utilized cells from multiple mice per group . The purpose of this study was to determine the role of miR-146a during diet induced obesity , diabetes , and metabolic inflammation . Before experiments , we hypothesized that feeding miR-146a-/- mice HFD will result in increased obesity and diabetic phenotypes compared with WT controls because of miR-146a’s anti-inflammatory regulation of NF-kB , leading to increased inflammation within metabolic and immunologic tissues . Experiments were performed on C57BL6 miR-146a-/- and WT mice described previously [11] . Additionally , BMDMs derived from WT and miR-146a-/- mice and the macrophage cell line Raw264 . 7 were utilized for in vitro work . Studies performed were controlled laboratory experiments . WT and miR-146a-/- mice were fed NCD or HFD for up to 18 weeks and weighed throughout the experiment . Food consumption was measured each week . Body composition and other metabolic parameters were measured using NMR body composition analysis and CLAMS metabolic cages . Fasting blood glucose was measured by fasting mice for 6 hours and reading blood glucose levels . Glucose tolerance tests were performed by injecting mice with glucose and measuring blood glucose levels at timepoints over 120 minutes . After up to 18 weeks on HFD , mice were sacrificed and tissues collected . Measurements included RNA and protein quantification from liver and VAT . WT and miR-146a-/- BMDMs macrophages were cultured , stimulated with LPS , and collected for RNA and protein levels . Additionally , mito-stress Seahorse tests were performed on LPS-stimulated macrophages . Mice were age and sex-matched between WT and miR-146a-/- . Mice were randomly selected from various breeder pairs , and multiple experimental repeats were performed over the course of several years on both males and females to account for environmental differences . Mice were also purchased from Jackson Laboratories and HFD experiments repeated to account for possible varied animal facility environments . HFD experiments were not conducted in a blinded manner , but data were analyzed to ensure there was a blinded assessment of outcomes . Of note , measurements such as weight gain are quantitative and therefore limit data collection bias . The investigators were aware which mice were WT and miR-146a-/- mice , as the animal facility requires genotypes to be posted on cages . Histological sections of tissue were scored blindly by a trained pathologist ( Mary Bronner ) . All WT and miR-146a-/- mice were on a C57BL/6 background [11] and were bred and housed in a specific pathogen-free mouse facility at the University of Utah , USA . WT ( strain 000664 ) and miR-146a-/- ( strain 016239 ) mice were also purchased from Jackson Labs to ensure phenotypes were repeatable in mice exposed to different housing . Mouse experiments were performed in female and male mice starting at 6 weeks , with results showing representative data from female cohorts . Mice were euthanized using carbon dioxide chambers at experimental endpoints . Cells from the RAW 264 . 7 murine macrophage cell line were cultured in DMEM complete media and kept at 37°C with 5% CO2 . Cells were passaged every 2–3 days to maintain logarithmic growth . Bone marrow was isolated from WT or miR-146a-/- mice , RBC lysed , and plated in DMEM complete media with 20 ng/mL mouse MCSF ( Biolegend ) . At 4 days of culture , fresh media containing MCSF was added to the cells . At day 7 , cells were stimulated with 1 μg/mL LPS ( Sigma ) for 24 hours . At 24-hours , media was removed and cells were stimulated with a second hit of fresh media containing LPS for an additional 2–6 hours . For Traf6i experiments , cells were pre-incubated with 20 μM Traf6i for 1 hour prior to each LPS stimulation . Protein lysate or RNA was collected using Qiazol/miRNeasy kit ( Qiagen ) and Western or qRT-PCR was performed on these cells . Mice were placed on NCD ( 10% kcal fat; Research Diets D12450Bi ) or HFD ( 45% kcal fat; Research Diets D12451i ) starting at 4–6 weeks of age for 12 to 18 weeks . Mice were weighed weekly , and food consumption was tracked . Body composition , including body fat , lean mass , and fluid , was measured via TD-NMR using the Bruker Minispec Body Composition Analyzer . Energy expenditure , movement , VO2 max , VCO2 max , respiratory exchange ratio ( RER ) , and food and drink consumption were measured using CLAMS metabolic cages , which was performed by the Metabolic Phenotyping Core . Serum was collected from mice treated with HFD for 0 , 2 or 17 weeks and Leptin levels were measured via MAGPIX , using the mouse metabolic hormone panel ( Millipore ) . For the rapamycin experiments , mice were placed on HFD for four weeks prior to administration of rapamycin , and then kept on HFD and intraperitoneally ( i . p . ) injected with rapamycin ( LC Laboratories ) three times per week for 10 weeks . Rapamycin was prepared in a solution of 100% ethanol , 10% PEG400 , and 10% Tween80 and was injected at 4 mg/kg based on body weight . For glucose tolerance tests ( GTT ) , mice were fasted for 6 hours prior to experimentation . 100 mg/mL of D- ( + ) -Glucose ( Sigma ) was prepared for intraperitoneal injection into mice and administered at 1mg/g based on body weight . Blood glucose levels were measured at timepoints 0 ( prior to injection ) , 5 , 15 , 30 , 60 , and 120 minutes following injection using a Bayer Contour glucometer . Blood for this experiment was taken via tail nicks . For insulin ELISA , serum was collected from 6-hour fasted mice , and insulin was measured using a mouse insulin ELISA kit ( Crystal Chem ) . The homeostasis model of β-cell function ( HOMA-B ) , the β-cell function index , was calculated using the formula: HOMA-B = [insulin ( microunits per milliliter ) 20]/[glucose ( millimoles per liter ) − 3 . 5] and homeostasis model assessment insulin resistance index ( HOMA-IR ) , the insulin resistance index , was calculated using the formula: HOMA-IR = [glucose ( millimoles per liter ) insulin ( microunits per milliliter ) /22 . 5] . Both HOMA-B and HOMA-IR were calculated using fasting values [38] . Approximately 0 . 1 grams of VAT or liver was collected from mice . RNA was extracted from these tissues using miRNeasy kits ( Qiagen ) , cDNA was made using qScript cDNA Synthesis Kit ( Quanta ) , and GoTaq Master Mix ( Promega ) ; The LC480 ( Roche ) was used for qPCR to measure expression of various genes . Primer set sequences can be found in supplemental materials and methods . For Western blots , levels of phosphorylated IKBα ( Cell Signaling ) , IKBα ( Cell Signaling ) , Traf6 ( Abcam ) , beta actin ( Abcam ) , and GAPDH ( Santa Cruz ) were measured from whole VAT lysates or BMDMs from WT or miR-146a-/- mice . Reproductive VAT pads were removed from mice , minced , and digested in buffer containing HBSS , Collagenase D ( Roche ) , and Dispase ( Worthington ) for 1 hour at 37 degrees . Homogenates were placed on ice for 30 minutes then spun to separate adipocytes from SVF . Adipocytes , which float on the top , were considered the adipocyte fraction . Supernatant was then removed to obtain the pelleted SVF . RBC lysis buffer was added to the SVF , which was spun and washed . RNA was collected from the adipocyte and SVF fractions via Qiazol/miRNeasy Kit ( Qiagen ) , and miR-146a levels were measured using miRCURY LNA RT PCR ( Exiqon ) and the mmu-miR-146a primer from Exiqon . Primer sequences for CD45 and Leptin can be found in supplemental materials and methods . WT and miR-146a-/- mice were treated with NCD ( Teklad 2920 , Envigo ) or HFD from 6 weeks of age ( Research Diets D12451i ) and were sacrificed at 20 weeks old . SVF pellets from three mice were combined to obtain sufficient cells for sorting and RNA collection for RNA-Seq that was performed as described above . Cells were stained with antibodies to CD45 , CD11b , and F4/80 , and live , singlet , CD45+ CD11b+ F4/80+ cells were sorted using a FACS Aria ( BD ) . RNA was collected via Qiazol/RNeasy Kit ( Qiagen ) . Library preparation used Illumina TruSeq Stranded RNA Kit with Ribo-Zero Gold and RNA-seq was performed using Illumina HiSeq 50 cycle single-read sequencing version 4 . Sequence alignment was performed through the University of Utah Bioinformatics Core Facility and Geneset Enrichment Analysis ( GSEA ) and Ingenuity Pathway Analysis software were used to examine types of genes that were up or downregulated in miR-146a-/- macrophages . Predicted miR-146a targets were obtained via Targetscan for mmu-miR-146a-5p . The RNA-Seq data have been deposited into NCBI GEO under accession number GSE119703 . RAW 264 . 7 cells were infected with a lentiviral CRISPR construct containing a Traf6 guide RNA or an Empty Vector using the same system we have previously described [39] . The two Traf6 guide RNA constructs were: GGAGATCCAGGGCTACGATG and GATGGAACTGAGACATCTCG . For infection , the lentiCRISPR and packaging plasmids pVSVg and psPAX2 were transfected into 293T cells using Trans-IT to produce virus . The lentiCRISPR was then transduced into RAW 264 . 7 cells via spin infection for 90 minutes at 30 C . The day after spin infection , cells were selected with 3 . 75 ug/mL puromycin . Media plus puromycin was replaced every 3 days and cells split as needed . Cells were allowed to grow for 17 days before experimental use . WT and miR-146a-/- BMDMs were cultured in DMEM and mMCSF for 7 days , then seeded into a 96-well Seahorse XF-96 plate . Cells were treated with LPS or media control and then incubated for 24 hours , followed by a second stimulation of LPS . For Traf6i experiment , cells were pre-incubated with 20 μM Traf6i or media control for 1 hour prior to each LPS stimulation . 1 hour after the second LPS stimulation , the Seahorse XF Mito Stress test was performed using a Seahorse XF-96 analyzer . Alternatively , Cultured RAW 264 . 7 cells were seeded into a 96-well Seahorse XF-96 plate and LPS-stimulated at 24 hours and 1 hour prior to the Seahorse experiment . This assay was performed by the Metabolic Phenotyping Core Facility at the University of Utah , USA . Concentrations of the following were added into the injection ports: 25 mM glucose ( A ) , 1 . 5 μM oligomycin A ( B ) , 1 . 5 μM FCCP + 1 mM sodium pyruvate ( C ) , 2 . 5 μM antimycin A + 1 . 25 μM rotenone ( D ) . Glucose and pyruvate-free assay media were used during the Seahorse assay , and cultured BMDMs or RAW 264 . 7 cells were washed with assay media before beginning the test . Graphpad Prism 7 software was used for graphing and statistical analysis of experimental data . Two-tailed Student’s T tests were used to calculate p-values . Quantitative data are displayed as mean +/− SEM . P-values are shown as indicated: *≤0 . 05 , **≤0 . 01 , ***≤0 . 001 , ns p>0 . 05 . For RNA-Sequencing , Ingenuity Pathway Analysis and Gene Set Enrichment Analysis were performed . FDR values are shown in GSEA plots , where FDR<0 . 25 is statistically significant .
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Obesity and metabolic disease are on the rise throughout the world , creating a need for research into the interaction between diet and genetics . It is known that chronic inflammation contributes to obesity , but it is not well understood how inflammation and inflammatory genes are controlled to prevent obesity . Here we found that a specific microRNA , miR-146a , controls inflammation and prevents obesity onset when mice are fed a high-fat diet . In the absence of miR-146a , mice become obese and develop diabetic symptoms in a diet-dependent manner . We found that miR-146a is highly expressed in immune cells and regulates the inflammatory function of macrophages within fat tissue . Within macrophages , we identified metabolic pathways regulated by miR-146a that could contribute to its protective role during DIO , and show that blockade of mTOR using rapamycin could reverse the obesity phenotype in miR-146a-/- mice . Human studies have previously shown that miR-146a expression is decreased in obese and diabetic patients , and our work suggests that miR-146a is mechanistically involved in the development of obesity and therefore can be a potential therapeutic target . Taken together , our work demonstrates that miRNA genetics and lifestyle are both important , contributing factors to diet-induced metabolic disease .
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2019
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Anti-inflammatory microRNA-146a protects mice from diet-induced metabolic disease
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Although leprosy is largely curable with multidrug therapy , incomplete treatment limits therapeutic effectiveness and is an important obstacle to disease control . To inform efforts to improve treatment completion rates , we aimed to identify the geographic and socioeconomic factors associated with leprosy treatment default in Brazil . Using individual participant data collected in the Brazilian national registries for social programs and notifiable diseases and linked as part of the 100 Million Brazilian Cohort , we evaluated the odds of treatment default among 20 , 063 leprosy cases diagnosed and followed up between 2007 and 2014 . We investigated geographic and socioeconomic risk factors using a multivariate hierarchical analysis and carried out additional stratified analyses by leprosy subtype and geographic region . Over the duration of follow-up , 1 , 011 ( 5 . 0% ) leprosy cases were observed to default from treatment . Treatment default was markedly increased among leprosy cases residing in the North ( OR = 1 . 57; 95%CI 1 . 25–1 . 97 ) and Northeast ( OR = 1 . 44; 95%CI 1 . 17–1 . 78 ) regions of Brazil . The odds of default were also higher among cases with black ethnicity ( OR = 1 . 29; 95%CI 1 . 01–1 . 69 ) , no income ( OR = 1 . 41; 95%CI 1 . 07–1 . 86 ) , familial income ≤ 0 . 25 times Brazilian minimum wage ( OR = 1 . 42; 95%CI 1 . 13–1 . 77 ) , informal home lighting/no electricity supply ( OR = 1 . 53; 95%CI 1 . 28–1 . 82 ) , and household density of > 1 individual per room ( OR = 1 . 35; 95%CI 1 . 10–1 . 66 ) . The findings of the study indicate that the frequency of leprosy treatment default varies regionally in Brazil and provide new evidence that adverse socioeconomic conditions may represent important barriers to leprosy treatment completion . These findings suggest that interventions to address socioeconomic deprivation , along with continued efforts to improve access to care , have the potential to improve leprosy treatment outcomes and disease control .
Leprosy , also known as Hansen’s disease , is a chronic and potentially disabling infectious disease caused by Mycobacterium leprae that primarily affects peripheral nerves and skin [1 , 2] . Since the introduction of multidrug therapy ( MDT ) in 1982 , the global burden of leprosy has been significantly decreasing [3 , 4 , 5] . In 2017 , the World Health Organization ( WHO ) reported 210 , 671 new cases of leprosy , including 26 , 875 from Brazil [3] . In endemic countries , treatment defaulting is still an important obstacle to effective leprosy control and elimination [6 , 7] . Specifically , interruptions and defaults from treatment may result in incomplete cures and persisting sources of infection in affected communities . Concerns have also been raised that patient non-adherence to MDT has the potential to contribute to drug resistance [8] . Further , delays in leprosy diagnosis and inadequate treatment may lead to irreversible physical disabilities that can cause stigma and social disadvantages in affected people [4] . Leprosy patients are grouped for treatment purposes according to their number of skin lesions: cases are classified as paucibacillary ( PB ) if they have up to five skin lesions and multibacillary ( MB ) in the presence of more than five skin lesions [1 , 2] . The classification of PB versus MB defines the nature and duration of the treatment regimen . Broadly , the term defaulting from treatment describes when an individual with leprosy does not complete the full MDT treatment despite repeated efforts from health services to ensure treatment completion [2] . As recently systematically reviewed by Girão and colleagues ( 2013 ) , there exists a limited evidence base regarding the determinants of leprosy treatment default [9] . Current evidence suggests leprosy treatment default may be influenced by both personal characteristics ( e . g . , quality of life , socioeconomic position ) and medical factors ( e . g . , treatment regimen and guidance , clinic distance , drug shortages ) [9] . Further , some poverty-related variables , including a low number of rooms per household and low familial income , have also been associated with leprosy treatment default in one population-based study in central Brazil [6] . Utilizing individual participant data from more than 20 , 000 leprosy cases followed up between 2007–2014 as part of the 100 Million Brazilian Cohort , this study used a hierarchical approach to investigate the association of geographic and socioeconomic factors with ( i ) overall leprosy treatment default , ( ii ) leprosy treatment default in PB and MB subtypes , and ( iii ) leprosy treatment default within Brazilian geographic regions .
The cohort used in this study was derived from the 100 Million Brazilian Cohort created by the Centre for Data and Knowledge Integration for Health at Oswaldo Cruz Foundation ( CIDACS/FIOCRUZ , Salvador , Bahia , Brazil ) . The aim of the 100 Million Brazilian Cohort is to investigate the role of social determinants and the effects of social policies and programs on health , through the linkage of data from social programs with databases of health information systems [10] . The 100 Million Brazilian Cohort was built using the baseline information of the national registry for social programs , Cadastro Único ( CadÚnico ) , from 2001 to 2015 . CadÚnico contains administrative records of all families applying for social programs in Brazil . To date , the 100 Million Brazilian Cohort includes socioeconomic data on over 114 million individuals . The individual records were linked with nationwide health datasets , including the 2007–2014 leprosy registries from the ‘Sistema de Informação de Agravos de Notificação’ ( SINAN-leprosy ) , through a deterministic algorithm , using the CIDACS-RL tool ( https://gitHub . com/gcgbarbosa/cidacs-rl ) . The specific variables used to match both datasets were patients’ name , date of birth , sex , mother’s name and municipality of residence . To assess the accuracy of data linkage , we carried out a manual analysis with a random sample of 10 , 000 pairs . For a cutoff of 0 . 93 , sensitivity was 0 . 91 ( 95% CI 0 . 90–0 . 92 ) and specificity was 0 . 89 ( 0 . 88–0 . 90 ) . The full linked dataset was de-identified to ensure anonymity/confidentiality of personal information and was made available for research from January 2018 ( https://hdl . handle . net/20 . 500 . 12196/FK2/FNMRCA ) . CIDACS implemented strong data security rules to control access , use , and data privacy and integrity . The final subset of the 100 Million Brazilian Cohort used in this study was restricted to individuals who were diagnosed with leprosy after enrolment in the cohort between 1 January 2007 and 31 December 2014 . Family units within the dataset included at least one member aged over 15 years old , with the oldest member of each family designated as the ‘head of the family . ’ Individuals were excluded if they: ( i ) were diagnosed with leprosy prior enrollment in the cohort , ( ii ) belonged to family units without one member aged over 15 ( i . e . , children who were registered separately from their original families were excluded from the study ) , ( iii ) had less than 1 day of follow-up on SINAN-leprosy , and ( iv ) were relapsed cases . Records with missing data on the study outcome and/or covariates were also excluded . Only for the covariates of schooling and employment ( with missing values ≥10% ) , missing information were considered as an additional category ( Fig 1 ) . We constructed a theoretical framework in which variables were grouped in three levels and blocks according to a predefined hierarchy represented by the conceptual framework shown in Fig 2 [11] . The distal level included geographic variables: region of residence in the country and location of family home ( i . e . , urban versus rural ) . The intermediate level was related to the socioeconomic position in the community and included: ethnicity/skin colour ( according to the self-identified classification used in the Brazilian census ) [12] , the highest level of education , employment and per capita family income ( i . e . , presented relative to Brazilian minimum wage ) . For individuals aged less than 18 years , schooling and occupation of the ‘head of the family’ were used as a proxy indicator . The proximal level comprised a set of variables related to household conditions experienced at the family level and included: housing material , household water supply , sewage disposal system , the source of home lighting , waste collection and household density ( i . e . , individuals per room ) . Because sex and age were considered as confounders a priori , they were included in all analyses . The study outcome was leprosy treatment default defined as a binary variable ( i . e . , default versus cure ) among newly detected leprosy cases [2] . For PB cases , treatment completion comprises 6 monthly doses of MDT until 9 months . For MB cases , treatment completion comprises 12 monthly doses of MDT until 18 months . The term ‘defaulter’ refers to leprosy patients who does not complete these full MDT treatment regimens ( PB patients who does not attend treatment for more than 3 months and MB patients for more than 6 months ) , even after repeated efforts of health professionals to tracking patients for treatment completion [2] . We conducted a descriptive analysis assessing the role of each geographic and socioeconomic variable on the study outcome in bivariate analyses . Then , in a multivariate analysis , blocks of variables from distal to proximal levels were added in a sequence following a hierarchical approach [11] as shown in the conceptual framework . The study outcome was analysed using logistic regression with cluster-robust standard errors to account for familial clustering of covariates . Because of the low prevalence of this study outcome ( i . e . , in less than 10% ) , the odds ratio ( OR ) estimates and their 95% confidence intervals ( CI ) provided a close approximation of the risk ratios [13] . An effect-decomposition strategy was applied to fit three logistic regression models ( A , B , and C ) by including step-by-step blocks of variables [11] . Variables in each block that were associated with leprosy treatment default at a significance threshold of P<0 . 10 were included in the next level model , with all models adjusting for sex and age . As a secondary analysis , we investigated the associations by leprosy subtype and across geographic regions . Because MB leprosy cases have been reported to have higher rates of treatment default and onward transmission than PB cases [1 , 7 , 14–17] , we compared the associations by leprosy subtype ( i . e . , PB versus MB ) . In addition , reflecting the important regional differences in social inequalities in Brazil , we performed analyses stratified by region ( North , Northeast , Midwest and South/Southeast ) [12] . All P-values were calculated for 2-sided statistical tests , and all analyses were performed using Stata , version 15 . 0 ( StataCorp LLC , College Station , Texas , USA ) . No personally identifiable information was included in the datasets used for analysis . Further , all data included in this study were stored on secured servers within CIDACS with strict access restrictions . This study was performed under the international ( Helsinki ) , Brazilian and United Kingdom research regulations and was approved by three ethics committee of research: ( i ) University of Brasília ( UnB ) ( protocol n° 1 . 822 . 125 ) , ( ii ) Instituto Gonçalo Moniz/FIOCRUZ ( protocol n° 1 . 612 . 302 ) and ( iii ) London School of Hygiene and Tropical Medicine’s Research Committee ( protocol n° 10580–1 ) .
Among 20 , 063 new cases of leprosy , 1 , 011 ( 5 . 0% ) defaulted from treatment . The percentage of default varied from 6 . 4% in 2007 to 5 . 4% in 2014 . Approximately half of the leprosy cases ( N = 10 , 101 , 50 . 4% ) were female . The median age was 34 . 9y ( IQ 24 . 6–52 . 5y ) , and 17 , 179 ( 85 . 6% ) were aged 15y or more . The proportion of children less than 15 years ( 14 . 6% ) was nearly 2-fold the average of new child cases in Brazil ( 7 . 3% of all new leprosy cases during 2007–2014 ) [18] . The median per capita income in US dollar ( USD ) was 34 . 0 ( IQ 16 . 6–89 . 3 ) . 13 , 063 ( 65 . 1% ) were residents in the Northeast and North regions , 16 , 050 ( 80 . 0% ) lived in an urban setting and 14 , 511 ( 72 . 3% ) self-identified as having a ‘pardo’ ( mixed ) ethnicity . 10 , 858 individuals ( 54 . 1% ) had up to 5 years of schooling , 11 , 080 ( 55 . 2% ) had a per capita familial income up to a quarter of the Brazilian minimum wage , and 9 , 030 ( 45 . 0% ) were unemployed or students . The majority of the leprosy cases lived in generally favourable household settings , with 13 , 956 ( 69 . 6% ) residing in houses made of brick or cement , 13 , 797 ( 68 . 8% ) accessing water supply networks , 16 , 166 ( 80 . 6% ) accessing electricity through a home meter , 15 , 271 ( 76 . 1% ) having public waste collection , and 15 , 267 ( 76 . 1% ) residing in households with up to 1 individual per room . Nevertheless , 13 , 480 ( 67 . 2% ) of the leprosy cases did not report access to improved sanitation ( Table 1 ) . In bivariate analyses , individuals from the North region were the most likely to default from leprosy treatment ( OR = 1 . 61; 95%CI 1 . 29–2 . 01 ) as compared to South and Southeast residents ( Table 1 ) . Intermediate factors associated with defaulting were black ethnicity ( OR = 1 . 39; 95%CI 1 . 07–1 . 79 ) , no income ( OR = 1 . 52; 95%CI 1 . 17–1 . 97 ) and per capita familial income up to a quarter of the minimum wage ( OR = 1 . 57; 95%CI 1 . 29–1 . 91 ) . Proximal factors associated with defaulting were: residency in accommodations constructed of wood and mud ( OR = 1 . 16; 95%CI 1 . 01–1 . 33 ) , informal home lighting or no electricity ( OR = 1 . 61; 95%CI 1 . 38–1 . 88 ) , no public waste collection ( OR = 1 . 18; 95%CI 1 . 02–1 . 36 ) , and household density between 0 . 75–1 ( OR = 1 . 29; 95%CI 1 . 08–1 . 54 ) and > 1 individual per room ( OR = 1 . 59; 95%CI 1 . 34–1 . 87 ) ( Table 1 ) . In multivariate analysis , region of residence was also associated with treatment default in the distal model . Relative to the South/Southeast regions , the North , Northeast , and Midwest regions had increased odds of treatment default . Similar to the bivariate analyses , participants from the North region had the highest odds of defaulting from leprosy treatment in the full cohort ( OR = 1 . 57; 95%CI 1 . 25–1 . 97 ) ( Table 2 ) . Intermediate factors associated with treatment default in the full cohort included ethnicity and income . Participants who self-identified as black ( OR = 1 . 29; 95%CI 1 . 01–1 . 69 ) and those with with 'no income' ( OR = 1 . 41; 95%CI 1 . 07–1 . 86 ) and a per capita income up to 0 . 25 minimum wage ( OR = 1 . 42; 95%CI 1 . 13–1 . 77 ) also had an increased probability of default from treatment . Of note , educational attainment and unemployment status were not associated with the odds of default ( Table 2 ) . Among the proximal factors , no conventional home lighting or no electricity ( OR = 1 . 53; 95%CI 1 . 28–1 . 82 ) and a household density greater than one person per room ( OR = 1 . 35; 95%CI 1 . 10–1 . 66 ) were associated with increased probability of treatment default ( Table 2 ) . Housing material , water supply , sewage disposal , and waste collection were not associated with leprosy treatment default in the multivariate model . In the subgroup analyses of leprosy subtype , the directions of effect were broadly consistent across the PB and MB cases . The higher odds of treatment default among individuals from the North of Brazil remained consistent in this subgroup analyses , as residence in this region was most strongly associated with treatment default of MB leprosy cases ( OR = 1 . 65 95%CI; 1 . 24–2 . 18 ) . Regarding the intermediate factors , black ethnicity and income level up to 0 . 25 minimum wage were associated with treatment default only in MB patients ( Fig 3 ) . In relation to proximal factors , subgroup analyses of leprosy subtype showed that the use of informal home lighting or lack of electricity and a high household density ( >1 individual peer room ) remained associated with treatment default across both leprosy subtypes ( PB and MB ) ( Fig 4 ) . In the subgroup analyses by Brazilian regions , lowest income level ( i . e . , no income or income up to 0 . 25 minimum wage ) was associated with odds of defaulting among residents in the Northeast region . An association between moderate educational attainment ( i . e . , 6–9 years ) and treatment default was only found in the Northeast inhabitants ( Fig 5 ) . Subgroup analyses by region also revealed higher odds of treatment default associated with use of informal electricity or lack of electricity supply among residents in the North ( OR = 1 . 75; 95%CI 1 . 28–2 . 40 ) . Finally , a high household density ( >1 individual per room ) was associated with higher odds of treatment default of individuals living in the Midwest of Brazil ( OR = 1 . 52; 95%CI 1 . 14–2 . 03 ) ( Fig 6 ) .
Using data from over 20 , 000 participants followed for up to 8 years , this cohort study is the largest to date investigating risk factors for leprosy treatment default ( corresponding to 57 . 1% of the average of 35 , 130 new leprosy cases registered in all country during the same period ) [18] . Our results revealed that individuals living in Brazilian regions carrying the highest leprosy burdens ( i . e . , North , Northeast , and Midwest regions of Brazil ) also had increased odds of treatment default relative to the lower burden South and Southeast regions . As inadequately treated cases have the potential to contribute to onward transmission , this finding suggests that enhanced efforts to improve treatment completion in these communities could have the potential to contribute to disease control in the most affected regions . Additionally , our findings indicate that self-identification as having black ethnicity as well as markers of deprivation , related to income , access to electricity , and household crowding , were associated with higher odds of MDT default . These important findings advance on prior research by indicating that individuals living in precarious socioeconomic conditions are not only at increased risk of leprosy infections [19 , 20] , but also they have an increased risk of treatment default following diagnosis . Few published studies have investigated factors associated with leprosy treatment default [7 , 16 , 21 , 22] . Factors suggested as barriers to adherence include poor household conditions , alcohol use , lack of knowledge about the disease and MB subtype [6 , 7 , 17] . In addition , a systematic review pointed to the need for more robust evaluations in this field , approaching regional particularities , since these associated factors may vary depending on the study location [9] . The largest previous study conducted in Brazil included 79 municipalities at high risk for leprosy transmission located in the Midwest region [6] . This study found that only low familial income ( i . e . , less than the current minimum wage ) and reduced number of rooms ( i . e . , less than 3 per household ) were associated with treatment default [7] . Our study provided important new evidence that geographic ( i . e . , region of residence ) , socioeconomic ( i . e . , black ethnicity ) and household conditions ( i . e . , access to electricity ) —factors well established as determinants of leprosy transmission [19 , 23]—may also be associated with defaulting from MDT . Evidence from the literature on socioeconomic factors associated with treatment default in other high leprosy burden countries is also scarce . In a study conducted in Nepal , most defaulters from MDT were illiterate , labourers and belonged to low-income families [21] . Another study , based in India , found an association of literacy status , per capita income and socioeconomic position with leprosy treatment outcomes . Higher default rates were evident among individuals that only completed primary education , had low per capita income , and belonged to the most deprived social classes [22] . In our study , the higher default rates among low income individuals might suggest the great financial impact of leprosy diagnosis and treatment on the affected households [24] . Our data also showed that living in households with informal lighting or no electricity was strongly associated with treatment default , mainly in the North region . Despite having adequate coverages of electricity , rural electrification of Brazil has not yet reached 100% [25] . Lack of access to electricity is an indicator of extreme poverty in the rural population . The use of irregular or informal sources of home lighting in peri-urban and urban areas also reflects socioeconomic deprivation [25 , 26] and may be a marker for poor access to the healthcare system . Consistent with previous research [7 , 14 , 15] , our findings showed higher probabilities of default associated with geographic ( residence in the North region ) and socioeconomic factors ( black ethnicity and low income ) in individuals classified as MB leprosy , when compared to PB forms . With regards to the higher rates of default in MB leprosy cases , the longer duration of treatment for these patients may present an additional barrier to treatment adherence [7 , 17] . Treatment default represents one of the most relevant obstacles to controlling chronic infectious diseases that require long-term treatment , such as leprosy [6] . A mathematical modelling investigation indicated that non-compliance to MDT and relapse of leprosy might have a negative impact on leprosy eradication , leading to an increase in disease prevalence and related deaths worldwide [27] . For the year 2017 , Brazil was the country reporting the highest number of relapses ( 1734 ) to WHO [3] . Individuals classified as defaulters are at high risk of relapses and might have a higher chance of developing resistance to leprosy drugs , representing obstacles to this disease control [5 , 9] . Among the main interventions to achieve leprosy control , the WHO recommends the strengthening of social and financial support with a focus on underserved populations , along with the use of a shorter and uniform regimen for all types of leprosy [5] . The use of a uniform multidrug therapy ( U-MDT ) regardless of any type of classification has been pointed out as the best option to halve treatment duration for MB patients ( from 12 to 6 months ) which could potentially decrease MDT default [28] . The strengths and limitations of this study should be stated . By linking nationally collected data on leprosy to socioeconomic information collected from more than 114 million individuals residing in all regions of Brazil , this study had an unprecedented sample size of leprosy cases with which to explore risk factors for leprosy default . Additionally , the inclusion of more than 20 , 000 cases enabled us to conduct stratified analyses and confirm that the associations were generally robust across leprosy subtypes and geographic regions . Importantly , this analysis also highlighted new factors associated with leprosy treatment default that have not previously been investigated ( i . e . geographic location , ethnicity and household living conditions ) in Brazil , the country with the second highest burden of leprosy worldwide [3] . On the other hand , this study also has limitations . First , as our data were collected routinely and not primarily for research purposes , 16 . 1% ( 3 , 848/23 , 911 ) of the linked individuals were excluded from the final analyses for having missing data . Second , we were unable to explore other determinants of default , such as characteristics of health services , individuals’ knowledge about the disease , and psychosocial and clinical factors , as these data were not available in our database . Qualitative assessment could provide a better understanding about the influence of these aspects in treatment completion of leprosy patients , as evidenced by a larger study conducted in Nepal aiming to understand people’s coping , help-seeking and adherence behaviour [29] . Third , although unlikely for most analysed socioeconomic characteristics , variables such as education and work might have changed in the time gap between the date of entry in the cohort and leprosy diagnosis . Finally , the generalizability of our results are restricted to individuals enrolled in CadÚnico , which represents approximately the poorest half of Brazilians who have registered for the national social protection programs . Although our findings may not be applicable to all leprosy cases in Brazil , it is likely that the point estimates of the associations between the indicators of deprivation and leprosy treatment default could be more pronounced if the full population of Brazil was included in the study . Based on the study findings , we can conclude that poor socioeconomic conditions may constitute obstacles to leprosy treatment compliance . We also highlighted a remarkable association between black ethnicity and leprosy treatment default . However , the overall evidence on the correlation between ethnic background and leprosy is limited [30] , which point to the need for further research . Our results also showed striking evidence on association of geographic and socioeconomic characteristics with treatment defaulting among MB leprosy individuals , who are the most important source of this disease transmission [2] . Decreasing default rates from MDT treatment has the potential to reduce the occurrence of relapses and physical disabilities and , by decreasing the infectious reservoir , may ultimately contribute to the goal of leprosy elimination . An integrated approach is needed , including actions on social determinants of leprosy and the adoption of full access to uniform treatment regimens for all PB and MB patients [5 , 28] , irrespective of material wealth . Other aspects that influence treatment default of leprosy cases , including distance from household to health service , adverse events/toxicity and mainly patient understanding the importance of correct treatment for cure should be better investigated . In addition to early diagnosis and prompt chemotherapy , social policies that reach the poor also at great risk of leprosy has been appointed about 100 years ago as a key strategy playing an important role and constituting a priority strategy to achieve leprosy control [31] .
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While the leprosy new case detection has been decreasing worldwide since the introduction of multidrug therapy ( MDT ) in the 1980s , treatment default remains an important risk factor for leprosy-associated disability and an obstacle to disease control and elimination . Treatment default occurs when an individual with leprosy does not take the prescribed number of doses required for treatment with MDT . We hypothesized that the frequency of defaulting may be influenced by geographic factors , especially as related to access to care , and socioeconomic factors , such as income , education , and household living conditions . To test this hypothesis , we investigated geographic and socioeconomic factors associated with leprosy treatment default among 20 , 063 new leprosy cases followed as part of the 100 Million Brazilian Cohort between 2007 and 2014 . In total , 5 . 0% of the leprosy patients defaulted from MDT . Among the associated factors , we found that having residency in the North and Northeast of Brazil , black ethnicity , low familial income , lack of formal electricity , and a high household density were associated with higher odds of leprosy treatment default . Overall , these findings highlight the need for tailoring MDT strategies for vulnerable populations in high-burden communities and suggest that social policies aiming to alleviate poverty should be investigated as potential tools for improving leprosy treatment completion .
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"Methods",
"Results",
"Discussion"
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2019
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Geographic and socioeconomic factors associated with leprosy treatment default: An analysis from the 100 Million Brazilian Cohort
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Adhesion governs to a large extent the mechanical interaction between a cell and its microenvironment . As initial cell spreading is purely adhesion driven , understanding this phenomenon leads to profound insight in both cell adhesion and cell-substrate interaction . It has been found that across a wide variety of cell types , initial spreading behavior universally follows the same power laws . The simplest cell type providing this scaling of the radius of the spreading area with time are modified red blood cells ( RBCs ) , whose elastic responses are well characterized . Using a mechanistic description of the contact interaction between a cell and its substrate in combination with a deformable RBC model , we are now able to investigate in detail the mechanisms behind this universal power law . The presented model suggests that the initial slope of the spreading curve with time results from a purely geometrical effect facilitated mainly by dissipation upon contact . Later on , the spreading rate decreases due to increasing tension and dissipation in the cell's cortex as the cell spreads more and more . To reproduce this observed initial spreading , no irreversible deformations are required . Since the model created in this effort is extensible to more complex cell types and can cope with arbitrarily shaped , smooth mechanical microenvironments of the cells , it can be useful for a wide range of investigations where forces at the cell boundary play a decisive role .
The dynamics of initial cell spreading – that is during the first few minutes – are governed by energy release through binding events of cell surface molecules , rather than by active cellular processes such as e . g . tension generated by stress fibers . These molecular binding events dominate the total adhesion energy of the cell . This adhesion creates a pulling effect that in turn generates strong local forces which result in deformations of the actin cortex . The dynamics of initial cell spreading ( the increase of radius of the contact area with time ) universally correspond to an early ( ) , and a later ( ) power law behavior [1] . It is only at an advanced stage when the cell is already moderately spread out that active pulling of actin stress fibers on focal adhesion complexes will reinforce cell spreading , depending on the cell type in question , see e . g . [2] . The viscoelastic behavior of the cell boundary is determined not so much by the cell membrane itself but by the intracellular cytoskeleton , or , in the case of red blood cells ( RBCs ) , a network of spectrin filaments directly underlying the membrane [3] , [4] . A model that can be used for describing cellular mechanics should be able to accurately describe the mechanical interactions that take place at the cell boundary , i . e . the contact interface with its substrate , the extracellular matrix or surrounding cells . Lattice-free , particle-based methods can describe the interaction forces and the resulting movement and deformation of particles in a natural way . At a point of contact between two particles , contact forces are calculated explicitly based on an appropriate contact force model . From these forces , movement of the particles is calculated by integrating the equation of motion . In the simplest approach , particles are assumed to be spherical . In that case , contact forces can be directly calculated from the sphere-sphere overlap distance ( are the radii of the spheres and the spacial coordinates of their centers ) . Calculating contact forces for non-spherical shapes is more challenging: approximations have to be made for the contact force model and it is not trivial to calculate a meaningful overlap distance for all cases . Arbitrary shapes have been modeled by using combinations of connected overlapping spheres [5] or by using polyhedra or poly-arcs , and calculating a contact force proportional to the overlapping volume of the shapes [6] , [7] . Besides , the surface of an arbitrary shape can be approximated by sampling points [8] . For each sampling point , a contact force can be calculated based on the indentation in the surface of another object . Disadvantages of using sampling points include that it is hard to directly compare it to a physical contact model such as the Hertz model for spheres , that they generally do not allow to reach complete force equilibrium , and that the precision of the approximation of the contact depends crucially on the local density of nodes , so that the contact parameters need to be re-scaled for different node densities [8] . We present a novel computational framework for describing the mechanical behavior of cells with an emphasis on the interaction between the cell and its environment . Although we only apply this model to cell spreading on a flat surface , the current implementation already allows for more complex settings of interaction with arbitrarily shaped smooth bodies , and cell-cell interaction . The main novelty of the method developed in this work lies in the fact that we calculate contact between a triangulated surface with “rounded” triangles reflecting the local curvature of a cell and its microenvironment by applying Maugis-Dugdale theory ( see section “Maugis-Dugdale theory” ) to all contacting triangles . To apply this adhesive contact model for the triangulated surfaces in our models , we build on the following six ideas ( see section “Contact mechanics of a triangulated surface” ) : This novel contact model is combined with a new implementation derived from existing mechanical models for red blood cells , mainly from Fedosov et al . [3] , [10] . That model has been previously computed using a dissipative particle dynamics ( DPD ) solver , a different meso-scale simulation method . The mechanical model of the cortex of the RBC includes finitely extensible nonlinear elastic ( FENE ) connections and viscous dissipation between the nodes of the triangulation , volume conservation and surface area conservation , as well as bending resistance – see section “Elastic model of the cortex” . Finally , we apply this newly developed method to an in-depth computational investigation of RBC spreading ( see Figure 1 and supplementary Videos S1 and S2 ) as reported by both Hategan et al . [11] and Cuvelier et al . [1] in order to unravel the governing mechanisms .
In the experiments reported by Cuvelier et al . [1] , biotinylated RBCs were osmotically swollen to become spherical and the change of the radius of the contact area with time was measured for spreading on a streptavidin coated surface . To compare to the spreading dynamics reported in that paper as well as by Hategan et al . [11] ( where the cells spread on a polylysine coated surface ) , we set up simulations of the described model with the parameters as given in table 1 . The red blood cell is modeled with a viscoelastic cortex including bending stiffness and Maugis-Dugdale contact interactions . Most parameters in table 1 are taken directly from the literature as indicated . The effective range of interaction ( see equation 8 ) was estimated at by interpolating from [14] for cells with a radius of ≈3 µm . The cortex Young's modulus used in the Maugis-Dugdale model is the material stiffness of the phospholipid-spectrin complex ( the elasticity of the deforming membrane is already taken into account by the FENE potentials ) . This material stiffness can be assumed to be much higher compared to the whole cell's Young's modulus and is set at a value of . The parameters for the cortex are validated by performing the cell stretching and relaxation experiments explained in the previous section “Validation of the RBC cortex model” . A view on three stages of the cell spreading for both biconcave and sphered RBCs is presented in Figure 1 . Note that the volume of the biconcave RBC is only about of the volume of the sphered RBC . As a result of that , for the sphered RBC , the final height of the spread-out cell is greater and it has a higher angle of contact compared to the final shape of the initially biconcave RBC . For this simulation , a triangulation based on a five-fold subdivision of an icosahedron was used – see section “Generating triangulated meshes of cells” . This level of mesh refinement is required to reproduce the final high curvatures at the edge of the contact area when the cell is fully spread out: The triangles at the edge have encompassing spheres with radii of ca . , while Hategan et al . [15] report a typical radius of the rim for this situation of , which is of comparable order of magnitude . The shape of the final spread-out cell is a spherical cap . By fitting a sphere through the top % of the nodes , the effective contact angle [16] can be estimated . For the modeled RBC , we calculate an effective contact angle of , which corresponds reasonably well to the measured effective contact angle of around 60° [15] . Figure 4 shows the power-law behavior of the sphered RBC spreading in double logarithmic representation . The “contact radius” of the RBC in these and the following figures is calculated from the sum of all the triangles' areas which are in contact by defining . The spreading dynamics of the model match the experimentally observed cell spreading [11] very well . Figure 5 summarizes the influence of varying one parameter at a time for the most influential parameters of the model starting from the base parameter set reported in table 1 . Its first sub-figure ( a ) shows simulation results of cell spreading for different values of the cell-substrate adhesion strength . A lower adhesion strength results in a lower final contact radius , but also makes the spreading slower . However , the power law behavior as reported by Cuvelier et al . [1] stays well conserved for different adhesion strengths . The influence of the FENE stretching constant is shown in Figure 5 ( b ) . In the range of the RBC FENE constant ( in the order of 1 µN/m ) , the influence of on the spreading dynamics is comparatively small . For larger deviations , higher values of limit the final spreading radius to a lower value , or conversely , lower values allow the cell to spread considerably more . A FENE connection is also characterized by the maximal stretch ( Figure 5 ( c ) ) , which expresses the maximal extension of the spring , at which the FENE force diverges ( equation 22 ) . The initial spreading dynamics are not affected by the precise value of , but the final spreading radius is . For higher values of , the same tension in the cortex corresponds to a larger extension and therefore a larger radius of the spread out cell . The effect of the bending stiffness on RBC spreading is shown in Figure 5 ( d ) . A higher bending resistance of the cortex speeds up cell spreading , the probable reason being that , through resisting to bending , the cortex keeps the contact angle within the effective range of adhesive interaction close to 180° . This range is of the order of for microscopic biomolecular surfaces [14] . It should be noted that for a theoretical vesicle with bending resistance , the actual contact angle is always 180° [16] . However , for a real RBC , the width of the adhesive spreading front is non-zero and determined by the effective range of interaction . This effective adhesive range is taken into account in Maugis-Dugdale theory ( equation 8 ) and relates the maximal adhesive tension at the edge of contact to the total work of adhesion . The normal friction coefficient is determined by the energy dissipation when adhesive contact is initiated . The dissipation is caused by snap-in-contact events when adhesion molecules form bonds , and the hysteresis arising from unbinding stochastically again [14] . In Figure 5 ( e ) , the effect of changing on the RBC spreading dynamics is shown . As could be expected , a lower value of diminishes the energy dissipation due to adhesion and therefore increases the rate of cell spreading . However it does not change the initial power law behavior of cell spreading . Finally , in Figure 5 ( f ) , the effect of the local area constraint on the spreading dynamics is shown . When the value of is too low , degenerate triangle shapes can arise with a strongly decreased area . This will result in an underestimation of the final spreading radius . It can be observed that for values of , the local area of the triangles is sufficiently well conserved and the predicted spreading dynamics are not affected . In Figure 6 ( a ) , the outward normal pressure on the nodes is visualized for three distinct phases of the cell spreading process for a sphered RBC . The normal pressure is defined here as the magnitude of the sum of all conservative forces ( on the left-hand side in the equation of motion , 31 ) in the nodes projected onto the normal in that node – therefore this normal pressure is dominated by contact forces , where adhesive ones yield a positive ( outward ) pressure in this case . Figure 6 ( b ) shows the in-plane tension ( in ) of the cortex ( further denoted as cortex tension , and not to be confused by the adhesive tension , given by Maugis-Dugdale theory , see equation 7 ) at the same time points . This tension is characterized by the FENE force at the inter nodal connections . Positive forces in these connections correspond to tensile stress in the cortex , while negative values are associated with compressive stress: ( 1 ) where is the number of FENE connections of node and is the inter-nodal distance ( see e . g . [17] ) . At the spreading dynamics correspond to the power law regime . At this stage , adhesive forces are strong especially at the edge of contact , but also in the entire rapidly increasing circular contact area . The elastic energy stored in the membrane at this point in time is very low , as the stretch and bending in the membrane is small . As a result , almost all the energy dissipation ( see section”Equation of motion” ) takes place in the contact area . At , a distinct adhesive edge can be observed , in which the magnitude of forces is much stronger than in the inner circle of the contact area , where the contact potential is already nearly minimal . At the edge , the cortex's bending stiffness provides resistance to the strong adhesive tension . Meanwhile , the upper spherical cap is being stretched while at the plane of contact the membrane – together with the substrate it is adhering to – is under compressive stress . At this stage , energy dissipation takes place not only at the substrate interface , but also in the entire stressed cortex . As a result of this , the spreading slows down to a lower rate than the power law regime . At , spreading has stopped and the cell has reached equilibrium . The forces at the nodes are zero , and the adhesive tension at the edge of contact is being balanced out by the elastic stress in the RBC membrane/cortex . The cortex in the spherical cap is under strong tensile stress and the stretch in the connections is close to its maximal value . At the substrate interface , compressive stresses have built up even more . For an elastic substrate , these compressive forces will cause radial inwards deformation of the substrate , as has been observed in traction force microscopy measurements [18] , [19] – although these experiments concern late cell spreading . It should be noted that the maximal normal pressure at the nodes – occurring in the first stage of cell spreading – corresponds to a force in the order of , which is in the range of the force applied in the stretching simulations which were used to validate the model parameters of the elastic cortex , see section “RBC stretching experiments” .
First , with regard to the performance of the newly developed model for a triangulated , deformable cell obeying Maugis-Dugdale contact tractions , we conclude that: The modeling technique described in this work has a number of limitations: Finally , regarding the initial dynamics of cell spreading , we find: Although the model as shown is restricted to RBC spreading dynamics , we expect that these conclusions can be generalized to other cell types: the same key mechanical components are present in other systems as well , and despite the fact that other cells' cytoskeletons are more complex and the cells can dissipate energy through “active biological processes” , we expect the initial cell spreading phase to be still characterized by contact dissipation . Eventually , stress in the membrane/cortex will build up as well and through this , the cell will dissipate energy in the entire cortical shell . However , it is possible that this dissipation involves irreversible deformation in the cortex .
For two spherical asperities in contact or one asperity in contact with a flat surface ( see Figure 7 ) , Maugis-Dugdale ( MD ) theory can be used to describe the contact mechanics [24] . This theory captures the full range between the Derjaguin-Muller-Toporov ( DMT ) zone of long reaching adhesive forces and small adhesive deformations to the Johnson-Kendall-Roberts ( JKR ) limit of short interaction ranges and comparatively large adhesive deformations in the transition parameter . This transition parameter relates to the Tabor coefficient by a factor of [25] . ( 3 ) In equation 3 , is the maximum adhesive tension ( measured in Pa ) from a Lennard-Jones potential , ( in J/m2 ) the adhesion energy , is the reduced radius of the asperities and the combined elastic modulus: ( 4 ) The ( repulsive ) Hertz pressure associated with a contact of radius ( see Figure 7 ) is given by ( 5 ) Assuming a spherical asperity – and therefore a circular contact area – the total Hertz force can be calculated by integrating equation 5 over the complete circular contact area with radius , which yields the total Hertz force: ( 6 ) An adhesive stress can be formulated as [24] , [26]: ( 7 ) In the Maugis-Dugdale model , local adhesion tension is assumed to be independent of the overlap until a cut-off distance . If the asperity is further than away from the flat surface , the adhesive tension drops to zero . Therefore , is related to the adhesion energy as: ( 8 ) is the total work of adhesion , i . e . the work required to move the asperity away from the surface and out of contact . To pull a small area out of contact , the required work is: ( 9 ) The total ( global ) adhesive force is the integral over the adhesive zone with radius ( see Figure 7 ) , which according to [26] becomes: ( 10 ) The force in equation 10 is dependent on . As equation 10 expresses the global adhesive force of the complete asperity , it is not a constant force , but through dependent on the indentation . To calculate the adhesive radius from the actual geometrical contact area with radius , the height at the edge of the adhesive zone can be used . Substituting both repulsive and adhesive pressures at ( see equation 5 and 7 ) this yields [25]: ( 11 ) where . In general , to calculate both and from a given state of the contact , one needs to solve this equation simultaneously with the equation for the net contact force [25]: ( 12 ) A very well validated contact model for soft , adhesive bodies like cells , the JKR theory [27]–[29] , is a limiting case of Maugis-Dugdale theory for negligible cutoff-distance for the adhesive interaction ( or ) . It has therefore a parameter less than MD theory . The adhesive pressure according to JKR ( compare to equation 7 ) is ( 13 ) Note that this pressure diverges at . Summarizing the Maugis-Dugdale theory for an adhesive contact , one considers three distinct zones: The meshes used in this work are derived from spherical shapes by subdividing an icosahedron and projecting the nodes on a sphere [30] . In a subdivision , each triangle gets split into four triangles as is illustrated in Figure 8 . Here it is shown how one triangle with an encompassing sphere matching the local curvature of the cell , is split into four triangles . Since the local curvature is kept , the new triangle nodes are all located on the surface of the same encompassing sphere . Every subdivision of an icosahedron has only twelve nodes with a five-fold connectivity and slightly longer distances to their neighbors; otherwise , the mesh is perfectly regular with six-fold connectivity and is ideal for curvature calculations ( see section “Local curvature of the 3D shape” ) as reported by [31] . The bi-concave shape of an RBC can be obtained by reducing the volume of the sphere to approximately 60% , and letting a system of linear springs with appropriately chosen parameters relax again . This is the reverse process to the well described technique of RBC sphering , see e . g . [32] . We use meshes of either four or five subdivisions of an icosahedron , corresponding to 642 and 2562 nodes , respectively . In the deformable cell model , the cortex nodes interact through viscoelastic potentials . In the most simple approach , a linear elastic spring could be used . For a given displacement of nodes and , the elastic spring force over a connection is: ( 20 ) in which and are the actual distance and equilibrium distance between connected node and . The linear spring stiffness is called . For red blood cells , two non-linear spring models have been used in literature: the finite extensible non-linear elastic model ( FENE ) and the worm-like chain model ( WLC ) [3] . These models express that upon stretching , the biopolymers of the cytoskeleton – a sub-membranous network of spectrin connections for RBCs – first uncoil , providing relatively little resistance , but when completely stretched out , become practically non-extensible . Between two connected nodes and , the FENE attractive potential reads: ( 21 ) where is the maximal distance , and the stretching constant . The force derived from this is: ( 22 ) FENE springs exert purely attractive forces . In order to account for the ( limited ) incompressibility of the spectrin , a simple power law is used ( power ) : ( 23 ) The incompressibility coefficient can be derived for the assumption that the total force must vanish for , the equilibrium distance: ( 24 ) In the present model , we set , as suggested by [3] . It is convenient to denote the maximal stretch by , the fraction of maximal extension and equilibrium distance . In addition to this purely elastic potential , we also include dissipation as per the Kelvin-Voigt model by adding a parallel dashpot with the damping constant : ( 25 ) Here , is the projection of the relative velocity of a pair of connected cortex nodes on their connecting axis . The force is also applied in the direction of the connection . Whereas in-plane stretching and compressive forces can be calculated purely based on the distance between two neighboring cortex nodes , bending forces are calculated for two neighboring triangles . The bending moment between two adjacent triangles is given as ( 26 ) Here , is the model parameter determining the bending rigidity , is the instantaneous angle and the spontaneous angle between a pair of triangles with a common edge . A corresponding force is applied to the non-common points of each of the two triangles , with a compensating force applied to the points on the common edge , ensuring that the total force on the cell remains unchanged . This type of bending-stiffness is commonly found in the literature for RBC models , eg . by [4] and [36] - a more general analysis is provided by [37] . Additionally , both a global and local area constraint is used , making sure that both the individual triangle areas and the total area of the red blood cell cannot strongly increase or decrease . As described by [4] , this is achieved by a local force with magnitude: ( 27 ) in which is the triangle area , the resting triangle area and the local constraint constant . The magnitude of the global force is formulated as: ( 28 ) where is the total RBC area , the total resting area and the global constraint constant . For both constants , values were taken from [3] . These forces are applied in the plane of each triangle in the direction from the barycenter of the triangle . Finally , we add a volume constraint since for short timescales , the total cytosol volume of the cell can be considered constant . As for the area , magnitude of the force takes the form ( 29 ) with the instantaneous cell volume and the initial cell volume . This force is applied to each node of the cell in its outward direction as found by the Laplace-Beltrami operator , see equation 14 . In the low Reynolds number environment in which cells live , motion is dominated by viscous forces [38] . In other words , inertial forces are negligible . For each integration node , Newton's second law ( with explicit Stokes' drag ) ( 30 ) by leaving out the inertial term , becomes ( 31 ) The total force on node is the sum of all the individual forces: Firstly , the forces that are calculated on the triangles are transferred to the nodes – the contact forces only exist for triangles , which are in contact with the substrate . Also , the local and global area constraints for the membrane are added here . Secondly , the cortex connection forces between node and all fixed connections are added , and finally the volume constraint and the gravitational force as well as a random force The total force on node i is the sum of all the individual forces: Firstly , the forces that are calculated on the triangles are transferred to the nodes – the contact forces Fcontact only exist for triangles , which are in contact with the substrate . Also , the local and global area constraints for the membrane are added here . Secondly , the cortex connection forces between node i and all fixed connections k are added , and finally the volume constraint and the gravitational force Fcontact as well as a random force Fcontact for taking into account fluctuations of the membrane can be added . Since those fluctuations do not much influence the spreading dynamics in our simulations , we neglect that term for the results presented . For the right-hand side , we not only discard the term proportional to mass , but we also more explicitly state the components of the constant : starting with the dissipative/friction term generated from the encompassing sphere - substrate friction between two contacting triangles . This coefficient is weighted by the distance of the node from the contact point in that triangle . This ensures symmetry of the friction-matrix ( see below ) and corresponds to the distribution of the contact force . The component of the substrate friction for a triangle is defined as ( compare to e . g . [39] ) where is the area of contact in that triangle , is the normalized direction vector between the two encompassing spheres and are , respectively , the normal and tangential friction constants . Secondly , we have the dissipative dashpot of the connections of this node , and lastly we add the drag coefficient for the whole cell in plasma: here , in a first order approximation , we simply divide the formula from Stokes' law by the number of nodes per cell , thereby recapturing the exact result for a spherical cell in Stokes flow . For nodes , whose surrounding triangles are all in contact with the substrate , we define a very high friction constant , effectively fixing those nodes in place . We found that this has no influence on the spreading curves ( it can be completely left out ) , but helps to dampen out small numerical fluctuations in the stiff potential of the contacting plane . This allows us to use larger time steps when solving this equation of motion . Equation 31 , which is used in essentially the same form by e . g . [13] , [39]–[43] , is a first order differential equation , which couples the movements of all particles together . When writing the whole system as ( 32 ) it can be shown [13] , that the matrix is positive definite , and therefore we are able to solve the system iteratively for the velocities by using the conjugate gradient method . Subsequently , the nodes' movement is integrated by a forward Euler scheme [44] . For a low Reynolds number environment , the amount of kinetic energy ( or motion ) directly corresponds to the amount of dissipated energy . Equation 32 shows all dissipative terms in the matrix dictating the degree of motion induced by the forces . Identifying all significant dissipative mechanisms is therefore crucial for calculating the dynamics of this system .
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How cells spread on a newly encountered surface is an important issue , since it hints at how cells interact physically with the specific material in general . It has been shown before that many cell types have very similar early spreading behavior . This observation has been linked to the mechanical nature of the phenomenon , during which a cell cannot yet react by changing its structure and behavior . Understanding in detail how this passive spreading occurs , and what clues a cell may later respond to is the goal of this work . At the same time , the model we develop here should be very valuable for more complex situations of interacting cells , since it is able to reproduce the purely mechanical response in detail . We find that spreading is limited mainly by energy dissipation upon contact and later dissipation in the cell's cortex and that no irreversible deformation occurs during the spreading of red blood cells on an adhesive surface .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Models"
] |
[] |
2013
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Analysis of Initial Cell Spreading Using Mechanistic Contact Formulations for a Deformable Cell Model
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Oral transmission of Trypanosoma cruzi , the causative agent of Chagas disease , is the most important route of infection in Brazilian Amazon and Venezuela . Other South American countries have also reported outbreaks associated with food consumption . A recent study showed the importance of parasite contact with oral cavity to induce a highly severe acute disease in mice . However , it remains uncertain the primary site of parasite entry and multiplication due to an oral infection . Here , we evaluated the presence of T . cruzi Dm28c luciferase ( Dm28c-luc ) parasites in orally infected mice , by bioluminescence and quantitative real-time PCR . In vivo bioluminescent images indicated the nasomaxillary region as the site of parasite invasion in the host , becoming consistently infected throughout the acute phase . At later moments , 7 and 21 days post-infection ( dpi ) , luminescent signal is denser in the thorax , abdomen and genital region , because of parasite dissemination in different tissues . Ex vivo analysis demonstrated that the nasomaxillary region , heart , mandibular lymph nodes , liver , spleen , brain , epididymal fat associated to male sex organs , salivary glands , cheek muscle , mesenteric fat and lymph nodes , stomach , esophagus , small and large intestine are target tissues at latter moments of infection . In the same line , amastigote nests of Dm28c GFP T . cruzi were detected in the nasal cavity of 6 dpi mice . Parasite quantification by real-time qPCR at 7 and 21 dpi showed predominant T . cruzi detection and expansion in mouse nasal cavity . Moreover , T . cruzi DNA was also observed in the mandibular lymph nodes , pituitary gland , heart , liver , small intestine and spleen at 7 dpi , and further , disseminated to other tissues , such as the brain , stomach , esophagus and large intestine at 21 dpi . Our results clearly demonstrated that oral cavity and adjacent compartments is the main target region in oral T . cruzi infection leading to parasite multiplication at the nasal cavity .
Human Chagas disease ( American trypanosomiasis ) is a neglected tropical illness caused by the protozoan Trypanosoma cruzi . Infection affects 6–8 million people worldwide and is considered a global health problem . Chagas disease is endemic in Mexico , Central America and South America and is also spreading in non-endemic countries through migration of infected people [1] . It can be transmitted by excreta deposition after biting of blood sucking Triatominae bugs , blood transfusion; organ transplantation; laboratory accident; congenitally and orally [2 , 3] . Outbreaks of oral transmission of Chagas disease were described in Brazil , Venezuela , Colombia , French Guyana , Bolivia , Argentina and Ecuador [4–9] . All of these outbreaks were associated with contaminated food/beverage consumption as wild animal meat , vegetables , sugar cane extract , açaí pulp , guava juice , bacaba , babaçu and vino de palma [10–12] . From 1968 to 2000 , 50% of acute cases in Amazon region were attributed to oral transmission [8] and these numbers reached 70% between 2000–2010 [6] . Venezuela has also reported the biggest outbreak described so far , with two distinct occurrences affecting respectively 103 and 88 people . These outbreaks involved adults and children from urban and rural schools [5 , 13] . Mortality rate in orally infected patients is reported as higher ( 8–35% ) when compared to the classical vectorial transmission , through triatomine excreta deposition after biting ( <5–10% ) [14] . It is well known that both trypomastigotes and metacyclic trypomastigotes are associated with oral Chagas transmission [15–17] . Regarding T . cruzi genotypes , isolates from DTUs I , II , III , IV and VI have been associated with patients from oral Chagas outbreaks [18–25] . Although relevant , there are few reports about T . cruzi oral transmission in the literature . Some authors have demonstrated parasite-mucosa interaction , some aspects of immune response as well as disease outcome after intragastric , pharyngeal or buccal parasite challenge . These models of oral T . cruzi infections present both patent parasitemia and heart parasitism , which indicate systemic infection [26–30] . In addition , T . cruzi glycoprotein gp82 seems to bind gastric mucin , promoting invasion and replication in epithelial cells from the gastric mucosa [31] . This initial invasion is related to establishment of a progressive gastritis and allows further systemic dissemination of the parasite . Nonetheless , the short replication period at this mucosal site induces specific immunity , as protection was observed after a secondary mucosal challenge , involving the production of IgA and IgG antibodies [27] . In orally infected mice , inflammatory infiltrates are observed in tissues such as pancreas , spleen , liver , bone marrow , heart , duodenum , adrenal glands , brain and skeletal muscle . Moreover , it was suggested that intraepithelial and lamina propria lymphocytes are involved in IFN-γ but not IL-4 production in orally infected hosts [27] . Following disease outbreaks caused by T . cruzi food contamination , a clear increase in severity of clinical manifestations was observed in patients , as compared with other types of transmission routes [8 , 14] . These observations raise important questions concerning the particular features of T . cruzi entry via the mucosa , including the possible modulation of local immune mechanisms and the impact on regional and systemic immunity [32 , 33] . We have recently demonstrated that the site of parasite entrance , through oral infection ( OI ) –directly in the mouth , as observed in natural infection , or gastrointestinal infection ( GI ) –directly to the stomach via gavage differentially affects host immune response and mortality . Thus , comparing to GI mice , we observed that OI mice presented elevated infection rate and parasitemia , higher TNF serum levels , more severe hepatitis and milder carditis [15] . This difference in immunological response and infection severity between GI and OI mice raised important questions about the primary site of T . cruzi infection by the oral route and its impact on disease progression . Bioluminescent imaging is a promising technique that brings the opportunity to approach the in vivo host-pathogen interactions through a highly sensitive and non-invasive way [34] . In addition to allow the follow up of infection progression by keeping the animal alive , this technique also gives the possibility to observe new sites of infection and parasite distribution that are hardly observed by histological techniques [35] . In the past years , some reports developed in vivo bioluminescent analysis both in T . cruzi infected mice and in the invertebrate host [35–37] . In the present work , by employing the bioluminescent technique and real-time qPCR , we followed the dynamics of T . cruzi Dm28c luciferase ( Dm28c-luc ) distribution throughout the host using our well-established model of OI in mice [15] . The bioluminescence results indicated the nasal cavity as the main primary site of parasite invasion and multiplication in the host . At later moments , luminescent signal progressively increased in the abdomen and genital region , as a result of parasite dissemination . Quantification of parasite load , via T . cruzi satellite DNA ( SatDNA ) detection by real-time qPCR at 7 and 21 dpi , corroborated the bioluminescence results , showing predominant T . cruzi detection in mouse nasal cavity . Parasite amplification was also observed in the mandibular lymph nodes , pituitary gland , heart , liver , small intestine and spleen at 7 dpi , and was disseminated to other tissues , such as the brain , stomach , esophagus and large intestine at 21 dpi . Our results indicate the oral cavity and adjacent tissues as the main target region for oral T . cruzi infection , leading to parasite multiplication at the nasal cavity .
Male BALB/c mice , aged 6–8 weeks , were obtained from the animal facility of Oswaldo Cruz Foundation ( Rio de Janeiro , Brazil ) and used in all experiments . Animals were handled according to the rules of the Ethics Committee for Animal Research of Oswaldo Cruz Foundation . The total number of mice used in each experimental set is described in S1 Fig flowchart . Mice were infected via the oral cavity ( OI ) with trypomastigotes of a Dm28c ( DTU- TcI ) genetically modified to express the firefly luciferase ( Dm28c-luc ) , Dm28c-GFP or Tulahuén ( DTU- TcVI ) strains [35 , 38] . Parasites were obtained from infected cultures of a monkey kidney epithelial cell line ( Vero cells ) from the particular Cell Line Collection of the Laboratory on Thymus Research , Oswaldo Cruz Institute . T . cruzi were counted using Neubauer's chamber in phosphate buffered saline ( PBS ) . Mice were maintained starving for 4 hours and then infected with 1x106 trypomastigotes in 50 μL of parasite suspension into the mouth . At the infection moment , mice swallowing time was respected to avoid parasite aspiration . A control experiment was performed with injection of 50 μL of black ink suspension at the oral cavity or intranasally to validate our protocol of oral infection and to exclude the possibility of an intranasal contamination ( S2 Fig ) . This study was performed 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 and the Federal Law 11 . 794 ( 10/2008 ) . The Institutional Ethics Committee for Animal Research of the Oswaldo Cruz Foundation ( CEUA-FIOCRUZ , License: LW-23/12 ) approved all the procedures used in this study . Mouse parasitemia was individually evaluated at different days post-infection ( 4 , 7 , 11 , 14 and 21 dpi–S1 Fig ) by counting trypomastigotes in 5 μL of tail vessels blood . Blood-parasite number was calculated according to the Brenner method . Photoluminescence signals were measured at different time points post-infection ( 15 and 60 minutes ( min ) , 7 and 21 dpi–S1 Fig ) , in anesthetized animal , by ventral and lateral position using the IVIS Lumina image system ( Xenogen Corp , CA , EUA ) . D-luciferin potassium salt ( Xenogen ) stock solution was prepared in PBS at 15 mg/mL and stored at -80°C . Analyses of 15 min post-infection imaging were performed with a 5 min pre-incubation of Dm28c-luc with 0 . 15 mg in PBS ( 10 μL ) of D-luciferin stock solution followed by mouse infection . Photoluminescent images of infected mice were acquired 15 min later . Images at 60 min post-infection were carried out after intraperitoneal injection of D-luciferin ( 150 mg/Kg of body weight ) followed by an addition of 50 μL of D-luciferin ( 0 . 75 mg in PBS ) at the oral cavity , just before capturing the images . At 7 and 21dpi analyses , photoluminescent signals were measured with images starting 15 min after an intraperitoneal injection of D-luciferin solution in potassium salt ( 150 mg/Kg of body weight ) . Mice were placed inside the animal chamber anaesthesia delivery system ( Xenogen XGI-8 Gas Anaesthesia system ) . Isoflurane ( 1 . 5% ) anaesthesia was applied until the mice became recumbent . These animals were then placed into the image chamber of IVIS Lumina system ( Xenogen Corp , CA , USA ) and controlled flow of isoflurane , with a nose cone device into the chamber , maintained them anesthetized during the bioluminescence imaging acquisition . For the analysis of T . cruzi presence in specific organs , mice were injected with D-luciferin at different times post-infection ( S1 Fig ) , and 10 min later mice were euthanized in order to perform single tissue harvest . Tissues were removed , transferred to a culture dish and images acquired at the IVIS Lumina image system . Acquisition of bioluminescent images of both mice and tissues was performed by 5 min of exposure and the photons emitted from luciferase-expression T . cruzi were quantified using the Living Image 3 . 0 software program . Uninfected and six days post infection mice were euthanized , the nasal cavity were isolated and tissue were included in tissue tek ( OCT , Sakura , USA ) . Cryosections ( 5 μm ) of frozen tissues were analyzed using a fluorescent Zeiss microscope ( Germany ) . Images were digitalized using AxioCam HRm and Axio Vision Rel 4 . 8 software . DNA extraction was performed from nasal cavity , palate , tongue , esophagus , stomach , small intestine , large intestine , liver , heart , spleen , mandibular lymph nodes , pituitary gland and brain , using the QIAamp DNA Mini kit ( Qiagen , CA ) . Tissues were obtained from dissected infected mice at different time points ( 60 min , 7 and 21 dpi ) , individually weighted ( maximum 10 mg for spleen and 30 mg for other tissues was used ) , washed in PBS ( except tissues from nasal cavity , mandibular lymph nodes and pituitary gland ) and stored at -20°C until DNA extraction . Blood was drawn via cardiovascular perfusion with PBS , immediately after euthanasia . Nasal cavity tissue was obtained after scraping the region . Tissues and organs from non infected mice were used for negative control . The protocol was carried out according to the manufacturer’s instructions and the DNA was eluted with 100 μL of elution buffer ( AE ) . As a qualitative internal reference control , the exogenous internal amplification control ( IAC ) , a pZErO-2 plasmid containing an insert from the A . thaliana aquaporin gene , was used as reported by Duffy ( 2009 ) . Before DNA extraction , 5 μL ( 40 pg/mL ) of linearized IAC were added to the samples . DNAs were stored at −20 ◦C until use and their purity and concentration were determined using a Nanodrop 2000c spectrophotometer ( Thermo Scientific ) at 260/280 and 260/320 nm . According to the international consensus for quantification of Trypanosoma cruzi DNA in Chagas disease patients [39] , Quantitative Real Time PCR Multiplex assays using TaqMan probes were performed targeting the satellite region of the nuclear DNA ( SatDNA ) of T . cruzi and the exogenous internal amplification control ( IAC ) , as described in Duffy et al , 2009 . The qPCR reactions were performed in a final volume of 10 μL containing 1 . 5 μL of DNA template , 5 μL of 2X TaqMan Universal PCR Master Mix ( Applied Biosystems , USA ) , 750 nM of both cruzi1 ( 5′ASTCGGCTGATCGTTTTCGA 3′ ) and cruzi2 ( 5′AATTCCTCCAAGCAGCGGATA3′ ) primers and 50 nM cruzi3 probe ( 5′FAM- CACACACTGGACACCAA-NFQ-MGB 3′ ) specific for T . cruzi SatDNA; 100 nM IAC Fw ( 5′CCGTCATGGAACAGCACGTA3′ ) and IAC Rv ( 5′CTCCCGCAACAAACCCTATAAAT 3′ ) primers and 50 nM IAC Tq probe ( 5′ VIC-AGCATCTGTTCTTGAAGGT-NFQ-MGB 3′ ) . Cycling conditions were a first step at 95˚C for 10 min followed by 40 cycles at 95°C for 15 seconds and 58°C for 1 minute . The amplifications were carried out in a ViiA7 Real-Time PCR System ( Applied Biosystems , USA ) . Standard curves for the absolute quantification were constructed by serial dilution of DNA , extracted from 1 x 106 trypomastigotes of T . cruzi ( Dm28c-luc and Tulahuén strain ) , ranging from 105 to 0 . 5 parasite equivalents ( par . eq ) . Normalization of the parasite load was performed by tissue mass , after the absolute quantification of T . cruzi by real time qPCR and results were expressed as parasite equivalents/tissue mass ( g ) . Kruskal-Wallis ( Dunn’s post-test ) or Mann-Whitney tests were used for the statistical analyses . P values < 0 . 05 were considered statistically significant . Tests were performed using GraphPad Prism 5 .
Mice orally infected with T . cruziDm28c-luc were examined for blood parasitemia during the acute phase of infection . Peripheral blood parasites started to be detectable at 7 dpi , with a peak of parasitemia at 11 dpi . At later moments , the number of circulating parasites gradually decreased ( Fig 1 ) . In order to determine the anatomical route of parasites entrance after OI , mice were infected and evaluated by bioluminescence imaging . At 15 and 60 min after OI , mice were placed inside the IVIS Lumina chamber and the images were obtained in ventral ( upper panels ) and lateral ( lower panels ) position ( Fig 2 ) . Detection of bioluminescence images after 15 min of OI showed that all infected mice analyzed had highest intensity of bioluminescence in the head region , concerning the mouth , nose and eyes . Although less intensive , bioluminescence was also observed in the neck , thorax and at the abdominal region . Bioluminescence signals were consistently observed from either ventral or lateral viewpoints ( Fig 2A and 2B ) . One hour after infection , the major bioluminescence image detected remained in the head region ( Fig 2C and 2D ) . To confirm luciferase activity in living trypomastigotes , 5x104 Dm28c-luc T . cruzi parasites were incubated in vitro with medium or D-luciferin in 24 well plate ( black circle ) . Medium or D-luciferin ( 150 μg/mL ) substrate was added to the well and , after 5 min of incubation , images were acquired . As demonstrated in S3 Fig , luminescent signals were only detected in D-luciferin treated parasites . Moreover , as in vivo controls , non-infected mice were treated with D-luciferin and bioluminescent signal analyzed . S4 and S5 Figs show that , in absence of T . cruzi infection , D-luciferin was incapable to promote bioluminescent signal . For ex vivo evaluation of parasites in specific organs , mice were euthanized at 15 and 60 min and 48 hours after OI . The selected head tissues ( nasomaxillary region , mandible region , cheek muscle , tongue and eyes ) and gastrointestinal tract ( esophagus , stomach , small and large intestine ) were excised . The ex vivo evaluation of dissected organs and tissues by bioluminescence imaging confirmed the in vivo bioluminescent T . cruzi foci , as most of the signal detected was localized in the head , specifically in the nasomaxillary region ( including areas of the nose , nasal cavity and upper oral cavity ) ( Fig 3A and 3B ) . A slight bioluminescence signal was observed in the cecum and mandible region in one single animal , 15 and 60 min after infection , respectively ( Fig 3B ) . Furthermore , no luminescent signal was observed in tongue , eyes , cheek muscle , stomach and small intestine at this time ( Fig 3B ) . At 60 min and 48 hours after OI , ex vivo bioluminescence imaging of the heart , brain , spleen , liver , male sex organs , lung and salivary gland was negative ( Fig 3C ) . Taken together , our data suggests that the primary site of T . cruzi invasion due to OI is located at the upper region of the oral cavity , specifically at the nasomaxillary region . To exclude the possibility of an intranasal contamination in our oral infection protocol , mice were inoculated with black ink suspensions at the oral cavity or intranasally . As observed in S2 Fig orally inoculated mice after 5 min showed ink labeling in the tongue and the oral cavity , but were negative in the nasal cavity . In contrast , the intranasal inoculation clearly labeled the nasal cavity ( S2 Fig ) . To have an overview of parasite distribution at different stages of infection , OI mice were analyzed at 7 dpi , an early stage of infection when blood parasites started to be detected , and at 21 dpi , a late point of the acute phase allowing a better analysis of parasite distribution and the target tissues . On 7 dpi , bioluminescent signal was detected in the head , neck and abdomen . It is noteworthy that the head region ( mouth , nose and eyes ) remained the major focus of bioluminescence ( Fig 4 ) . At 21 dpi , infection was dispersed trough the animal body , including head , ears , abdomen , genital region and thorax . Interestingly , at this moment , the genital region showed to be an important focus of bioluminescence signal ( Fig 4 ) . To accurately identify the infected tissue , images of individual organs were captured at 7 and 21 dpi . Dissected tissues comprise the nasomaxillary region , palate , mandible , tongue , eyes , cheeks muscle , esophagus , stomach , small and large intestines , mandibular lymph nodes , salivary gland , heart , lung , spleen , liver , brain , pituitary gland , mesenteric fat and lymph nodes and male sex organ , including preputial glands , testicles , epididymis fat and penis . To better evaluate the nasomaxillary region , we removed the hard and soft palate exposing nasal septum and nasal cavity . Ex vivo evaluation of dissected organs and tissues at 7 dpi demonstrated that high bioluminescent signal remained at the nasomaxillary region of the mice ( Fig 5A and S6 Fig ) . Furthermore , after removal of the entire palate , nasal cavity and nasal septum region showed the major bioluminescence signal ( Fig 5A ) . Light foci were also detected in the palate in 75% of OI mice , shown in Table 1 , which describes the percentage T . cruzi-positive tissues analyzed ( Fig 5A and Table 1 and S6 Fig ) . Interestingly , at this moment of infection , images of T . cruzi were detected in the brain , located in the olfactory bulb region ( Fig 5C and S6 Fig ) . Bioluminescence was also detected in the cheek muscle , mandibular lymph nodes and mandible in 50% of OI mice ( Fig 5A and 5C and Table 1 and S6 Fig ) and 66 . 6% of spleens ( Fig 5D and Table 1 ) . A slight bioluminescence signal was observed in the esophagus , liver , large and small intestines , mesenteric fat and lymph nodes ( Fig 5B and 5D and S6 Fig ) . Bioluminescent foci were also detected in male sex organs , specifically in the testicle and epididymis fat in 33 . 33% of OI mice ( Fig 5E and Table 1 and S6 Fig ) . The bioluminescence signal was undetected at this time in the tongue , eyes , stomach , pituitary gland , salivary gland , lung and heart ( Fig 5A , 5B , 5C and 5F and S6 Fig ) . In agreement with initial bioluminescent images , a large number of T . cruzi Dm28c-GFP amastigote nests are detected in the nasal cavity of OI mice at 6 dpi ( Fig 6 ) . At 21 dpi , bioluminescence was clearly observed in the nasomaxillary region , palate , mandible region , cheek muscle , esophagus , mandibular lymph nodes , spleen , liver , mesenteric fat and lymph nodes and male sex organ ( Fig 5A , 5B , 5C , 5D and 5E and S7 Fig ) . The major affected tissues and organs in the genital region were penis and preputial gland ( Fig 5E ) . In addition , tissues such as the salivary glands , heart and lung started to reveal parasite presence at this moment ( Fig 5C and 5F and S7 Fig ) . At this time of infection , we also observed an increased signal of bioluminescence in the gastrointestinal tract , mostly in the stomach , intestines and mesenteric fat ( Fig 5B and 5D and S7 Fig ) . Bioluminescence signal was observed in 75% of the intestines analyzed and in 50% of stomach and esophagus ( Fig 5B and Table 1 and S7 Fig ) . Finally , at 21 dpi , the ex vivo evaluation revealed that parasites were disseminated to different organs of the body . In conclusion , at 7 and 21 dpi , T . cruzi spreads to other parts of the body , infecting other organs . The persistence of bioluminescence signal emitted from the nasomaxillary region suggested the existence of a general maintenance of parasite proliferation in this region . In contrast to the classical techniques , bioluminescence imaging is able to identify small foci of infection in the whole animal , but , in some cases , bioluminescent signal can be under detection limits . Quantitative real-time PCR ( qPCR ) is an accurate technique to evaluate the presence of parasites in tissues . To examine the parasite burden in target tissues , we collected tissues from orally infected mice at 60 min , 7 and 21 dpi and performed qPCR to compute parasite load . Initially , tissues of the oral cavity , the gastrointestinal tract and adjacent regions , such as the nasal cavity , tongue , palate , mandibular lymph nodes , esophagus , stomach , large and small intestines were all analyzed by qPCR . Consistent with the bioluminescence results observed in the nasomaxillary region at 60 min ( Fig 3B ) and 7 dpi ( Fig 5A ) , T . cruzi foci were detected in elevated numbers at the nasal cavity by qPCR . The first hour after infection showed T . cruzi Sat DNA detection in the nasal cavity among 80% of OI mice , with parasite quantification up to 560 parasite equivalents/g ( par . eq . /g ) ( mean of 180 ) ( Fig 7A and Table 1 ) . Parasite amplification was also detected in the esophagus , stomach , small intestine and large intestine ( Fig 7A ) , although these tissues were negative by bioluminescence imaging ( Fig 3B ) . Interestingly , at 60 min , SatDNA detection was observed in one OI mouse at the esophagus , small intestine and large intestine ( Fig 7A ) . Furthermore , T . cruzi SatDNA was detected in 75% of the analyzed OI mice in the stomach and mandibular lymph nodes at 60 min , with T . cruzi quantification up to 191 . 1 ( mean of 52 . 0 ) and up to 1 . 63 ( mean of 1 . 0 ) par . eqs . /g , respectively ( Fig 7A and Table 1 ) . In addition , SatDNA T . cruzi quantification in the nasal cavity was much higher at 7 dpi , ranging from 6 . 2x103 to 7 . 5x106 par . eqs . /g ( mean of 2 . 2x106 ) ( Fig 7A ) . In this time points after infection , nasal cavity showed the highest parasite load among the analyzed tissues . Interestingly , mandibular lymph nodes also showed high parasite loads , ranging from 31 . 2 to 6300 par . eqs . /g ( mean of 3 . 5 x 103 ) ( Fig 7A ) . It becomes evident that the mean parasite load detected in the nasal cavity was 103 times higher than in the other organs ( Fig 7A ) . At 21 dpi , due to parasite dissemination , high levels of par . eqs . /g were detected in all tissues ( Fig 7A ) , in accordance to the bioluminescence imaging . In addition , it was not possible to detect parasite presence in the palate and tongue due to PCR inhibition ( no amplification of the qualitative exogenous internal amplification control ( IAC ) . To evaluate parasite dissemination throughout the body and to determine if there was any correlation with the bioluminescence signal , we analyzed parasite load in the pituitary gland , brain , heart , spleen and liver at 60 min , 7 and 21 dpi . Ex vivo imaging of the brain , spleen and liver did not reveal any bioluminescence signal at 60 min ( Fig 3C ) . As expected , qPCR results confirmed the bioluminescence imaging and T . cruzi DNA amplification was undetectable in these organs ( Fig 7B ) . Low amount of parasite detection was observed in the heart of a single animal ( 0 . 8 par . eq . /g ) , at 60 min ( Fig 7B ) . At 7 dpi , T . cruzi SatDNA was detected in the heart , spleen , liver and pituitary gland ( Fig 7B ) . Finally , at 21 dpi , parasite dissemination favored T . cruzi detection in all analyzed tissues ( Fig 7B ) . T . cruzi is highly genetically diverse and currently six Discrete Typing Units ( DTU ) , TcI to TcVI , are recognized [38] . TcI , TcII , TcIII , TcIV and TcVI genotype has been reported in oral transmission of acute Chagas disease [18–25] . Because of this biological polymorphism , different strains may present tropisms for distinct tissues ( cardiac muscle , myoenteric plexuses in the esophagus and rectum and others tissues ) and consequently differences in the clinical forms of the disease [40] . Due to this difference tissues tropism in T . cruzi strains , qPCR of gastrointestinal tract , nasal cavity and heart tissues from OI mice using a different strain ( Tulahuén strain , DTU—TcVI ) was performed to compute parasite load . Tissues were collected at 60 min and 7 dpi from OI mice . Consistent with the qPCR results observed in OI mice with Dm28c-luc strain ( DTU- TcI ) ( Fig 7 ) , sixty minutes after infection , T . cruzi foci was detected in elevated numbers at the nasal cavity in OI mice with Tulahuén strain ( DTU- TcVI ) . T . cruzi presence was also detected in the stomach at this time point ( Fig 8 ) . However , at 7dpi the highest SatDNA T . cruzi quantification in the nasal cavity suggested intense parasite growing in this tissue , in contrast with the stomach ( Fig 8 ) . Altogether , these data confirms that the nasal cavity is the preferential site T . cruzi infection and expansion in oral infection , regardless of DTU strain specificity ( Fig 8 ) . Interestingly , the percentage of OI mice with blood parasitemia at 7 and 21 dpi was 25% and 56% , respectively . However , by assessing the percentage of infected mice in these same points of infection using bioluminescent imaging ( evaluating the presence of the bioluminescence signal ) and qPCR ( evaluating T . cruzi SatDNA amplification in tissue ) , 100% of OI mice showed both bioluminescent signal and T . cruzi SatDNA amplification in tissues at 7 and 21 dpi . We conclude that the parasitemia is less sensitive to determine the percentage of infection in animals inoculated by the oral route in our model , since the bioluminescence techniques and qPCR showed signs of active infection in mice in these times . Taken together , bioluminescence and qPCR data showed that at the first moments after OI , T . cruzi is able to infect nasal cavity , mandibular lymph nodes and stomach . However , nasal cavity is the major focus for parasite permanence and replication . These results show parasite distribution kinetics , thus suggesting that T . cruzi may disseminate to other organs ( pituitary gland , brain , heart and liver ) from the nasal cavity ( Fig 9 ) .
In the past years , the number of oral Chagas disease outbreaks in Brazil and other Latin America countries are increasing . Presently , the most common pathway of T . cruzi infection in the Brazilian Amazon is the oral route and , from 2000 to 2013 , this pathway of infection was responsible for 70% of acute cases in Brazil [4 , 6] . These outbreaks were associated with ingestion of contaminated food and beverage[11 , 41] . Orally infected patients are frequently highly symptomatic , presenting long-lasting fever , headache , facial and bipalpebral edema , lower limb edema , myalgia , abdominal pain , meningoencephalitis and the classical cardiac involvement [6 , 9 , 42–44] . Analysis of distinct outbreaks demonstrated that the mortality rate of affected patients in the first two weeks of infection is estimated at 8–35% , considerably higher than the mortality rate from the classical vectorial transmission ( < 5–10% ) . The higher mortality rate can be associated with elevated prevalence of cardiac pathology and absence of an earlier diagnosis [14 , 43] . Despite being an important route of infection , there are few studies regarding T . cruzi oral transmission in the literature . Previous data , using histopathology studies , showed signs of a possible T . cruzi penetration in the oral , esophageal , gastric , and intestinal mucosa with a local reaction with eosinophilia , infiltrated lymphocytes and monocytes after oral infection in dog [45] . In contrast , some authors have demonstrated that orally T . cruzi infected mice involves gastric mucosal invasion for the systemic infection . It has been shown , by histological analysis , that T . cruzi infection is observed in the gastric mucosal epithelium . However , parasites were not detected in other areas throughout the gastrointestinal tract , like esophagus and oropharynx . These authors observed that T . cruzi initiates systemic parasite dissemination as a consequence of an oral infection by gastric mucosal invasion [27] . By using intragastric or intrapharyngeal challenge , another group observed that T . cruzi glycoproteins , such as gp82 and gp30 , are important for gastric invasion . Prior to invasion , the parasite binds to gastric mucin using these glycoproteins that allow T . cruzi to invade and replicate in the stomach [29 , 31 , 46 , 47] . We have previously shown that the site of inoculation , through the oral cavity ( OI ) or the stomach ( by gavage-GI ) , differentially affects host immune response and mortality . OI developed a highly severe acute disease with higher parasitemia , TNF serum levels , hepatitis and mortality rates when compared to GI [15] , suggesting that the inoculum site is a key factor in Chagas disease progression , possibly modulating local immune mechanisms that impacts in the systemic immunity . In addition , intraperitoneal ( IP ) , intravenous and subcutaneous infection shows higher infection rates and mortality than mucosal ones ( oral , intragastric , intrarectal , genitalia or conjunctival ) [33 , 48 , 49] . Here , we searched for the site of parasite entry in the host in orally infected mice . It is well accepted that bioluminescence imaging is an innovative technique that helps the identification of parasite distribution in distinct tissues , allowing a panoramic comprehension of T . cruzi dissemination in the entire animal body [34] . By using bioluminescence technique , we demonstrated that , few minutes after OI , parasites are concentrated in the head region , specifically in the nasomaxillary region ( upper oral cavity , nose and nasal cavity ) . In a lesser intensity , parasites were also detected in the thorax and at the abdominal region . In addition , T . cruzi was detected in the nasal cavity and draining lymph nodes at 60 min post-infection by qPCR , confirming that the nasal cavity has the highest parasite load among affected tissues , in contrast with the stomach and intestines . In the same way , two and seven days after inoculums , images revealed that the nasomaxillary region remains as the major focus of infection . Interestingly , facial edema is a common feature in affected patients , being described in 57–100% of cases in Brazilian outbreaks of oral infection [6] . Nevertheless , a contaminated sugar cane juice outbreak of oral infection in Paraiba State ( Brazil ) revealed the presence of bilateral palpebral edema in 92% of orally infected patients [44] . An outbreak with contaminated fresh guava juice in Venezuela showed that 40% of hospitalized patients had facial edema [50] . Moreover , another outbreak in Venezuela involving five members of the same family described that all patients showed edema in the face , mouth and cheek , and edema and paraesthesia of the tongue [51] . Furthermore , other clinical finding in the face region , such as gingivitis and dry cough has been attributed to the penetration of the parasite throughout the oral or pharyngeal cavity [6 , 43] . Interestingly , T . cruzi infection and gingival inflammatory foci has been shown at the oral cavity from a chronic Chagas disease patient [52] . These findings might be associated to our present data , which describe for the first time the nasomaxillary region as the main target tissue following oral T . cruzi infection . The mouth can be targeted by various infectious diseases , including viral , bacterial , and fungal . The oral cavity contains distinct mucosal surfaces composed of sophisticated structures and molecules , such as mucins , in which the microorganisms can bind and colonize the environmental cells [53] . It has been shown that the soft palate is an important site of infection and adaptation of influenza viruses . The soft palate infection may contribute to airborne transmission by providing a mucin-rich microenvironment and perhaps the initial region of infection . In fact , the expression of α 2 , 3 sialic acids and viral hemagglutinin ligand is detected on the soft palate in the regions of the oral surface , mainly at the basal cells , and the nasopharyngeal tissues from humans and ferret [54] . Interestingly , α 2 , 3 sialic acids are the main molecule involved in T . cruzi transialidase mediated binding . Transialidase has been considered as an important virulence factor of T . cruzi , due to its ability to reduce host cell immune response and mediate T . cruzi and host cells adhesion [55] . It has been shown that transialidase have adhesive capacity with host sialoglycans , generating “eat me” signals in epithelial cells , facilitating the parasite entry into non-phagocytic cells [56] . Based in these previous studies we can hypothesize that oral T . cruzi infection may occur on the palate , through the interaction of transialidase molecules in the parasite membrane with α 2 , 3 sialic acids residues present in the soft palate [54] . Other molecules may also be involved in T . cruzi adhesion with oral cavity cells , such as mucins and glycoproteins such as gp82 , gp30 , gp90 [57] . Seven days after infection reveals that nasal cavity , nasal septum region , palate , cheek muscles , mandible and mandibular lymph nodes are target tissues of the parasite . Surprisingly , the mean parasite load detected by qPCR in the nasal cavity of OI mice with Dm28c-luc ( DTU- TcI ) , is 103 times higher than other tissues . This predominant T . cruzi detection in mouse nasal cavity is also observed in OI mice with other T . cruzi strain ( Tulahuén strain , DTU- TcVI . Altogether this data suggesting that nasal cavity is the main site of T . cruzi maintenance and replication following oral infection . In the line with our findings , Giddings and colleagues demonstrated that nasal cavity is the principal site of parasite infection and replication after conjunctival T . cruzi infection with Tulahuén strain ( DTU-TcVI ) . The predominant invasion occurs through epithelia lining nasal cavity and nasolacrimal ducts . T . cruzi initially replicates within these sites and further spread to draining lymphoid organs with systemic dissemination . In the nasal cavity , parasites were detected in areas such as the submucosa of the epithelial lining the nasal septum , nasal mucosa-associated lymphoid tissue and bone marrow of the facial bones surrounding the nasal cavity [58] . Mice infected with the Tulahuén strain of T . cruzi by the intranasal route shows higher brain parasitism than mice infected by the subcutaneous pathway [49] . It was also observed that parasites gain access to the brain via olfactory nerve tissues . The authors proposed that , within the first moments , parasites invade nasal cavity cells , multiply and then migrate to the brain via the olfactory tissues [49] . Supporting this idea , we have observed that after infection and multiplication of parasites in the nasal cavity of orally infected mice , bioluminescence imaging of T . cruzi at 7 dpi were detected in the bulbous olfactory region of the brain in orally infected mice . Interestingly , parasites were also detected by qPCR in the pituitary gland at 7 and 21 dpi , but not in the central region of the brain at 7 dpi , turning positive at 21 dpi . Thus , we propose that brain infection is subsequent to the nasal cavity and the olfactory nerve tissue commitment . Corroborating our results of T . cruzi detection in the pituitary gland and in the brain , a previous study detected the parasite kinetoplast DNA in the pituitary gland during the acute phase [59] . Despite bioluminescence imaging is able to identify small foci of infection in the tissues and in the whole animal , this technique has limitations and some aspects that should be considered [34 , 37] . The detection sensitivity is dependent on several factors , such as the level of luciferase expression , type of tissues , depth of labeled cells within the body and sensitivity of the detection system . Thus , in some cases , bioluminescent signal can be under the detection limit [37 , 58–60] . As we have observed in our model , the percentages of T . cruzi-positive analyzed samples by bioluminescence and qPCR are different in some tissue ( Table 1 ) . Indeed in both pituitary gland and the heart at 7 dpi the presence of T . cruzi was not detected by bioluminescence , however it was detected by qPCR . This can be explained by higher sensibility of the qPCR compared to bioluminescence , as the qPCR allows detection of at least 0 . 5 equivalents parasites [61] and bioluminescence does not . T . cruzi infection has been associated to disturbances in immune-endocrine systems , leading to activation in the hypothalamus–pituitary–adrenal ( HPA ) axis and high glucocorticoid production . The high glucocorticoid secretion seems to limit the excessive production of pro-inflammatory cytokines , protecting the host from tissue injury and metabolic alterations . Furthermore , the elevated glucocorticoid production in the acute phase is involved in thymus atrophy and immature T CD4+CD8+ cell apoptosis [60 , 61] . In Fig 4 we observe that animals analyzed showed differences in bioluminescence signal . Some animals present less intensity of bioluminescence signal in the head , demonstrating that these animals have a lower parasitism in this region in that time point . Note that with 21dpi these same animals presented a larger signal in the region in the nasal cavity , which shows that they may have different evolution kinetics . This does not exclude the fact that they were infected and presented high intensity of signal at the same regions as the others , but not exactly at the same time . These differences between mice in T . cruzi infection can be observed also in parasitemia ( Fig 1 ) or in parasitism load at different tissues ( Figs 7 and 8 ) . Interestingly , we can also see in Fig 7A a large difference in parasite load in the nasal cavity with 7 dpi between animals analyzed by qPCR , although not analyzed in the same animals bioluminescence . Interestingly , with the development of the infection and spread of T . cruzi , we observed the presence of bioluminescence signal mainly in the male sexual organs ( testicles , epedidimal fat , preputial gland and epididymis ) . As described in previous studies , male sex organs are frequently infected in T . cruzi experimental infections , including testes , penis , epididymis ducts and accessory sex glands ( prostate , preputial gland and seminal vesicle ) of mice infected by IP route [62–65] . In humans some cases of orchitis due to gonadal parasitism during the acute phase of Chagas disease have been described . Furthermore , clinical manifestations of sexual dysfunction such as decreased of libido , erection and ejaculation were reported [66–69] . Although the possibility of sexual transmission of T . cruzi has been suggested , few studies have been published on this theme . In the acute phase of experimental infection , sexual transmission has been described , but with low transmission rates in uninfected and immunosuppressed females through males infected by IP route [70] . Ribeiro and colleagues evaluated the potential of sexually transmission of T . cruzi in the chronic phase with infected males to uninfected females and vice versa by using mice infected via IP route . After copulation , 100% of the animals , both males as females seroconverted ( ELISA and IF ) and presented T . cruzi DNA in the heart and skeletal muscle [71] . In the present work , we have identified the site of T . cruzi initial invasion and replication after infection through the oral route . Our results demonstrated that oral infection involves T . cruzi passage through the mouth into the nasal cavity , where parasite replication occurs . Then , nasal cavity parasites might disseminate through the olfactory nerve tissues and blood to distant tissues ( Fig 9 ) . Thus , the proper oral cavity operates as a potential source of infection , and places the regional innate and adaptive immune systems as central players in the disease progression . Therefore , the elucidation of the tissue/organs targets and the molecular components regulating the establishment of oral T . cruzi infection is critical to understanding the pathogenesis of this current form of Chagas’ disease .
|
Oral transmission of Trypanosoma cruzi associated with food/beverage consumption is presently an important route of infection in Brazil and Venezuela . Colombia , Bolivia , Argentina and Ecuador have also reported to have acute cases of Chagas disease transmission through the oral route . Significant studies about this form of T . cruzi infection are largely lacking . In addition to the classic cardiac involvement , orally-infected patient progress to a highly symptomatic disease and increased mortality rate ( 8–35% ) , surpassing the calculated mortality produced by the disease resulting from the biting of infected insect vectors ( 5–10% ) . Here , we explored by in vivo bioluminescent images , qPCR and fluorescence microscopy the primary site of parasite entry and multiplication in oral infection ( OI ) . Our results clearly demonstrated that the oral cavity is the main T . cruzi target region in OI , leading to parasite multiplication at the nasal cavity and parasite dissemination to the brain and peripheral tissues . Interestingly , facial edema , paraesthesia of the tongue , gingivitis and dry cough were already described in affected patients . These findings might be associated to our present data , which describe for the first time the nasomaxillary region as the main target tissue following oral T . cruzi infection .
|
[
"Abstract",
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"Methods",
"Results",
"Discussion"
] |
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"medicine",
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"parasitic",
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2017
|
Unraveling Chagas disease transmission through the oral route: Gateways to Trypanosoma cruzi infection and target tissues
|
Hantaan virus ( HTNV ) causes a severe lethal haemorrhagic fever with renal syndrome ( HFRS ) in humans . Despite a limited understanding of the pathogenesis of HFRS , the importance of the abundant production of pro-inflammatory cytokines has been widely recognized . Interleukin 33 ( IL-33 ) has been demonstrated to play an important role in physiological and pathological immune responses . After binding to its receptor ST2L , IL-33 stimulates the Th2-type immune response and promotes cytokine production . Depending on the disease model , IL-33 either protects against infection or exacerbates inflammatory disease , but it is unknown how the IL-33/ST2 axis regulates the immune response during HTNV infection . Blood samples were collected from 23 hospitalized patients and 28 healthy controls . The levels of IL-33 and soluble ST2 ( sST2 ) in plasma were quantified by ELISA , and the relationship between IL-33 , sST2 and the disease severity was analyzed . The role of IL-33/sST2 axis in the production of pro-inflammatory cytokines was studied on HTNV-infected endothelial cells . The results showed that the plasma IL-33 and sST2 were significantly higher in patients than in healthy controls . Spearman analysis showed that elevated IL-33 and sST2 levels were positively correlated with white blood cell count and viral load , while negatively correlated with platelet count . Furthermore , we found that IL-33 enhanced the production of pro-inflammatory cytokines in HTNV-infected endothelial cells through NF-κB pathway and that this process was inhibited by the recombinant sST2 . Our results indicate that the IL-33 acts as an initiator of the “cytokine storm” during HTNV infection , while sST2 can inhibit this process . Our findings could provide a promising immunotherapeutic target for the disease control .
Hantaan virus ( HTNV ) is a member of the Bunyaviridae family [1] . HTNV can cause severe lethal haemorrhagic fever with renal syndrome ( HFRS ) in humans , which is characterised by increased capillary permeability and thrombocytopenia . At present , the pathogenesis of HFRS remains unclear . Previous reports suggest that cytokine storm is a potential mechanism of HFRS pathogenesis [2] . Increased cytokines , such as IL-6 , IL-8 , and CXCL10 , have been found in the serum , plasma , urine , and tissues of patients with hantavirus infections and correlate with the severity of the disease [3–7] . It has also been suggested that the viral infection of endothelial cells plays an important role in capillary leakage [8] , which is triggered by cytotoxic CD8+ T cells and augmented by pro-inflammatory cytokines [2] . Interleukin-33 ( IL-33 ) , a new member of the IL-1 cytokine family , serves as a ligand for the ST2 receptor [9] . Recent studies have suggested that IL-33 is specifically released during necrotic cell death but is intracellular during apoptosis . Because of these properties , IL-33 is identified as an “alarmin” and is defined as a member of danger-associated molecular pattern ( DAMP ) molecule for alerting the immune system after infection or injury [10] . As a potent inducer of the T-helper 2 ( Th2 ) immune response , IL-33 promotes the production of Th2-associated cytokines , such as IL-4 , IL-5 , and IL-13 , mostly released from polarized Th2 cells [9] . In addition to Th2-related effects , IL-33 also induces inflammatory responses in endothelium [11] and epithelium [12] . The ST2 gene , a member of the IL-1RL1 superfamily , is known to encode at least 3 isoforms of ST2 proteins by alternative splicing: a membrane-anchored long form ( ST2L ) , a secreted soluble form ( sST2 ) , and a membrane-anchored variant form ( ST2V ) [13–14] . sST2 , serving as a decoy receptor for IL-33 , can neutralize the function of IL-33 . ST2L has been reported to be constitutively expressed by mast cells as well as Th2 cells . Upon binding with IL-33 , ST2L forms a complex with the IL-1R accessory protein ( IL-1RAcP ) , recruits the adaptor protein MyD88 , activates MAP kinases ( MAPK ) and NF-κB pathways , and promotes the production of inflammatory mediators [9] . Numerous studies have reported the expression and function of IL-33/ST2 signalling in various diseases . IL-33/ST2 overstimulation has been implicated in airway inflammatory diseases [15–17] , autoimmune diseases [18] , viral infection diseases [19–20] , and many other diseases [21–24] , suggesting an important role for IL-33/ST2 in the development of inflammatory pathologies . However , the mechanism by which the IL-33/ST2 axis exerts its immunomodulatory effects in HFRS has not yet been elucidated . In this study , we have quantified for the first time the plasma levels of IL-33 and sST2 in HFRS patients , analyzed the relationships between IL-33 , sST2 , and disease severity-indicating parameters in vivo , and explored the role of IL-33/ST2 in regulating immune response in vitro during HTNV infection . We found that elevated plasma IL-33 and sST2 levels were associated with the development of HFRS . Our in vitro examination indicated that IL-33 could enhance the production of pro-inflammatory cytokines in HTNV-infected endothelial cells and that this process could be inhibited by the recombinant sST2 . Taken together , our data suggested that the IL-33/ST2 axis may function as an inflammatory regulator during HTNV infection .
The study was approved by the Institutional Review Board of the Fourth Military Medical University . Written informed consent was obtained directly from each subject for the collection of samples and subsequent analysis . Enrolled in the study were 23 hospitalised HFRS patients from Tangdu Hospital of the Fourth Military Medical University ( Xi’an , China ) from October 2012 to January 2013 ( see Table 1 ) . The clinical diagnosis of HFRS was confirmed by the detection of IgM antibodies against HTNV nucleocapsid protein . A total of 28 healthy donors were included as the normal control . The plasma samples were collected and stored as previously described [25–26] . Based on the classically defined 5 stages of HFRS , we classified the HFRS patients in this study into acute phase ( including febrile , hypotensive , and oliguric stages ) and convalescent phase ( including diuretic and convalescent stages ) [7 , 27–28] . The plasma viral load of the entire subjects group , an important indicator of disease severity , was determined using a quantitative 1-step real-time reverse-transcriptase polymerase chain reaction ( RT-PCR ) assay published previously [26] . Human umbilical vein endothelial cells ( HUVECs ) were prepared by a previously described method [29] . The HTNV strain 76–118 and inactivated HTNV ( mock virus ) control were prepared and stored in our lab as described [7] . For all infections , the virus was allowed to adsorb to HUVECs at a multiplicity of infection ( MOI ) of approximately 1 in serum-free EGM maintenance medium for 2 h at 37°C . The cells were then washed and incubated in EGM growth medium with 10% fetal bovine serum . The proportion of infected HUVECs was tested using immunofluorescence . At 48 hours postinfection , over 90% of the HUVECs expressed viral nucleocapsid protein in the cytoplasm . HUVECs were seeded 24 h before treatment . When the cell confluence up to 60%-70% , the cells were infected with HTNV/mock virus ( MOI = 1 ) for 48 h or treated with interleukin-33 ( IL-33 , R&D system , USA ) for 6 h; alternatively , the cells were pre-infected with HTNV/mock for 48 h and then treated with 20 ng/ml IL-33 for another 6 h . To assess the role of ST2 and its signalling pathways in the induction of pro-inflammatory cytokines via IL-33 stimulation , HUVECs were exposed to human recombinant sST2 ( R&D Systems , USA ) or inhibitors ( Calbiochem , USA ) of the indicated signalling pathways at different concentrations for 2 h prior to the stimulation indicated above . The amounts of IL-33 and sST2 present in HFRS patient plasma were determined using ELISA kits from eBioscience ( USA ) and RayBiotech ( USA ) , respectively , according to each manufacturer’s instructions . For the specific knockdown of ST2 and p65 , double-stranded small interfering RNAs were synthesised by Gene Pharma ( China ) using the sequences shown in the supporting information ( S1 Table ) . siRNA transfection into HUVECs was performed using Lipofectamine 3000 ( Invitrogen , USA ) according to the manufacturer’s protocol . The efficiency and confirmation of the knockdown was identified by determining the mRNA and protein levels of the target gene after transfection of the corresponding siRNA or the mock vector into HUVECs . Total RNA from HUVECs was extracted using TRIzol ( Invitrogen , USA ) according to the manufacturer’s protocol , and 1 μg was used for cDNA synthesis ( Takara , Japan ) . A quantitative analysis of mRNA expression was determined by quantitative real-time PCR using the SYBR Green ( Takara , Japan ) detection method . The specific primers for each gene are shown in the supporting information S2 Table . The reactions were analysed using a BIO-RAD system ( CFX96 Real-Time System ) . The delta delta Ct method was used to calculate each gene of interest , and each gene was normalized to the housekeeping gene GAPDH and presented as copies of the normal medium control for HUVEC studies . The cells were exposed to virus or IL-33 for predetermined periods of time . The cells were then washed with PBS and lysed in RIPA buffer . For western blotting , 20 μg of total protein from each sample was subject to a stacking gel and separated by a 10% SDS-PAGE separating gel using the Tris-glycine system and then transferred onto nitrocellulose membranes ( Millipore , USA ) . The membranes were blocked in 3% BSA and then probed overnight at 4°C with antibodies specific , respectively , for ST2 ( Abcam , UK ) , p65 , p-IKK , IKK , p-IκB , IκB , p-JNK , JNK , p-ErK , ErK , p-p38 , p38 , and β-tubulin ( Cell Signaling Technology , USA ) , and GAPDH ( Ambion , USA ) . The membranes were washed and incubated with an HRP-conjugated goat anti-mouse antibody or HRP-conjugated goat anti-rabbit antibody ( Pierce , USA ) . After washing the membranes , the blots were developed using electrochemiluminescence ( Alpha Innotech , USA ) . HUVECs from the different treatment groups were collected and resuspended at a concentration of 107cells/ml in flow buffer ( PBS + 1% FCS + 0 . 1% NaN3 ) . Fc receptors on the HUVECs were blocked by the addition of normal goat serum . Then , 106 cells were incubated for 30 minutes at 4°C with the anti-ST2 monoclonal antibody ( Abcam , UK ) . After washing the cells twice with flow buffer , the cells were stained with phycoerythrin ( PE ) -conjugated goat anti-mouse secondary antibody ( BD Biosciences , USA ) and with an isotype antibody as a negative control . The cells were washed twice with flow buffer , and 200 μL of 4% formalin was added to fix the cells . A minimum of 100 , 000 cells were acquired using a BD FACS Calibur Flow Cytometer ( Beckman Coulter , USA ) and analysed using Flowjo software ( Treestar , USA ) . The pNF-κB-luc plasmid was purchased from Beyotime , China . HUVECs were seeded in a 24-well plate . When the confluence was approximately 70% , the cells were transfected with 1 . 6 μg/well of pNF-κB-luc plasmid and 5 ng/well of the pRL-TK plasmid using jetPEI-HUVEC Polyplus Transfection reagent ( Polyplus , USA ) according to the manufacturer’s protocol . After 4 hours , the HUVECs were infected with HTNV ( MOI = 1 ) for an additional 48 h and then treated with 20 ng/ml IL-33 for 6 h . The luciferase activity in each sample was then detected using the Dual-luciferase reporter assay system ( Promega , USA ) according to the manufacturer’s instructions , and the transfection efficiency was normalised to the Renilla luciferase activity . For data analyses , the medium control was set as 1 . The analysis was performed using SPSS and GraphPad Prism5 software . The statistical significance was determined using one-way ANOVA . The Spearman correlation test was used to test the correlation between the IL-33/sST2 concentrations and clinical parameters . A p value less than 0 . 05 was considered to be statistically significant .
A total of 23 HFRS patients with 59 plasma samples were collected at the acute ( febrile/hypotensive/oliguric ) , or convalescent ( diuretic/convalescent ) phases of the disease . Each patient contained two , three , or four samples collected in different stages . The details of the clinical parameters detected during the hospitalization of the patients are summarized in Table 1 . The mean levels of IL-33 ( Fig . 1A ) and sST2 ( Fig . 1B ) in the HFRS patients were respectively , 3 times or 38 times higher than that in the normal control . The IL-33 ( Fig . 1C ) and sST2 ( Fig . 1D ) contents in HFRS patients in the acute phase were both markedly higher than those in the convalescent phase and in the normal controls ( p < 0 . 001 ) . However , there was no significant difference in IL-33 or sST2 level between the convalescent-phase HFRS patients and the healthy donors ( Fig . 1C-D ) . The individuals’ kinetic data was shown to determine the changes trends of IL-33 ( Fig . 1E ) and sST2 ( Fig . 1F ) levels in each HFRS patient , who expressed much higher levels of IL-33 and sST2 in the early phase . Generally , the level of IL-33 peaked ( 297 . 00 pg/ml ) in the early phase of HFRS and decreased sharply at 10 days after fever onset ( Fig . 1G ) . Although the kinetic changes in sST2 were similar to that of IL-33 , the decrease of sST2 was much more dramatic than that of IL-33 ( Fig . 1H ) . Interestingly , the plasma levels of IL33 and sST2 were also positively correlated , as determined by the Spearman correlation analysis ( r = 0 . 71 , p < 0 . 05 ) ( Fig . 1I ) . Within the first 10 days of fever onset , the ratio of sST2 to IL-33 was 4 . 70 times higher than that after 10 days of fever ( Fig . 1J ) . Spearman correlation analysis revealed that this increasing level of IL-33 was correlated with increasing WBC count ( r = 0 . 28 , p < 0 . 05 ) ( Fig . 2A ) and viral load ( r = 0 . 64 , p < 0 . 05 ) ( Fig . 2B ) and decreasing PLT count ( r = −0 . 44 , p < 0 . 05 ) in HFRS patients ( Fig . 2C ) . Similarly , the sST2 content was correlated with increasing WBC counts ( r = 0 . 54 , p < 0 . 05 ) ( Fig . 2D ) and viral load ( r = 0 . 64 , p < 0 . 05 ) ( Fig . 2E ) and decreasing PLT counts ( r = −0 . 79 , p < 0 . 05 ) in HFRS patients ( Fig . 2F ) . To elucidate the role of IL-33 in modulating the pattern of cytokine production , we evaluated the mRNA expression of pro-inflammatory cytokines and chemokines in primary HUVECs by real-time PCR . Using cells only treated with IL-33 or cells only infected with HTNV as controls , the mRNA expression of IL-1β , IL-6 , IL-8 , CCL2 , CCL20 , CXCL1 , CXCL2 , and CX3CL1 was significantly induced in HUVECs prior to infection with HTNV ( MOI = 1 ) for 48 h and then exposed to IL-33 ( 20 ng/ml ) for 6 h ( Fig . 3 ) . The cells pre-infected with inactive HTNV ( mock virus ) for 48 h did not show the same effect ( Fig . 3 ) . Because previous reports have demonstrated that IL-33 could potently induce the production of Th2-associated cytokines [9] , we also measured the mRNA levels of IL-4 , IL-5 , and IL-13 and found no inducement in our experimental system ( S1 Fig . ) . We investigated whether the ST2 signalling pathway participates in IL-33-mediated inflammatory responses in HUVECs infected with HTNV . Using a non-targeting siRNA as the scramble control , HUVECs were transfected with an siRNA against ST2L for 6 h , and the cells were treated with both HTNV and IL-33 , as indicated above . The mRNA and protein levels of ST2L were determined by real-time PCR ( Fig . 4A ) and western blotting ( Fig . 4B ) , respectively . The induction of pro-inflammatory cytokines by both HTNV infection and IL-33 treatment was significantly inhibited when ST2L was depleted ( Fig . 4C ) . Pre-incubation of HUVECs with soluble recombinant human ST2 protein ( 100 ng/ml ) two hours prior to HTNV infection and IL-33 stimulation , with the recombinant human IgG ( 100 ng/ml ) as the isotype control , resulted in the significant suppression of the production of these pro-inflammatory cytokines and chemokines ( Fig . 4C ) . These results suggested that an ST2-dependent pathway is involved in IL-33-mediated inflammatory responses in HUVECs . Next , we measured the mRNA and protein levels of both sST2 and ST2L in HUVECs . The HUVECs stimulated with both HTNV and IL-33 expressed higher levels of sST2 and ST2L , both at the mRNA ( Fig . 5A-B ) and protein levels ( Fig . 5C-D ) . Our findings suggested that HTNV and IL-33 could synergistically promote the induction of ST2 in HUVECs . We then investigated the signalling pathways involved in the IL-33-stimulated inflammatory response in HUVECs pre-infected with HTNV . Although the signalling pathways activated by IL-33 remain poorly understood , it has been reported that IL-33 could active the NF-κB and MAPK pathways [9] . Therefore , we performed western blotting and found that the phosphorylation of both IKK and IκB was increased in the HUVECs infected with HTNV alone for 48 h and in the HUVECs only treated with IL-33 ( 20 ng/ml ) for 8 min . Interestingly , the levels of p-IKK and p-IκB were notably enhanced when the HUVECs were pre-infected with HTNV for 48 h and then treated with IL-33 ( 20 ng/ml ) for 8 min together ( Fig . 6A ) . Although the total amount of IKK protein was unchanged in the differently treated HUVECs , the total amount of IκB proteins was reduced , whereas the phosphorylation form increased ( Fig . 6A ) . The densitometric analysis of the WB results in Fig . 6B shows the ratios of the protein levels of IKK and IκB relative to the loading control GAPDH . To further confirm the activation of the NF-κB pathway , a dual luciferase assay was performed by transfecting the pNF-κB-luc plasmid into HUVECs . The luciferase activity indicated that the activation of the NF-κB pathway was enhanced in the HUVECs treated with both HTNV and IL-33 ( Fig . 6C ) . We also conducted western blotting to evaluate whether the MAPK pathway is involved in the IL-33 responses in HUVECs and found that the expression of p-JNK , p-ErK , and p-p38 were not enhanced when IL-33 was added in together with HTNV ( Fig . 6D ) . The ratios of the protein levels of JNK , ErK , and p38 relative to the loading control β-tubulin were revealed by the densitometric analysis of the western blot results ( Fig . 6E ) . Our findings indicated that HTNV and IL-33 treatment enhanced the activation of the NF-κB pathway rather than the MAPK pathway . To further verify the role of the NF-κB pathway in the IL-33-mediated inflammatory response , we transfected an siRNA specific to the p65 subunit into HUVECs and demonstrated that the knockdown of p65 ( Fig . 7A ) could significantly impair the production of pro-inflammatory cytokines in HUVECs treated with both HTNV and IL-33 ( Fig . 7B ) . Additionally , pre-treatment of the NF-κB activation inhibitor pyrrolidine dithiocarbamic acid ( PDTC , 100 μM ) markedly suppressed the mRNA expression of these pro-inflammatory cytokines ( Fig . 7C ) . These findings suggested that the NF-κB pathway plays an important role in the regulation of HTNV/IL-33-mediated inflammatory responses in HUVECs .
Our study demonstrated that the higher plasma IL-33 and sST2 levels in the HFRS patients , which were associated with the disease severity-indicating clinical parameters , may exert their pro- and anti- functions in inflammatory response during HTNV infection , respectively . As an alarmin , IL-33 is mainly produced by structural and lining cells , such as endothelial cells , fibroblasts , and epithelial cells , where the first line of host defence against pathogens normally arises [30] . In our study , elevated IL-33 was detected in the plasma of HFRS patients , indicating that HTNV infection might induce cellular damage or necrosis ( Fig . 1A ) . To identify the source of this high level of IL-33 during HTNV infection , we measured IL-33 in HTNV-infected HUVECs . However , we did not detect IL-33 at either the mRNA or protein level ( S2 Fig . ) . Thus , we hypothesised that under in vivo conditions , endothelial cells , epithelial cells , and fibroblasts may undergo necrosis and release IL-33 during HTNV infection . The roles of IL-33 in various diseases have been discussed recently . In mice infected with influenza virus , IL-33 treatment led to significantly reduced inflammation and pathology of the lungs [30] . IL-33 also directly drives protective antiviral CD8+ T cell responses against lymphocytic choriomeningitis virus ( LCMV ) infection in mice [31] . These results present an important protective role of IL-33 in some infectious diseases . However , in Th2-mediated inflammatory diseases , such as asthma , rheumatological diseases , and inflammatory skin disorders , IL-33 appears to have pro-inflammatory effects and exacerbates the diseases [32] . Hantavirus pathology is suggested to be linked to T cell activation , either through the excess secretion of pro-inflammatory cytokines or through the CTL-mediated killing of infected cells [33] . It was also demonstrated that Th1 and CTL subsets , rather than the Th2 subset , preferentially proliferate and differentiate during the course of HFRS [34] . Therefore , whether IL-33 plays a protective role or a damaging role during HTNV infection remains an enigma . In our study , we found that elevated IL-33 was positively correlated with the severity of HFRS ( Fig . 2A-C ) . Our in vitro results suggested that in HTNV-pre-infected HUVECs , IL-33 significantly enhanced the production of pro-inflammatory cytokines ( Fig . 3 ) , rather than the Th2-related cytokines , such as IL-4 , IL-5 and IL-13 ( S1 Fig . ) . Our in vivo study also showed that pro-inflammatory cytokines like IL-6 and IL-8 were elevated in the same HFRS patients’ plasma , especially in their acute phases ( S3 Fig . ) . However , the plasma levels of IL-4 , IL-5 , and IL-13 cannot be detected . Combined with previous reports that IL-33 could increase vascular permeability in HUVECs [35] , we believe that IL-33 may act as an initiator of the “cytokine storm” and contribute to the development of HFRS . However , the role of IL-33 in the regulation of Th1 cells , Th2 cells , NK cells , and mast cells during HTNV infection still needs to be investigated . The ST2/IL-33 signalling pathway has been reported to participate in the pathophysiology of numerous inflammatory and immune diseases [18–20] . To our knowledge , this is the first study to measure plasma IL-33 and sST2 levels simultaneously in patients with HFRS and discuss their roles during HTNV infection . Our results indicated that high levels of IL-33 and sST2 could act as biomarkers of HFRS development ( Fig . 2 ) . It has been reported that sST2 has immunosuppressive activity and direct anti-inflammation action [36] . In our study , recombinant sST2 could inhibit the IL-33-induced inflammatory response ( Fig . 4C ) . To exert its regulatory function effectively , sST2 could be actively secreted by HUVECs stimulated by both HTNV and IL-33 ( Fig . 5A ) , which was a more rapid route than sST2 shedding from ST2L , the extracellular domain of which shares a common sequence with sST2 [37] . Therefore , the increased expression of sST2 in HFRS plasma could be a physiological natural response mechanism for suppressing the damaging inflammatory responses induced by IL-33 , preventing further ST2L-mediated immune cell activation and actively participating in the regulation of the immune system . As shown in Fig . 1J , during the first 10 days of disease onset , the sST2 content was more than 50 times the IL-33 content to balance the IL-33-mediated inflammatory response . Although we cannot at present affirm the protective effect of sST2 during HTNV infection in vivo , because of the lack of proper animal models for HFRS [38] , our results add weight to understanding the important role of IL-33/ST2 axis in the regulation of “cytokine storm” during the acute phase of HFRS and provide a possible therapeutic target of the HFRS . An important consideration arising from this study is why in HUVEC model , HTNV infection or IL-33 treatment alone did not induce excess cytokine production , whereas their combination augmented the expression of pro-inflammatory cytokines . IL-33 is a selective activator and preferentially targets nonquiescent HUVECs [11] . Previous reports have demonstrated that HTNV infection could induce increased levels of VCAM-1 and ICAM-1 in HUVECs [39] , which could drive HUVECs into the nonquiescent state . Therefore , we hypothesised that HTNV infection activated HUVECs , causing them to become nonquiescent cells , which then became the target cells for IL-33 . Thus , IL-33 could augment its pro-inflammatory function . Furthermore , HTNV infection could activate the NF-κB pathway in HUVECs [7 , 39] . Acting as cross-talk between HTNV and IL-33 , the NF-κB pathway was highly activated when the HUVECs were treated with both HTNV and IL-33 ( Fig . 6A-C ) . Taken together , nonquiescence and the enhanced activation of the NF-κB pathway may contribute to the synergistic effect on cytokine production induced by both HTNV and IL-33 in HUVECs . Different infectious diseases may have different cytokine expression profiles . The mechanisms of initiating and fading of the “cytokine storm” may be also various in each infectious disease . Although Peng et . al have showed that during Angiostrongylus cantonensis infection , both splenocytes and brain mononuclear cells became IL-33 responsive and produced IL-5 and IL-13 [21] , we are still lack of knowledge whether the mechanisms showed in our study are specific for HFRS . At present , we cannot demonstrate these mechanisms on other infectious diseases for the lack of other pathogens in our lab . Further studies are still needed to address this issue . Overall , our results indicate that the IL-33/ST2 axis , serving as an important regulator of the inflammatory response during HTNV infection , may be involved in the pathogenesis of HFRS . The utilisation of sST2 to selectively reduce IL-33/ST2 , with a consequent decrease in the inflammatory response in endothelial cells , may be exploited as a therapeutic target for Hantavirus infections .
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Hantaan virus ( HTNV ) causes human hemorrhagic fever with renal syndrome ( HFRS ) with a mortality rate of approximately 15% in Asia . At present , the primary treatment for HFRS is limited to critical care management and the use of anti-viral drugs , such as Ribavirin . However , the cytokine storm at the acute phase of HFRS , which is thought to contribute to the development of the disease , is still lacking an effective way to prevent . An alternative way to prevent the development of cytokine storm is of priority to overcome the problem . We found that IL-33 and sST2 levels were higher in the plasma of HFRS patients , especially in their acute phase . Although both of them were positively correlated with the severity of the diseases , they acted in different roles in the regulation of the immune response during HTNV infection . In vitro study showed that IL-33 acted as an initiator of the cytokine storm in HTNV-infected endothelial cells , while sST2 acted as an inhibitor of the process . For the first time , we defined the IL-33/ST2 axis as inflammatory regulators during HTNV infection . Our results may provide a novel therapeutic target of HTNV infections .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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IL-33/ST2 Correlates with Severity of Haemorrhagic Fever with Renal Syndrome and Regulates the Inflammatory Response in Hantaan Virus-Infected Endothelial Cells
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The glycosphingolipid isoglobotrihexosylceramide , or isogloboside 3 ( iGb3 ) , is believed to be critical for natural killer T ( NKT ) cell development and self-recognition in mice and humans . Furthermore , iGb3 may represent an important obstacle in xenotransplantation , in which this lipid represents the only other form of the major xenoepitope Galα ( 1 , 3 ) Gal . The role of iGb3 in NKT cell development is controversial , particularly with one study that suggested that NKT cell development is normal in mice that were rendered deficient for the enzyme iGb3 synthase ( iGb3S ) . We demonstrate that spliced iGb3S mRNA was not detected after extensive analysis of human tissues , and furthermore , the iGb3S gene contains several mutations that render this product nonfunctional . We directly tested the potential functional activity of human iGb3S by expressing chimeric molecules containing the catalytic domain of human iGb3S . These hybrid molecules were unable to synthesize iGb3 , due to at least one amino acid substitution . We also demonstrate that purified normal human anti-Gal immunoglobulin G can bind iGb3 lipid and mediate complement lysis of transfected human cells expressing iGb3 . Collectively , our data suggest that iGb3S is not expressed in humans , and even if it were expressed , this enzyme would be inactive . Consequently , iGb3 is unlikely to represent a primary natural ligand for NKT cells in humans . Furthermore , the absence of iGb3 in humans implies that it is another source of foreign Galα ( 1 , 3 ) Gal xenoantigen , with obvious significance in the field of xenotransplantation .
Identification of endogenous antigens that regulate NKT cell development and self-recognition represents a major goal in immunology . This unique population of T cells is characterised by expression of an invariant Vα14Jα18 TCR—Vα24Jα18 in humans—and the recognition of glycolipid antigens presented by CD1d [1] . When activated , natural killer T ( NKT ) cells regulate immune responses through their ability to produce large amounts of cytokines such as interferon ( IFN ) -γ and interleukin ( IL ) -4 [2] . NKT cell deficiencies are associated with a range of diseases , including cancer , autoimmunity , and infection , in mice and humans [2] . This , combined with the fact that NKT cell numbers vary widely in humans [3] , highlights the importance of understanding the endogenous antigens in humans that regulate NKT cell development and function . Initial work demonstrated that α-galactosylceramide ( α-GalCer ) , a glycosphingolipid originally derived from a marine sponge [4] , was a potent agonist for NKT cells in a CD1d-dependent manner in both mice and humans [5 , 6] . However , the physiological relevance of this in mammalian systems was difficult to understand because α-GalCer is not a mammalian product . Zhou et al . [7] demonstrated that a deficiency in the lysosomal enzymes β-hexaminidase A and B selectively abrogated NKT cell development , suggesting that glycolipid ( s ) downstream of these enzymes are responsible for NKT cell selection . Experiments to directly test which of the candidate glycolipids were capable of stimulating NKT cells pointed to the glycosphingolipid , isogloboside 3 ( iGb3 ) . Both mouse and human fresh NKT cells , and NKT cell hybridomas and lines , responded to iGb3 , and furthermore , specific inhibition of iGb3 on human cells , using isolectin B4 ( IB4 ) that should selectively target iGb3 via its terminal Galα ( 1 , 3 ) Gal sugars , suggested that iGb3 was also a primary human self-antigen for NKT cells [7] . These data led the authors to suggest that iGb3 was the main endogenous ligand responsible for NKT cell development and self-recognition in both mice and humans . Subsequent studies from independent groups have confirmed that iGb3 is an agonist ligand for at least a subset of mouse and human NKT cells [8–13] , and furthermore , that this glycosphingolipid appears to be important for shaping the NKT cell TCR repertoire in mice [12] . However , recent studies have challenged the hypothesis that iGb3 is the primary ligand responsible for NKT cell development in mice [14–16] . One of these studies [16] failed to detect iGb3 in mouse or human thymus , although this study could not exclude the existence of low levels of iGb3 , or higher levels of iGb3 expressed by a minor subset of the thymus . Another study [15] demonstrated , by using iGb3 synthase ( iGb3S ) knockout mice , that NKT cell development was apparently normal , which more strongly suggested that iGb3 is at least not essential for this process in mice . Lastly , two papers have provided evidence that the defect in NKT cell development in Hex-b–deficient mice may be the lysosomal storage disease that occurs with this mutation , thus causing a nonspecific defect in glycolipid processing and presentation [14 , 17 , 18] . The ability of iGb3 to activate human NKT cells is not in dispute; what remains in question is the role of iGb3 in human NKT cell biology . Another issue of major importance involving iGb3 is from the perspective of xenotransplantation , in which expression of this glycolipid on the cell surface of pig tissue could represent a major problem if it is not present in humans . iGb3 is synthesized by iGb3S , a member of the α1 , 3Gal/GalNAc transferase or Family 6 glycosyltransferases . Other family members include α1 , 3galactosyltransferase ( α1 , 3 GT ) , and the B blood group transferase , which like iGb3S , transfer αGal . In contrast , the two other members of the family , A blood group transferase and Forssman synthetase , transfer αGalNAc . Members of Family 6 are the only known mammalian glycosyltransferases that transfer either αGal or αGalNAc in an α1 , 3 linkage to their respective acceptor molecules . Analysis of the human genome shows the genes for the α1 , 3GT , iGb3S , A/B blood group transferase , and the Forssman synthetase are present [19] . Recently , GT6m7 , a new Family 6 member , was reported [19]; however , in humans , the gene for this glycosyltransferase contains a premature stop codon in the last exon . Indeed , mutation appears to be common in this family , with varying effects ( see Table 1 ) . In a similar fashion to ABO blood groups , in which natural antibodies are made to specificities that individuals lack , humans produce anti-αGal antibodies as a consequence of nonfunctional , or nontranslated enzymes . The evolutionary event that led to selection of the αGal-ve phenotype in humans is not clear , but selective pressure of the αGal+ve protozoan parasites has been postulated [20] . It is well known that the presence of the Galα ( 1 , 3 ) Gal xenoepitope , synthesized by α1 , 3galactosyltransferase ( α1 , 3GT ) , causes hyperacute rejection of donor organs in pig-to-human xenotransplantation [21] . To avoid this problem , the α1 , 3GT gene has recently been deleted in pigs [22] . However , there is still low-level expression of Galα ( 1 , 3 ) Gal [23] , presumably synthesized by iGb3S . Thus , organs from GT−/− pigs transplanted into humans may still potentially be subject to rejection by human natural antibodies to Galα ( 1 , 3 ) Gal in the form of iGb3 . The study from Zhou and colleagues [7] provided data suggesting that this is unlikely to pose an immediate problem , because they showed that human anti-Gal antibodies did not react with iGb3 , presumably because it was a self-ligand that caused deletion of iGb3-reactive lymphocytes in humans . Thus , although there is clearly a significant level of controversy surrounding iGb3 , the fact remains that there are compelling results both for and against a role for iGb3 in NKT cell development . For the sake of understanding the factors that regulate this process , as well as whether iGb3 poses an additional problem for xenotransplantion , further studies are required to resolve this issue . We recently characterized the mouse iGb3S cDNA [24] that encodes the enzyme that synthesizes the Galα ( 1 , 3 ) Gal xenoepitope on iGb3 by catalysing the transfer of donor sugar from UDP-Gal in an α−1 , 3 linkage to its acceptor molecule Galβ ( 1 , 4 ) Glc-ceramide [25] . This reaction is the first step in the isoglobo-series pathway , which also results in the generation of iGb4 and isoForssman glycolipids ( Figure 1 ) . Here , we have examined iGb3S expression and functional potential in human tissues . Moreover , to assess whether iGb3 might represent a xenoantigen that remains in α1 , 3GT knockout pigs , we investigated whether iGb3 glycolipid is recognized by natural human anti-Gal antibodies present in normal human serum , and we also determined whether cells expressing this glycolipid on the cell surface are readily targeted for complement-mediated lysis .
Several lines of evidence from our studies of the Galα ( 1 , 3 ) Gal epitope suggest that iGb3S is not expressed in humans . In contrast to both rat and mouse , in which the iGb3S gene is transcribed and the RNA processed [24 , 25] , analysis of a human multiple tissue northern blot did not detect iGb3S mRNA ( unpublished data ) . Furthermore , anti-Gal monoclonal antibodies ( mAbs ) that detect both rat and mouse iGb3 on tissues and cell lines do not react with a range of both normal and malignant human tissues and cell lines [24] ( and our unpublished data ) . To examine expression of iGb3S mRNA in greater detail , reverse-transcription PCR ( RT-PCR ) was used to analyse several human tissues . Oligonucleotide primers for these experiments ( Table S1 ) were designed based on the exon arrangement of the human iGb3S gene , established by the analysis of Genbank DNA sequences . RNA from heart , kidney , spleen , lung , and thymus ( the latter two tissues express iGb3S in both rat and mouse ) generated a product of the correct size ( ∼550 bp ) with forward and reverse primers within exon five ( unpublished data ) . A product was also obtained from cDNA from human dendritic cells ( Figure 2B , lane 6 ) . Some products were confirmed as human iGb3S by direct sequencing ( unpublished data ) . However , generation of products within an exon may be due to genomic DNA or heteronuclear RNA; therefore , amplification across exon boundaries is required to show the presence of mRNA . We have previously shown that iGb3S mRNA can be successfully used as a template for cross-exon RT-PCR from mouse RNA [24] . Despite exhaustive attempts ( at least 50 times ) to amplify human iGb3S using a combination of primers spanning all five exons in different tissues ( Figure 2A ) , products were either not obtained from human template or the size of several of the PCR products corresponded to that expected from genomic DNA rather than spliced mRNA . The data shown are the amplification from dendritic cells ( Figure 2B ) ; however , similar results were also obtained from all tissues examined . Primers across exon 1 to 3 , exons 1 to 4 , and exons 2 to 4 yielded products expected from amplification of genomic DNA rather than spliced RNA ( lanes 1 , 2 , and 4 , respectively , Figure 2B and Table S2 ) . The products observed in lanes 6 and 7 are within a single exon and could represent either genomic or mRNA products as there is no splicing over this region of the iGb3S gene . Despite using numerous primer combinations , including forward primers from exons 2 , 3 , or 4 with a reverse primer from exon 5 ( Figure 2C ) , we were unable to detect any products of the correct size to suggest spliced iGb3S mRNA in any human tissue examined ( Figure 2D ) , even when a high cycle number ( up to 40 ) was used ( unpublished data ) . As expected , products were not observed when template was omitted . From our experience , mouse iGb3S mRNA is expressed at low levels and is difficult to amplify . Therefore , our inability to detect human iGb3S mRNA was not conclusive evidence that it was absent . Transfection of CHOP cells with mouse iGb3S cDNA results in high level expression of its product Galα ( 1 , 3 ) Gal [24] . We used the same approach to determine whether humans express functional iGb3S . In vitro functional studies indicate that the catalytic domain of iGb3S ( which represents 75% of the entire molecule ) is encoded by two exons , a small exon ( exon 4 ) and a larger one ( exon 5 ) encoding the major part of the functional domain [26] . Soluble forms of the truncated catalytic domain of several members of the glycosyltransferase family have been shown to be enzymatically active . Using splice overlap extension PCR , we initially generated a chimeric molecule in which exon 5 of the functional rat iGb3S was substituted with that of the human iGb3S homolog ( generated from human genomic DNA ) ( Figure 3A and Table S3 ) . The other rat exons encode the cytoplasmic tail , transmembrane domain , and stalk that anchors the molecule in the lipid bilayer . This approach of exchanging catalytic domains to examine function has been successfully used with Forssman synthetase , another member of this glycosyltransferase family [27] . The ability of this chimeric rat/human ( exon5 ) -iGb3S molecule to synthesize Galα ( 1 , 3 ) Gal was determined by analysis of transfected CHOP cells . As expected , cells transfected with DNA encoding rat iGb3S displayed strong cell surface expression of the Galα ( 1 , 3 ) Gal epitope on glycolipid as determined by binding of the monoclonal antibody 15 . 101 [28] and human anti-Gal immunoglobulin ( Ig ) purified from normal human serum ( Figure 3B ) . The 15 . 101 mAb has been shown to bind preferentially to Galα ( 1 , 3 ) Gal on iGb3 lipid [28] . The chimeric molecule containing the majority of the catalytic domain of human iGb3S ( rat/human ( exon5 ) -iGb3S ) was unable to synthesise the Galα ( 1 , 3 ) Gal epitope as staining was not observed with 15 . 101 or human anti-Gal Ig ( Figure 3B ) . A second chimeric molecule comprising the entire human catalytic domain , exon 4 together with exon 5 ( rat/human ( exon4 , 5 ) -iGb3S ) , was also unable to synthesize Galα ( 1 , 3 ) Gal ( Figure 3B ) . Data from several other mAbs and Bandeiraea simplicifolia IB4 lectin that bind the Galα ( 1 , 3 ) Gal epitope ( Figure S1 ) support the conclusion that the human iGb3S catalytic domain is not functional . Detection of the FLAG epitope in both chimeric enzymes confirmed that the absence of Galα ( 1 , 3 ) Gal synthesis was not due to impaired translation or expression ( Figure S2A ) . As glycosyltransferases are integral membrane proteins of the Golgi complex where oligosaccharides are synthesized , perinuclear staining ( Golgi-like ) confirmed correct trafficking of the chimeric enzymes ( Figure S2B ) . Staining was not observed with cells transfected with vector alone . To explore the unlikely possibility that iGb3 staining was not observed due to antibody inaccessibility , an alternative detection method was used . Synthesis of iGb3 is the initial step for the formation of the isoglobo-series glycolipid pathway , and iGb3 is the precursor to iGb4 and , ultimately , isoForssman [25] ( see Figure 1 ) . To examine whether iGb3 was synthesized , a complementation assay in CHOP cells , which lack both iGb3S and Forssman synthetase ( FS ) , was used to determine whether coexpression of chimeric iGb3S with FS results in expression of isoForssman glycolipid . As expected , cells transfected with FS alone did not stain for isoForssman ( unpublished data ) , whereas cells transfected with both rat iGb3S and FS were positive for isoForssman ( Figure 3C ) . Expression of Galα ( 1 , 3 ) Gal was confirmed by 15 . 101 binding . Cells cotransfected with either of the chimeric molecules ( rat/human ( Exon5 ) -iGb3S or rat/human ( Exon4 , 5 ) -iGb3S ) and FS did not show any detectable isoForssman staining ( Figure 3C ) . As expected , no Galα ( 1 , 3 ) Gal was observed following staining with 15 . 101 . Thus , the human catalytic domain appears to be incapable of generating detectable iGb3 and does not initiate the downstream synthesis of the iGb4 structure required for FS to function . Using site-directed mutagenesis , we analysed which amino acid ( s ) contributed to the loss of function we observed in human iGb3S . Despite an overall similarity of approximately 72% , there are 77 differences within the catalytic domain of the functional rat iGb3S and nonfunctional human iGb3S , any of which , either alone or in combination , may be involved in the loss of function observed with human iGb3S . The targeted residues were selected by comparison of the aligned amino acid sequences of the iGb3S catalytic domains ( exon 4 and 5 encoded ) of species known to synthesize iGb3 ( rat , mouse , and dog ) with that of human . To identify more precise candidates , amino acids were excluded: ( 1 ) if the human amino acid was identical to either the mouse or dog , ( 2 ) the amino acid residue was different in all four species , or ( 3 ) the substitution was with an homologous amino acid . Four of these amino acids in rat exon 5 were selected in these initial studies and mutated to their human equivalent ( Figure 4A ) . The single isolated substitution of rat Y252N resulted in the complete elimination of Galα ( 1 , 3 ) Gal staining ( Figure 4B ) , showing that this asparagine in human iGb3S is sufficient to ablate enzymatic function . Rat L187P showed a significant reduction ( typically 70%–95% ) in Galα ( 1 , 3 ) Gal staining , whereas both the rat A221S and rat E280A substitutions showed strong Galα ( 1 , 3 ) Gal expression that was comparable with that observed following transfection with rat iGb3S ( Figure 4B ) . As expected , a complementation assay with FS resulted in strong isoForssman staining with both rat A221S and rat E280A substitutions ( Figure 4C ) . A similar high level of isoForssman staining was also observed with the rat L187P substitution , despite there being minimal Galα ( 1 , 3 ) Gal expression , thus demonstrating the sensitivity of this method . IsoForssman staining was not observed with cells cotransfected with rat Y252N ( Figure 4C ) . It is possible that the Y252N and L187P substitutions are not the only ones in humans that influence function . This was examined by reverse mutation of the nonfunctional chimeric rat/human ( exon 5 ) -iGb3S to their rat equivalents with either point mutation alone ( i . e . , P187L or N252Y ) , or in combination ( P187L+N252Y ) . A gain of function would suggest these are the primary residues involved in determining whether the transferase is functional . Staining with mAb 15 . 101 showed no Galα ( 1 , 3 ) Gal expression following transfection of CHOP cells with either the single or combined reverse-mutated chimeric cDNA molecules ( Figure 5 ) . The implications of these data are that human iGb3S must have multiple mutations that have resulted in its inactivation . Typical strong Galα ( 1 , 3 ) Gal expression was observed with cells transfected with rat iGb3S . High-performance thin layer chromatography data from Zhou et al [7] suggested natural mixed human serum antibodies did not recognize iGb3 , suggesting that iGb3-reactive B cells had been deleted from the human repertoire , further evidence that iGb3 lipid is present in humans . To test this ourselves , we used a lipid ELISA , and by this approach , demonstrated clear binding of both natural human anti-Galα ( 1 , 3 ) Gal antibodies and the mAb 15 . 101 to purified iGb3 lipid over several antibody dilutions ( Figure 6A ) . Binding was not observed with either anti-CD17 ( lactosylceramide ) or anti-CD77 ( Gb3 ) mAbs . Furthermore , as a specificity control , treatment of iGb3 lipid with α-galactosidase , which specifically removes the terminal α ( 1 , 3 ) Gal moiety , resulted in a significant inhibition ( up to 60% ) of binding by natural human anti-Galα ( 1 , 3 ) Gal antibodies ( Figure 6B ) , yet had no effect on anti-CD17 binding to lactosylceramide ( no αGal moiety ) in a parallel assay . Antibody specificity was further demonstrated by the lack of binding of natural human anti-Galα ( 1 , 3 ) Gal antibodies to either Gb3 ( Figure 6C ) or lactosylceramide ( Figure 6D ) . However , as expected specific binding of both anti-CD77 and anti-CD17 mAbs ( used at the same dilutions as in Figure 6A ) were observed ( Figure 6C and 6D , respectively ) . A key question that remains to be answered is , if human cells were to express iGb3 , would they be susceptible to antibody-dependent complement-mediated lysis due to natural human anti-αGal antibodies present in normal human serum ( NHS ) ? In contrast to nontransfected human cells ( αGal−ve ) that do not undergo lysis with NHS , human cells expressing iGb3 ( αGal+ve ) were lysed by NHS ( in the presence of rabbit complement ) in a dose-dependent manner ( Figure 7A ) . Removal of anti-αGal antibodies from NHS by absorption with Galα ( 1 , 3 ) Gal coupled to glass beads abolished lysis to background levels ( Figure 7A ) . However , lysis was not affected by NHS absorption with uncoupled glass beads ( unpublished data ) . Furthermore , lysis of human cells expressing iGb3 was re-established when anti-αGal IgG antibodies were purified from NHS and used in the cytotoxicity assay ( Figure 7A ) . In addition , this activity could be removed by absorption with Galα ( 1 , 3 ) Gal coupled to glass beads ( unpublished data ) . Inhibition experiments verified that Galα ( 1 , 3 ) Gal is the epitope that the antibodies recognise , as a significant dose-dependent reduction in lysis was observed by preincubation of both NHS and anti-αGal IgG antibodies with Galα ( 1 , 3 ) Gal disaccharide ( Figure 7B ) . No inhibition was observed when lactose ( Galβ ( 1 , 4 ) Glc ) was used ( Figure 7B ) .
Glycolipids represent one of the last molecular frontiers in immunological recognition . Whereas glycolipids are known to be synthesized in the Golgi and are typically expressed on the cell surface , the exact transport pathway ( s ) for newly synthesized glycolipids is not well defined . However , it is assumed to be similar to glycoproteins and involve vesicular flow from the endoplasmic reticulum through the Golgi complex to the plasma membrane . Glycolipids , particularly exogenous glycolipids , can localize to lysosomal compartments via endocytosis . Similarly , our knowledge of how glycolipids control immune responses and the context in which they are presented by CD1d and recognized by NKT cells is also still very limited . Since the glycolipid , α-GalCer , was originally shown to potently stimulate NKT cells in a CD1d-dependent manner , there has been an enormous effort to identify other ligands . Several classes of natural CD1d-binding ligands for NKT cells have been identified , including microbial-derived α-linked glycosphingolipids from the nonpathogenic Sphingomonas bacteria and phosphatidylinositol mannoside from Mycobacteria ( reviewed in [29] ) . Recently , a diacylglycerol glycolipid from Borrelia burgdorferi , a human pathogen responsible for Lyme disease , was shown to directly stimulate both human and mouse NKT cells [30] . Although these ligands are all candidates for NKT cell recognition of non-self , none of these are present in normal mammalian cells . The main candidate self glycolipid-antigen is iGb3 . The original collective data , primarily based on the use of β-hexosaminidase-B–deficient mice that are incapable of degrading iGb4 into iGb3 in lysosomes , supported the claim that iGb3 lipid was a principle endogenous ligand for Vα14 NKT cells in mice and , albeit indirectly , in humans [7 , 12 , 31] . The interpretation of data using β-hexosaminidase-B–deficient mice was contested by Gadola et al . [14] , where it was argued that these mice have a generalised lysosomal storage disease that indirectly impaired CD1d loading in lysosomes . Their interpretation was that it was the accumulation of glycolipids in lysosomes , rather than the lack of iGb3 , that abrogated NKT cell development . Some of the data in this paper [14] simply conflicted with that of the earlier study of β-hexosaminidase-B–deficient mice [7] , making it difficult to determine which interpretation was correct [14] . Similar suggestions were raised in an independent study of mutations leading to lysosomal storage diseases [18] . Recently , Porubsky et al . and Speak et al . [15 , 16] failed to detect iGb3 in mouse and human thymus using a biochemical approach . Furthermore , Porubsky et al . [15] reported normal development and function of invariant NKT ( iNKT ) cells in iGb3S−/− mice . Although the lack of biochemical evidence for iGb3 in thymus might simply be an issue of insufficient sensitivity , the results from the iGb3S−/− mice more strongly challenge the significance of iGb3 in mouse NKT cell development . There is no easy interpretation that incorporates and integrates the findings from the studies for , and against , a role for iGb3 in mouse NKT cell development . In our opinion , this represents one of the most important and controversial issues in the NKT cell field that requires additional input from independent research groups . In humans , synthetically derived iGb3 can stimulate human NKT cells to proliferate and produce cytokines [7 , 8 , 32] and recognition of human dendritic cell self-antigen can be blocked by IB4 lectin [7] . However , direct biochemical evidence to show that human iGb3 is an endogenous NKT cell ligand has been lacking . Although iGb3 was not detected in human thymus or human dendritic cells using a high-performance liquid chromatography ( HPLC ) assay , this assay had a detection limit of 1% iGb3 to 99% Gb3 , which does not exclude the presence of iGb3 at low but still biologically significant levels [16] . Indeed , during review of this manuscript , two publications from Li et al . , claimed to be able to discriminate iGb3 from Gb3 ( in artificial mixtures and from rat cells ) and identified iGb4 from human paediatric thymi , using electrospray ionisation-ion trap mass spectrometry [33 , 34] . Although these analyses are at odds with our own , they have yet to conclusively demonstrate immunologically significant levels of iGb3 in human tissue . Specifically , the formal possibility remains that the minor MSn mass spectral signature for iGb4 detected in these studies is derived from related tetraglycosylceramides , as acknowledged by these investigators . Alternatively , the very low levels of iGb4 detected in these analyses may be derived from dietary sources and distributed throughout the body via lipoprotein particles . The presence or absence of iGb3 in humans has potential major implications for xenotransplantation . If humans express iGb3S , iGb3 lipid present on transplanted pig tissues will not be “seen” as foreign and therefore would not represent a drawback for xenotransplantation . However , as humans do not express functional iGb3S ( reported herein ) , then the presence of lipid-linked Galα ( 1 , 3 ) Gal in pigs , synthesized by iGb3S , may pose a serious risk to successful xenotransplantation , even when using α1 , 3GT knockout pigs as donors ( which were specifically generated to eliminate Galα ( 1 , 3 ) Gal epitopes for xenotransplantation purposes ) . What are the implications of this in a transplant setting ? Currently , we know that expression of iGb3 does not mediate hyperacute rejection of pig tissues transplanted into baboons [35 , 36] . However , human serum has at least a 4-fold higher level of natural anti-Galα ( 1 , 3 ) Gal antibodies ( ∼1% of human IgG ) than other primates [37] , so this may not directly represent the human situation . Furthermore , iGb3 expression in pigs may have more serious consequences in the later phases of graft rejection . Firstly , changes in the affinity/avidity of the elicited antibodies may cause tissue damage by complement fixation . It is clear that the level of anti-Galα ( 1 , 3 ) Gal antibody is critical for the speed of rejection in experimental models [38] . Alternatively , elicited anti-Galα ( 1 , 3 ) Gal antibodies may contribute to the acute vascular rejection observed when hyperacute rejection is eliminated , such as in knockout pig-to-primate transplants , by activation of endothelial cells via cross-linking of the lipid itself . Pathological features similar to acute vascular rejection are seen in humans when the Gb3 lipid ( closely related to iGb3 ) is cross-linked by bacterial toxins [39] . Secondly , because iGb3 activates human NKT cells [7 , 32] ( in which synthetic , purified . and enzymatically derived iGb3 were all tested ) , consequently the expression of iGb3 on pig cells could lead to NKT cell activation resulting in destruction of the xenograft . Furthermore , our data clearly show that the anti-Gal antibodies in NHS can lyse iGb3 expressing cells ( Figure 7 ) and therefore any remaining iGb3 on pig cells may be a target for antibody-mediated destruction . Whereas it is clear that the use of heavy immunosuppression can control the later phases of xenograft rejection , the major advantage of xenotransplantation over allotransplantation is the ability to genetically modify the donor . The ultimate goal is to engineer a donor pig such that minimal , or indeed no immunosuppression is required for long-term graft survival . It is likely that genetic modification of pigs may be required to eliminate any effects of iGb3 . Only at that stage will other obstacles be revealed . Thus , in formally demonstrating the lack of functional iGb3S in humans , this study alerts transplantation immunologists to a previously unrecognised risk associated with expression of iGb3 glycolipid on α1 , 3GT knockout pig tissues . Expression of this glycolipid could act as a secondary source of Galα ( 1 , 3 ) Gal xeno-antigen capable of binding natural human anti-Gal antibodies present in normal human serum and marking these cells for destruction by complement mediated lysis . In a perspectives article , Godfrey , Pellicci , and Smyth [40] asked whether the search for the elusive NKT cell antigen is over . In mice , in view of several recent publications [14–16] , the possible existence of NKT cell-selecting ligands other than iGb3 remains an important consideration [17] . It had generally been assumed that experimental data obtained from mice would be directly relevant to humans , and Zhou et al . [7] provided indirect evidence that iGb3 is also a self-ligand for human NKT cells . However , our new data demonstrate that there appears to be critical differences between the two systems , and suggests that we are a long way from calling off the search for NKT cell-selecting antigens in humans . This remains one of the most important objectives in the field , and will ultimately lead to a better understanding of the factors that regulate NKT cell development and function in health , and in developing novel therapies for the treatment of disease .
The human genomic iGb3S sequence was obtained from the National Center for Biotechnology Information Web site ( http://www . ncbi . nlm . nih . gov ) and searching the human genomic database . The nucleotide sequence of the gene A3GALT2 ( accession number NT 032977 ) was used , with the exon/intron boundaries for the human iGb3S gene as listed with the sequence . We were unable to clone human iGb3S from total RNA from adult human tissues ( heart , lung , kidney , spleen , and thymus ) ( Stratagene ) or cDNA from dendritic cells using the TITANIUM One-Step RT-PCR Kit ( Clontech ) with a series of degenerate primers ( Tables S1 and S2 ) . The chimeric rat/human molecules included the exchange of rat exon 5 ( rat/human ( exon5 ) -iGb3S ) and rat exons 4 and 5 ( rat/human ( exon4 , 5 ) -iGb3S ) with the equivalent human exon ( s ) . The rat/human chimeras were generated using splice overlap extension PCR . The specific primer combinations used are shown in Table S3 . The single amino acid substitutions in rat iGb3S ( L187P , A221S , Y252N , and E280A ) and the reverse mutations in rat/human ( exon5 ) -iGb3S ( P187L , N252Y , and the combined P187L+N252Y ) were introduced using the QuikChange site-directed mutagenesis kit ( Stratagene ) ( Table S4 ) . Sequence fidelity , orientation of the insert , and presence of the desired mutation ( s ) were confirmed by DNA sequencing ( Big Dye 3 . 1; PE-Applied Biosystems ) . CHOP cells ( Chinese Hamster Ovary cells transformed with Polyoma Large T antigen ) [41] and E293 cells ( human kidney fibroblasts ) were cultured in DMEM ( CSL ) supplemented with 10% FCS overnight at 37 °C . Transfections were with LipofectAMINE Plus ( Life Technologies ) as recommended by the manufacturer . Cells were examined after 48 h for either cell surface or intracellular expression of Galα ( 1 , 3 ) Gal using purified natural human anti-Gal antibodies ( 0 . 49 mg/ml ) , and the anti-Galα ( 1 , 3 ) Gal mAbs 15 . 101 , 22 . 121 , 24 . 7 , 25 . 2 , and 8 . 17 ( supernatants ) [24 , 42] and Bandeiraea simplicifolia IB4 lectin . Expression of the FLAG epitope was revealed by staining with the anti FLAG M2 mAb ( Sigma ) . Expression of IsoForssman glycolipid was revealed using an anti-Forssman mAb , FOM-1 ( BMA Biomedicals ) . Expression of lactosylceramide and Gb3 were revealed with anti-CD17 ( ascites ) and purified anti-CD77 ( 0 . 15 mg/ml ) mAbs , respectively ( Pharmingen ) . Antibodies were detected with FITC-labelled sheep anti-mouse or human IgG ( Dako ) or HRP conjugate Sheep anti-human Ig ( Silenus ) and rabbit anti-mouse Ig ( Dako ) , and analysed either by fluorescence microscopy , flow cytometry ( Becton Dickinson FACS Canto II ) , or lipid ELISA . Porcine lactosylceramide ( Calbiochem ) and iGb3 ( Alexis Biochemicals ) were dissolved in methanol at 1 mg/ml and stored at −20 °C . The ELISA was performed in 96-well Maxisorb plates ( Nunc ) . Lipids were diluted in n-hexane and used at 500-ng/well , incubated for 1 h in a fume hood to dry; plates were then blocked with 3% BSA/PBS for 2 h and washed ×1 with PBS . Primary antibodies , diluted in blocking buffer , were added and incubated for 1 h . After washing ×5 with PBS , secondary antibodies , diluted in blocking buffer , were added and incubated for 1 h before washing ×8 with PBS . All incubations were carried out at room temperature ( RT ) on a rocking platform . TMB peroxidase substrate ( KPL ) was used to develop the plate . Colour development was stopped with 0 . 18 M H2SO4 and quantitated at an optical density at 405 nm ( OD405nm ) on an ELISA plate reader . For the enzyme digestion , α-galactosidase ( Sigma-Aldrich ) was diluted in 0 . 1 M citrate/phosphate buffer ( pH 6 ) and incubated with the lipids overnight at RT , after which an ELISA was performed as described above . Human E293 cells expressing rat iGb3 [28] were tested for lysis with rabbit complement and normal human sera ( NHS , pooled from ten healthy individuals and heat inactivated ) or purified human anti-Gal IgG antibodies ( prepared by fractionation of the NHS pool on a Protein G Sepharose column ( Pharmacia ) followed by affinity chromatography on Galα ( 1 , 3 ) Gal-coupled macroporous glass beads ( Syntesome ) as described previously [43] . In brief , 50 μl of antibody at doubling dilutions were added to 2 . 5 × 105 cells per well in round-bottomed 96-well plates ( Greiner ) , resuspended , and incubated on ice for 30 min . After two washes , 50 μl of rabbit complement , at an appropriate dilution , was added to the cell pellet , resuspended , and incubated at 37 °C for 30 min ( NHS ) or 60 min ( purified anti-Gal IgG ) . Cells were pelleted and resuspended in 400 μl of DMEM/0 . 5% BSA containing 1 μg/ml propidium iodide ( PI; Sigma ) and analysed by flow cytometry . Percentage lysis ( cytotoxicity ) was determined by analysis of 10 , 000 cells . The importance of anti-Gal antibodies for lysis was determined by serum absorption and carbohydrate inhibition: ( 1 ) Absorption; 200 μl of NHS or human anti-Gal IgG was added to an equal volume of Galα ( 1 , 3 ) Gal-coupled macroporous glass beads or non-coupled beads ( control ) at 4 °C for 30 min; the beads were removed by centrifugation and the absorption step repeated with another aliquot of beads . ( 2 ) Carbohydrate inhibition; 25 μl of 20 mM Galα ( 1 , 3 ) Gal disaccharide or lactose ( control ) was serially diluted and mixed with an equal volume of NHS or human anti-Gal IgG at an appropriate dilution ( two dilutions less than the 50% titre of the antibody ) and incubated at 4 °C for 16 h . After both of these treatments , the sera were analysed for complement-mediated lysis .
|
Identification of endogenous antigens that regulate natural killer T ( NKT ) cell development and function is a major goal in immunology . Originally the glycosphingolipid , iGb3 , was suggested to be the main endogenous ligand in both mice and humans . However , recent studies have challenged this hypothesis . From a xenotransplantation ( animal to human transplants ) perspective , iGb3 expression is also important as it represents another form of the major xenoantigen Galα ( 1 , 3 ) Gal . In this study , we assessed whether humans expressed a functional iGb3 synthase ( iGb3S ) , the enzyme responsible for lipid synthesis . We showed that spliced iGb3S mRNA was not detected in any human tissue analysed . Furthermore , chimeric molecules composed of the catalytic domain of human iGb3S were unable to synthesize iGb3 lipid , due to at least one amino acid substitution . We also demonstrated that purified human anti-Gal antibodies bound iGb3 lipid and mediated destruction of cells transfected to express iGb3 . A nonfunctional iGb3S in humans has two major consequences: ( 1 ) iGb3 is unlikely to be a natural human NKT ligand and ( 2 ) natural human anti-Gal antibodies in human serum could react with iGb3 on the surface of organs from pigs , marking these tissues for immunological destruction .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology"
] |
2008
|
Humans Lack iGb3 Due to the Absence of Functional iGb3-Synthase: Implications for NKT Cell Development and Transplantation
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Epigenetic mechanisms suppress the transcription of transposons and DNA repeats; however , this suppression can be transiently released under prolonged heat stress . Here we show that the Arabidopsis thaliana imprinted gene SDC , which is silent during vegetative growth due to DNA methylation , is activated by heat and contributes to recovery from stress . SDC activation seems to involve epigenetic mechanisms but not canonical heat-shock perception and signaling . The heat-mediated transcriptional induction of SDC occurs particularly in young developing leaves and is proportional to the level of stress . However , this occurs only above a certain window of absolute temperatures and , thus , resembles a thermal-sensing mechanism . In addition , the re-silencing kinetics during recovery can be entrained by repeated heat stress cycles , suggesting that epigenetic regulation in plants may conserve memory of stress experience . We further demonstrate that SDC contributes to the recovery of plant biomass after stress . We propose that transcriptional gene silencing , known to be involved in gene imprinting , is also co-opted in the specific tuning of SDC expression upon heat stress and subsequent recovery . It is therefore possible that dynamic properties of the epigenetic landscape associated with silenced or imprinted genes may contribute to regulation of their expression in response to environmental challenges .
It has been long recognized that transcriptional gene silencing ( TGS ) in plants is associated mainly with increased levels of DNA methylation [1] , [2] . DNA methylation is found in cytosines ( C ) residing in CG , CHG and CHH sequence contexts ( where H stands for A , T or C ) . Methyltransferase 1 ( MET1 ) perpetuates CG methylation patterns during DNA replication . Cytosine methylation in CHG and CHH sequences is mediated by Chromomethylase 3 ( CMT3 ) and Chromomethylase 2 ( CMT2 ) , respectively [3]–[6] . Cytosine methylation in asymmetric CHH sequences cannot be maintained in a replicative manner and the RNA-dependent DNA methylation ( RdDM ) pathway leads to their methylation de novo through sequence-specific targeting with small interfering RNAs , and thus the mitotic persistence of TGS [7] . De novo DNA methylation occurs in all sequence contexts and is mainly catalyzed by Domains Rearranged Methyltransferase 2 ( DRM2 ) [3] , [8] . TGS is involved in the epigenetic suppression of invading DNA , such as that of pathogens but also of endogenous transposons , which threaten genome stability by their mutational capacity and deleterious regulatory effects on neighboring genes [9] , [10] . However , TGS is also involved in genomic imprinting , i . e . allele-specific expression dependent on the parent-of-origin . The expression of some imprinted genes in plants is restricted to seed endosperm and is associated with silencing during somatic growth [11] . Such strict developmental regulation of imprinted gene expression is critical for seed and plant development . In Arabidopsis thaliana , aberrant expression of imprinted genes such as Medea ( MEA ) and Fertilization Independent Seed 2 ( FIS2 ) has strong phenotypic consequences that lead to seed abortion [12] . Ectopic expression of imprinted genes during vegetative growth may also have phenotypic consequences . For example , the imprinted A . thaliana gene SDC is epigenetically silenced in somatic tissues due to DNA methylation targeted by the RdDM pathway to tandem-repeats within its promoter . This locus is highly activated in particular combinations of TGS mutants such as drm1/drm2/cmt3 and ddm1/drd1 , which results in leaf curling and plant dwarfism [5] , [13] . Although epigenetic mechanisms can suppress transcription at ambient temperatures , it was reported recently that transcriptional activation can occur transiently during prolonged exposure to heat [14]–[17] . The degree of activation was proportional to the duration of the stress and was associated with decreased nucleosome occupancy and resulting chromatin decondensation . Importantly , chromatin assembly factors restored silencing within 48 h after heat stress [14] . Here , we report on heat stress-mediated ectopic activation of the imprinted SDC gene in vegetative tissues . The stress-triggered transcriptional response of SDC occurred particularly in young developing leaves and the kinetics of re-silencing could be entrained by repeated heat stress cycles . We provide evidence for a physiological role of this unexpected regulation of an imprinted gene during recovery from heat stress .
To analyze the heat-mediated release of TGS , we used a transgenic line carrying a silent 35S::GUS construct , referred to as L5-GUS [2] . The transcription of this transgene is repressed by DNA methylation of the promoter , and its silencing is released in several epigenetic mutants such as mom1 , ddm1 and met1 [2] , [14] . L5-GUS A . thaliana seedlings were subjected to an acclimation treatment consisting of a varying number of diurnal heat cycles as entrainment . Each cycle comprised 12 h at 37°C in the light and 12 h at 21°C in the dark . This experimental design with elevated temperature associated with light periods closely models natural growth conditions , when plants experience high temperatures mostly during the day . The entrainment was followed by a recovery period of 3 days at 21°C with a 12 h/12 h light/dark cycle . After recovery , an additional heat cycle ( second stress ) was applied to a subset of the entrained seedlings ( Figure 1A ) . As expected , GUS transcription was released upon heat stress but resulted in similar transcript levels between each heat cycle and the second stress . However , GUS mRNA levels during recovery showed a stepwise increase proportional to the number of heat stress cycles ( Figure 1B ) . This result suggests either transcriptional memory related to the previous heat-induced release of silencing or merely the physiological consequence of a higher perceived stress dose . To distinguish between these possibilities , we examined the transcriptional regulation of typical heat stress-responsive genes after the entrainment . These loci did not show an L5-GUS-related pattern of mRNA accumulation , during either activation or recovery ( Figure S1 ) , indicating that the transcriptional consequences of entrainment were specific to the epigenetically regulated L5-GUS transgene . In a search for protein-coding genes displaying responses similar to the L5-GUS transgene , we examined a subset of loci known to be transcriptionally suppressed by epigenetic modification of their promoters but activated in epigenetic mutants or under heat stress [16] , [18]–[20] . We tested the heat stress entrainment of the genes SDC ( AT2G17690 ) , SQN ( AT2G15790 ) , and APUM9 ( AT1G35730 ) . Of these three candidates , only SDC showed a response pattern similar to L5-GUS ( Figure 1B and S1 ) . We hypothesized that for both L5-GUS and SDC , the positive correlation between elevated transcript levels during recovery and the number of heat stress cycles reflects an altered speed of re-silencing as a consequence of the entrainment ( Figure S2 ) . To test this possibility , we focused on SDC re-silencing kinetics . Indeed , after entrainment by 5 heat cycles , SDC transcripts displayed significantly slower re-silencing kinetics than SQN and APUM9 ( Figure 1C ) . In this experiment , we also assayed some heat-induced transposable elements from different families . We observed cases of fast and slow re-silencing , suggesting that both patterns are possible in TGS targets ( Figure S2 ) . The SDC promoter contains tandem-repeats targeted by the TGS machinery and it is possible that this particular promoter structure contributes to the observed transcriptional entrainment . To address this , we constructed a vector containing the SDC promoter linked to the luciferase reporter ( -1200PromSDC::LUC+ ) and transformed A . thaliana Col-0 wild type and the drm2-2/cmt3-11 double mutant ( referred to as dc ) , which is deficient in RdDM and CMT3-mediated DNA methylation responsible for SDC silencing [13] . In dc transgenic plants under control conditions , a strong luciferase signal was recorded from -1200PromSDC::LUC+ throughout entire seedlings , implying that the SDC promoter does not require heat for activation ( Figure 2A ) . In the Col-0 transgenic plants , -1200PromSDC::LUC+ was transcriptionally suppressed but remained responsive to activation by heat stress ( Figure 2A ) , demonstrating that DNA methyltransferases targeted the -1200PromSDC::LUC+ transgene and the promoter of the endogenous SDC gene in a similar way . Closer examination of the luciferase signals showed them to be highest in young true leaves , lower in cotyledons , and absent from roots ( Figure 2A ) . To determine whether the transcriptional regulation of the -1200PromSDC::LUC+ transgene indeed reflects the heat-induced activation and developmental regulation of the SDC gene , we compared their relative transcript levels in various tissues of seedlings subjected to heat stress . The levels and tissue distribution of mRNA were very similar for both transgenic and endogenous loci , with highest heat induction in young leaves and the lowest in roots . Moreover , they clearly differed from the expression patterns of typical heat-responsive genes , which are induced ubiquitously throughout all seedlings tissues ( Figure 2B and S3A ) . After entrainment for 5 heat-cycles , the 1st and 2nd leaves of -1200PromSDC::LUC+ Col-0 plants showed high transgenic transcript levels and high luciferase signals , decreasing during the recovery phase with kinetics similar to that observed previously for SDC ( Figure S3B ) . Interestingly , leaves 3 to 5 developed during the 4 days of recovery and also showed luciferase signals ( Figure 2C ) . Furthermore , when older plants were subjected to heat stress , marked luciferase signals were found mostly in developing leaves 5 and 6 but were largely absent from older leaves ( 1 to 4 ) developed before stress application ( Figure S3C ) . This indicated that not fully expanded young leaves , or possibly even their primordia in the apical meristem , respond predominantly to heat stress by activation of the SDC promoter . Moreover , the slow re-silencing kinetics of endogenous SDC and the luciferase signals from -1200PromSDC::LUC+ Col-0 plants suggest that the acquired active state is maintained during the maturation of leaves when recovering from stress . We determined that the number of tandem repeats within the SDC promoter in a subset of A . thaliana accessions is typically seven or eight . Therefore , we compared the kinetics of heat-induced SDC activation and re-silencing in these two categories by applying 5 heat cycles and 3 days of recovery . Across all accessions tested , the SDC gene was silent under control conditions and activated by heat stress , suggesting an evolutionary conservation of the heat-induced transcriptional response ( Figure S4 ) . However , the relative transcript levels induced by heat stress and their persistence during recovery varied significantly between accessions ( Figure S4 ) . The observed differences in SDC regulation could not be attributed to the different number of repeats , demonstrating that a slight variation in the genetic constitution of the promoter does not determine differences in the kinetics of heat-induced release of SDC silencing . Next , we compared the pattern of transcriptional responses of SDC to typical heat-responsive loci . The thermal threshold of their activation was tested with daily increases in the ambient temperature of growing seedlings ( between 28°C and 36°C , Figure 3A ) . Heat-responsive genes were already activated transcriptionally when plants were moved from 21°C to 28°C , and transcript levels further increased stepwise with increasing temperature ( Figure 3A and Figure S5A ) . However , the SDC locus displayed a distinct thermal threshold for activation within a window of 2°C , from 32°C to 34°C ( Figure 3A ) . A further experiment using a single-step change in temperature yielded similar results , demonstrating that the narrow thermal threshold was independent of the temperature applied on the previous day ( Figure 3B and Figure S5B ) . In a heat time-course ( 76 h at a constant 37°C ) , expression of the typical heat-responsive genes peaked rapidly 3 h after the start of the treatment and remained at high levels relative to the control conditions . However , the accumulation of SDC transcripts showed no peak but developed in proportion to the length of the heat stress ( Figure 3C and S5C ) , similar to the responses of other epigenetically regulated loci [14] , [16] , [17] . Notably , SDC activation occurred only when the heat stress reached the thermal threshold , unlike the heat-responsive genes ( Figure 3D and Figure S5D ) . Overall , similar activation patterns to SDC were observed for the luciferase transcript from -1200PromSDC::LUC+ Col-0 transgene ( Figure S5A and C ) . Taken together , these data support the notion that the particular transcriptional regulation of SDC takes place independently of canonical heat-shock perception and signaling . To assess whether compromised heat stress tolerance contributes to SDC regulation , we tested the heat stress hypersensitive mutant hot1-3 , which is impaired in the Heat Shock Protein 101 ( HSP101 ) [21] . Heat-induced SDC transcription , re-silencing and thermal-threshold patterns in hot1-3 were identical to that in wild-type plants ( Figure 4A and B ) . Moreover , experiments performed with this mutant should be indicative of the relative level of heat stress perceived by plants under our experimental conditions . Transcriptional regulation of typical heat-responsive genes was not altered in hot1-3 , consistent with the heat stress levels applied in our experimental conditions being relatively low ( Figure S6 ) . Since transcription of SDC is suppressed during vegetative growth by DNA methylation and possibly other epigenetic mechanisms , we examined SDC transcriptional heat-stress responses , re-silencing kinetics , and thermal threshold properties in mutants impaired in various aspects of TGS . The transcriptional heat-stress responses of SDC observed in these epigenetic mutants could be divided into three categories: a ) not different to the wild type ( kyp-7 and suvh2 ) ; b ) almost complete release of SDC silencing and thus loss of additional transcriptional activation induced by heat ( dc ) ; c ) partial release of SDC silencing under control conditions but heat-stress induction maintained ( nrpd1-3 , nrpd2a-2/2b-1 , nrpe1-2 , rts1-1 , bru1-4 , mom1-2 , ddm1-2 and met1-1 ) ( Figure 4A ) . In the latter category , re-silencing of SDC to control levels was largely impaired in some mutants , especially in mom1-2 , ddm1-2 and met1-1 ( Figure 4A ) . Importantly , typical heat-responsive genes displayed unaltered transcriptional responses across all the mutants tested ( Figure S6 ) , indicating that canonical heat-shock signaling is not influenced by the epigenetic mechanisms examined here . We further tested whether the specific thermal threshold for SDC activation is affected by the mutations in epigenetic regulation . The threshold was clearly disturbed in these mutants and was moved towards lower ( mom1-2 , ddm1-2 and met1-1 ) or higher temperatures ( bru1-4 ) ( Figure 4B ) . Because MOM1 and BRU1 influence the stability of particular chromatin states at target loci [18] , [20] , [22] but their mutated alleles do not affect DNA methylation within the SDC promoter [23] , their effect on the thermal threshold is consistent with the chromatin state per se being a candidate for the threshold regulation . We examined DNA methylation and a set of histone modifications within the SDC locus in control and heat-stressed plants but found no evidence for major stress-induced alterations in these epigenetic marks ( Figure S7 and S8 ) . Therefore other as yet undefined chromatin properties may determine the narrow temperature range of the transcriptional activation of the SDC gene . SDC is an imprinted locus , with maternal allele activation during endosperm development and otherwise silent during the entire vegetative growth [24] , [25] . To test whether additional endosperm-imprinted genes may be subjected to stress-triggered transcriptional activation , we examined 93 genes for SDC-like transcriptional activation triggered by environmental stress . These 93 , selected from 114 previously confirmed endosperm-imprinted genes [24] , were represented on the Affymetrix GeneChip ATH1 used in experiments with plants subjected to various stress conditions [26] . A number of stresses including cold , osmotic stress , salinity , wounding , oxidative stress , UV-B irradiation and heat were able to induce transcription of many of these genes during vegetative growth ( Figure S9 ) . Although these results suggest that they may contribute to the stress responses and possibly stress tolerance , this hypothesis requires further experimental support , comparable to the study on the SDC gene described below . The sdc mutant displays neither seed nor somatic developmental abnormalities [13] , [25] and , thus , it's physiological or developmental role remains unknown . However , epigenetic suppression of SDC seems to be required for proper plant development . DNA methylation mutants such as dc display abnormal phenotypes during vegetative growth , which can be attributed to high ectopic activity of SDC , given that the phenotype is suppressed in the sdc/dc mutant [13] . Therefore , we tested the effect of SDC induction in vegetative tissues under elevated temperatures , comparing the heat stress responses of wild type to the sdc , dc and sdc/dc mutants . Under our standard heat-entrainment/recovery conditions , transcripts levels of typical heat-responsive genes did not change in any of these mutants ( Figure S10 ) , indicating unaltered heat perception and signaling . As a consequence , we did not expect any disturbance of heat shock-induced acquired-thermotolerance [27] , so we performed a non-lethal long-term heat stress experiment of wild type and sdc , dc or sdc/dc . Seven-day-old seedlings received 15 entrainment heat-cycles followed by 3 days of recovery and were then grown in soil for a further 15 days under standard conditions , before harvesting and determination of their total aerial fresh weight . The sdc and sdc/dc mutants showed significantly reduced biomass than the corresponding controls , wild-type and dc respectively ( Figure 5A , left ) . Absence of a functional SDC gene accounted on average for approximately 30% of biomass deficit ( Figure 5A , right ) , suggesting a role for the SDC protein in the response to long-term heat . The growth of sdc/dc was more affected by non-lethal heat stress than sdc or dc separately , suggesting that mutations leading to depletion of CHG and CHH DNA methylation may not behave epistatic to a mutation of SDC under specific heat stress treatments . To test this hypothesis in an independent experimental setup , we examined the survival of wild type , sdc , dc , and sdc/dc under moderately high temperatures that resulted in 50% lethality of the wild type [27] . Seedling survival was scored after growth consecutively at 21°C , 35°C , and then 21°C , each for 7 days . The wild-type , sdc and dc lines showed survival rates of approximately 50% but the survival of sdc/dc was significantly lower at 13% ( Figure 5B ) . These results are consistent with at least two parallel pathways contributing to recovery from moderately high temperatures; the first mediated by SDC activity and the second involving epigenetic regulation of CHG/CHH methylation , potentially influencing the transcriptome . As a consequence , we compared the transcriptomes of wild type , sdc , dc and sdc/dc during recovery from heat stress entrainment ( Table S1 ) . Compared with the wild type , a total of 109 , 840 and 913 loci were differentially regulated in sdc , dc and sdc/dc , respectively ( Figure 5C ) . Selected genes found to be altered in the transcriptome analysis were validated in an independent experiment , using qRT-PCR ( Figure S11 ) . The 109 genes differentially regulated in sdc are involved in a wide range of cellular processes ( Figure S12A ) . Of these , 68 were represented on the Affymetrix GeneChip ATH1 microarray used earlier for expression profiling that revealed genes differentially expressed during a prolonged heat treatment that led to release of TGS [14] . Out of these 68 loci , transcript levels of 27 ( ca . 40% ) changed under long-term constant heat [14] ( Table S1 ) , supporting the notion that SDC is involved in the regulation of a sub-set of responses to heat stress . Surprisingly , one-third of the genes with altered transcript levels in sdc were altered similarly in dc ( Figure S12B ) . This raises the possibility that SDC controls a sub-set of genes regulated by CHG and CHH methylation . To determine whether this regulation is mediated directly by changes in DNA methylation , we examined available DNA methylation data and observed that none of the loci are subjected to DNA methylation , either in wild-type or in epigenetic mutants [23] . Therefore , the influence on their transcription in both sdc and dc mutants seems to be indirectly linked to DNA methylation . Although approximately two-thirds of the loci in dc or sdc/dc that differed from wild type overlapped and encoded mostly transposons activated in the dc mutant , approximately one-third ( 297 ) displayed transcriptional changes linked specifically to sdc/dc and not shared with either sdc or dc ( Figure 5C ) . The sdc/dc miss-regulated transcripts observed during heat stress recovery are potentially linked to the higher heat sensitivity of the sdc/dc mutant . Indeed , 163 of these 297 transcripts were represented on the Affymetrix GeneChip ATH1 microarray used for profiling after long-term heat as described before [14] , and 48 of them ( ca . 30% ) were altered under these condition ( Table S1 ) . This is again consistent with the involvement of SDC in heat-stress responses , acting together but only partially redundantly with activities involved in the maintenance of CHG and CHH methylation . Taken together , the gene expression profiling data and the observed altered recovery of sdc and sdc/dc mutants point towards the physiological significance of SDC during plant vegetative growth in adverse environmental conditions . SDC is an F-Box protein putatively involved in ubiquitin-mediated degradation of target proteins by the proteasome [13] but its substrate ( s ) are unknown . Our attempt to recover potential target interactors using high-throughput tandem-affinity-purification/mass-spectrometry with a TAP-tag fusion [28] with SDC was unsuccessful . However , since the activities of the RdDM pathway and CMT3 that influence SDC expression are restricted to the nucleus , we used a vector containing the ubiquitin promoter linked to the coding sequence of SDC fused to the GFP reporter ( UBQ10::SDC-GFP ) in transient transformation assays and obtained clear evidence for the nuclear localization of the SDC-GFP signal ( Figure S13 ) .
Epigenetic regulation , typically involving modification of histones and/or remodeling of chromatin , has been implicated previously in plant responses to biotic and abiotic stress [29] . Components of the RdDM pathway and histone deacethylase activity seem to support the survival of plants subjected to lethal heat [30] . Moreover , a heat-sensitive mutant has been isolated in which the heat-induced release of heterochromatic silencing is attenuated [17] . However , the physiological significance of TGS disturbance under long-term heat remained largely unknown , and was suggested previously to be merely a consequence of the thermal disruption of protein-DNA and/or protein-protein interactions [14] . The results presented here suggest that one potential role of silencing release may be the transient expression of epigenetically suppressed loci that encode genes whose activities contribute to stress tolerance . We provide the example of the epigenetically silenced and imprinted gene SDC , with a physiological role in responses to long-term heat stress . Our findings suggest that the silencing of SDC in vegetative tissues was concealing its involvement in stress responses , when transcriptional reactivation occurs following exposure to heat stress . Interestingly , this appears to take place independently of canonical heat-stress signaling pathways . The expression of a subset of imprinted loci is restricted to the endosperm and although some imprinted genes are active in seed development and maturation , many of them have as yet no ascribed roles in seeds [11] . The results presented here for SDC provide an example that their activity may be revealed under particular growth conditions and that their functions could be executed beyond the tissue of parent-of-origin expression . Furthermore , it may be possible that such concealed activities exist for other imprinted genes , under different stress conditions . For example , it was reported previously that pathogens , UV , cold , and freezing treatments may also transiently disturb epigenetic silencing [15] , [31] , [32] . In line with this , stress-induced change in DNA methylation has been proposed to impart regulatory control over defense genes that become activated by pathogen attack [32] . Our analyses of published data demonstrated that some endosperm-imprinted genes can be activated by various environmental stresses . Thus , it is plausible that imprinted or epigenetically suppressed loci may exert their activities during vegetative growth upon trigger-specific destabilization of TGS . However , the interactions of particular stress ( es ) /gene ( s ) require detailed studies of individual examples . It was shown previously that heat-induced release of silencing occurs across all plant tissues [16] . However , in the case of SDC , TGS destabilization seemed to occur mostly in young , expanding leaves . Together with the decreased shoot biomass observed with the sdc mutant following heat stress , these results implicate this gene in the expansion/maturation of leaves of plants exposed to high temperatures . This reinforces the current concept that epigenetic silencing may bring about new and unexpected plasticity to gene regulation and plant phenotypes [33] . As a remarkable example , we also observed that SDC activation occurred only above a certain window of absolute temperature , resembling a thermal-sensing mechanism [34] . The epigenetic machinery may , therefore , mediate transcriptional control of certain stress responses in a threshold fashion , as defined previously [35] . However , it is not currently clear whether the SDC locus itself senses absolute temperature through its dynamic epigenetic landscape . We have shown that the kinetics of SDC re-silencing following variable repeated heat stress is tailored to the previous entrainment , thus displaying transcriptional memory that is especially apparent in the recovery phase . Such a convoluted transcriptional regulation appears also to rely on post-stress epigenetic resetting , since SDC transcripts after heat treatment did not recover to the control levels in epigenetic mutants like mom1-2 , ddm1-2 and met1-1 . This implies that epigenetic regulation in plants stores previous stress experiences during vegetative development . In connection to this , somatic transcriptional memory was demonstrated recently in plants subjected to osmotic stress and this was attributed to dynamic changes in histone modification [36] , [37] . Major stress-induced changes in several common histone marks were not observed within the SDC locus . Also , since the heat-mediated activation of SDC gene occurs in mutants impaired in siRNA biogenesis , a regulatory involvement of siRNAs is unlikely . However , it remains possible that other not tested histone modifications or physical properties of chromatin are responsible for the memory phenomenon . Previously , it have been demonstrated that the transient release of epigenetic suppression under heat stress coincides with decreased nucleosome occupancy , and that chromatin remodelling or assembly factors are a requirement for the fast restoration of silencing [14] , [38] . Although these mechanisms may contribute to the regulation of SDC , publically available data suggest that nucleosome density in its promoter area is already low without heat stress [39] . The SDC protein is present in cell nuclei and belongs to the F-Box protein family , which mediates ubiquitin-tagged degradation of proteins and are among the fastest evolving gene families in plants [40] . It is therefore possible that SDC targets a yet unknown nuclear protein for degradation . The A . thaliana gene Upward Curly Leaf 1 ( UCL1 ) , which encodes a protein very similar to SDC , has been shown to target Curly Leave ( CLF ) [41] . CLF is a histone-methyl-transferase of the polycomb-repressive-complex-2 ( PRC2 ) , which is involved in various aspects of sporophyte development [42] . Despite an intensive search , we failed to reveal a SDC substrate but CLF or CLF-related proteins remain as potential candidates . Regardless of the actual target protein ( s ) , our transcriptome analysis pointed towards the involvement of SDC in the transcriptional regulation of a sub-set of genes responding to long-term heat . We propose a silencing/de-silencing loop model illustrating the thermal control of SDC expression ( Figure 6 ) . In this model , heat-induced destabilization of the suppressive chromatin allows the transcriptional machinery to access the SDC promoter , but this occurs only above a particular window of absolute temperature . Moreover , the level of transcriptional activation depends on the severity and duration of the heat stress . Expression of SDC leading to synthesis and nuclear translocation of the SDC protein subjects certain nuclear protein ( s ) to ubiquitin-mediated degradation . Following termination of the heat stress , slow SDC re-silencing allows a temporal extension of SDC activity . All these regulatory mechanisms occur independently and in parallel to canonical heat-shock perception and signaling , but rely on epigenetic properties . It is likely that these arise through the targeting of TGS to the tandem-repeats residing in the SDC promoter . We propose that two steps , the emergence of these repeats and the subsequent epigenetic control , led to rapid evolution of a novel type of environmentally regulated transcriptional output .
All Arabidopsis thaliana Col-0 mutants used in this study have been described and characterized previously: sdc , dc ( drm2-2/cmt3-11 ) and sdc/dc [13] , met1-1 [43] , mom1-2 [18] , ddm1-2 [1] , nrpd1-3 and nrpd2a-2/2b-1 [44] , nrpe1-2 [45] , kyp-7 [4] , suvh2 [46] , bru1-4 [23] , rts1-1 ( HAD6 , Aufsatz et al . [47] ) , and hot1-3 [21] . As control wild type , we used the Col-0 line N22681 ( The Nottingham Arabidopsis Stock Centre , NASC ) . The L5-GUS silenced transgenic is the b5b line of Morel et al . [2] , and the LUC+ positive control was the UBQ3::LUC+ line ( named LUC26 ) from Yokthongwattana et al . [20] . The different A . thaliana accessions are available at ABRC ( http://abrc . osu . edu/ ) and NASC ( http://arabidopsis . info/ ) . Seeds were surfaced sterilized and sown in sealed petri dishes with 0 . 5× MS medium containing 1% sucrose , 0 . 8% agar , and 0 . 05% MES at pH 5 . 7 . Stratification was applied for 3 days at 4°C . Seedlings were grown for 7 or 12 days at 21°C in a CU-22L growth chamber ( Percival ) with a 12 h/12 h ( day/night ) light cycle ( termed “standard conditions” ) , and then subjected to changes in temperature only during the light period ( with the exception of the heat time-course and the survival under moderately high temperatures experiments ) . The experimental design for each particular experiment is shown at the top of the corresponding graphs in the Results section . In all cases , the light cycle was maintained . Heat always means 37°C unless otherwise stated . Long-term heat-entrainment experiments consisted of a varying number of heat cycles ( day at 37°C , night at 21°C , 12/12 h ) , followed by 3 days of recovery at 21°C and a second stress treatment of 37°C for 12 h . For the non-lethal long-term heat experiment , seedlings were subjected to 15 days of heat cycles followed by 3 days of recovery . Plants were then transplanted to soil for a further 15 days of recovery in a growth room at 21°C , after which total above-ground fresh weight was determined . For survival under moderately high temperatures , seedlings were growth in vitro consecutively at 21°C , 35°C , and then 21°C , each for 7 days [27] . Total RNA was isolated using the RNeasy Plant Mini kit ( Invitrogen ) . cDNA synthesis and quantitative real-time RT-PCR analysis were performed as described previously [48] using the geometric mean of three housekeeping genes for normalization . In short , 5 ug of total RNA was treated with TURBO DNA-free kit ( Ambion ) and first-strand cDNA was synthesized with an oligo dT primer using the SuperScript III First-Strand Synthesis System ( Invitrogen ) . Real-time PCR was performed with the Power SYBR Green PCR Master Mix ( Applied Biosystems ) in a final reaction volume of 10 µl and with a 1/10 dilution of the cDNA . Cycling and dissociation curves were analyzed in an ABI PRISM 7900HT Sequence Detection System ( Applied Biosystems ) . Primer design , reaction parameters and analysis of expression data were performed as described previously [49] , [50] . We used the geometric mean of three housekeeping genes for normalization; these were UBQ10 ( AT4G05320 ) , SAND ( AT2G28390 ) and PDF2 ( AT1G13320 ) . However , only SAND and PDF2 were used as housekeeping genes for comparisons across A . thaliana accessions . Typical heat-responsive genes were selected from those showing high transcriptional induction under long-term heat [14] . A list of the primers used is available in Table S2 . Whole transcriptome analysis was performed by the Functional Genomics Center of ETH University ( Zurich , Switzerland ) using a HiSeq 2000/2500 ( Illumina ) platform to perform unstranded RNA-seq from purified poly-A RNA obtained from duplicated biological replicates . For annotation , mapping of reads was carried out using gene models from the TAIR10 genome assembly ( http://www . arabidopsis . org/ ) . Statistical analysis was performed with the edgeR Bioconductor package and the false-discovery-rate ( FDR ) was computed with the Benjamini-Hochberg algorithm . A genomic feature was considered differentially changed in a mutant versus wild type comparison when the Benjamini-Hochberg's FDR was <0 . 1 and the log2 fold change was >1 or <−1 . Raw GeneChip Arabidopsis ATH1 Genome Array ( Affymetrix ) data were analyzed with the RobiNA software [51] and the probesets were considered differentially changed by the heat treatment using the above parameters . The non-redundant functional categories of the differentially changed features were assessed with the MapMan software [52] . Mean-normalized expression data of confirmed endosperm-imprinted genes in seedlings under stress was taken from Kilian et al . [26] . For cloning purposes , PCR was performed using the Phusion High-Fidelity DNA Polymerase ( NEB ) and blunt-end products were cloned using the CloneJET PCR Cloning Kit ( Thermo Scientific ) . A fragment of the SDC promoter ( 1198 bp upstream of the ORF ) was PCR amplified from genomic DNA , cloned , sequenced , and re-cloned into a pGPTVII-bar-MCS ( multi-cloning-site ) plasmid using the BamHI/XhoI sites [53] , producing the pGPTVII-bar-1200PromSDC vector . LUC+ was PCR amplified from genomic DNA of a UBQ3::LUC+ line [20] , cloned , sequenced , and re-cloned into the previous construct using the XhoI/XmaI sites , thus producing the -1200PromSDC::LUC+ construct . The ORF of SDC without a stop codon was PCR amplified from genomic DNA , cloned , sequenced , and re-cloned into the pGPTVII-bar-UBQ10-GFP5 using the BamHI/XhoI sites in frame with the GFP5 , giving the UBQ10::SDC-GFP construct . All original pGPTVII binary plasmids were kindly provided by Dr . Rainer Waadt ( University of California SD , USA ) . A list of the primers used is available in Table ST2 . The Agrobacterium tumefaciens pGV3101 strain was used to transform A . thaliana using the standard floral-dip method . Transgenic lines were selected in vitro for resistance to BASTA ( dl-phosphinothricin , Duchefa ) . In vivo measurements of luciferase activity were performed by spraying the treated transgenic seedlings with luciferin ( Biosynth , 1 mM in water ) . After 5 min in the dark , images were captured with a CDD ORCA2 C4742-98 digital camera ( Hamamatsu ) and then analyzed with Wasabi Imaging software . Luciferase activity was detected without a filter , whereas a 632 . 8 nm filter and blue light was used to detect the chlorophyll signal . DNA methylation was analyzed by cloning and sequencing of PCR products from bisulfite-treated genomic DNA , from whole young seedlings subjected to entrainment by 5 heat cycles along the corresponding controls . Genomic DNA was isolated by standard CTAB buffer and further fenol-chloroform extractions and precipitation . Bisulfite treatment was performed with the Epitect Bisulfite Kit ( Quiagen ) . PCR products were amplified with Taq polymerase ( Promega ) using a touch-down PCR strategy and cloned with the pGEM-T Easy Vector System I ( Promega ) . Primer design and analysis of sequences with differentially methylated cytosines were performed with Kismeth and CyMATE [54] , [55] . Samples used to assess histone modifications in the SDC locus were kindly provided by Dr . Herve Gaubert ( University of Cambridge , UK ) . Chromatin immunoprecipitation was performed in tissue from whole young seedlings following a protocol adapted from Gendrel et al . [56] and Nelson et al . [57] . A list of the primers used to test these samples is available in Table ST2 . For cellular localization of the SDC-GFP fusion protein , 4-week-old Nicotiana benthamiana plants were infiltrated with A . tumefaciens carrying the corresponding constructs according to Schütze et al . [58] . After 3 days , pieces of leaves were mounted and the GFP signal from transiently transformed epidermal cells photographed with a confocal LSM 700 laser scanning microscope ( Zeiss ) housed by the UNIGE Bioimaging Core Facilities ( http://www . unige . ch/medecine/bioimaging/index . html ) .
|
In plants , expression of certain imprinted genes is restricted to embryo nourishing tissue , the endosperm . Since these genes are silenced by epigenetic mechanisms during vegetative growth , it has been assumed that they have no role in this phase of the plant life cycle . Here , we report on heat-mediated release of epigenetic silencing and ectopic activation of the Arabidopsis thaliana endosperm-imprinted gene SDC . The stress induced activation of SDC involves epigenetic regulation but not the canonical heat-shock perception and signaling , and it seems to be required for efficient growth recovery after the stress . Our results exemplify a potential concealed role of an imprinted gene in plant responses to environmental challenges .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"and",
"life",
"sciences",
"plant",
"science"
] |
2014
|
Heat-Induced Release of Epigenetic Silencing Reveals the Concealed Role of an Imprinted Plant Gene
|
Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where , both for inference and for assessing prediction uncertainties , it is essential to characterize the system behavior globally in the parameter space . However , current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models . Here , we propose an alternative deterministic methodology that relies on sparse polynomial approximations . We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively . We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables , leading to numerical approximations of the parametric solution on the entire parameter space . The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space . As Monte-Carlo sampling , it is “non-intrusive” and well-suited for massively parallel implementation , but affords higher convergence rates . This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications , including parameter estimation , uncertainty quantification , and systems design .
Chemical reaction networks ( CRNs ) form the basis for analyzing , for instance , cell signaling processes because they capture how molecular species such as proteins interact through reactions , for example , to form larger macromolecular complexes . In the limit of ( sufficiently ) high copy numbers of the molecular species when stochasticity can be ignored [1] , the dynamic behavior of a CRN is described by a parametric , nonlinear deterministic system of ODEs of the form ( see , e . g . , [2] and references therein ) : dx ( t ) dt=f ( x ( t ) , u ( t ) , p ) =Nv ( x ( t ) , u ( t ) , p ) , x ( t0 ) =x0 , ( 1 ) where x ( t ) ∈ 𝓢 = I R ≥ 0 n x is the vector of the non-negative concentrations of the nx molecular species that depend on time t , f ( x ( t ) , u ( t ) , p ) is a system of nx functions that model the rate of change of the species concentrations depending on the current system state x ( t ) and on the parameter vector p = ( pk ) k=1np∈ IR≥0np of dimension np which equals the number of kinetic parameters ( physical constants ) associated with the biochemical reactions . The inputs u ( t ) ∈ I R n u may be time-varying , for example , when external stimuli to signaling networks are being considered . The initial conditions are given by x0 . Here , we follow the notational conventions of the application domain; the mathematical literature usually denotes states and parameters by x and y , respectively . For CRNs , specifically , the right-hand-side f ( x ( t ) , u ( t ) , p ) can be decomposed into two contributions: the stoichiometric matrix N ∈ I R n x × n r that encodes how species participate in reactions ( its entries correspond to the relative number of molecules of each of the nx species being consumed or produced by each of the nr reactions ) , and the vector of nr reaction rates , or fluxes , v ( x ( t ) , u ( t ) , p ) ∈ I R ≥ 0 n r . Using ODE models Eq ( 1 ) to analyze cellular networks is challenging , in particular , because np is large and the parameter values are usually unknown . For instance , enzyme kinetic parameter values are distributed over several orders of magnitude [3] , making it often difficult to ascertain even rough estimates when the parameter values cannot be determined experimentally . In practice , parameter values need to be estimated from experimental observations such as time-course data of species concentrations , which typically involves solving computationally expensive global optimization problems [4] . In addition , mainly due to limited measurement capabilities and a still prevailing shortage of quantitative experimental data , most of the established ( systems biology ) models have ‘sloppy’ parameters . That is , their values are not sufficiently constrained by the data used for estimation , or some parameters are even redundant , for a given set of measurement data . These parametric uncertainties may propagate to large uncertainties in model predictions [5 , 6] . In parameter estimation and uncertainty quantification , one needs to determine how the system behavior x ( t ) depends on the parameters p , ideally on the entire ( physically feasible ) parameter space . While local evaluations in parameter space may suffice in certain cases , for instance , methods for Bayesian inference of model parameters and topologies [7 , 8] are global by design , making the last aspect a critical requirement . In systems biology ( most of the ensuing considerations apply beyond systems biology ) , two broad classes of approaches to computational quantification of parametric uncertainty can be distinguished . So-called local methods rely on parameter sensitivities sk ( t , p ) =∂x ( t ) ∂pk|p=p0 ( 2 ) that provide first-order approximations of the systems’ behavior when the k-th parameter , pk , has small variations around the nominal parameter set p0 = ( p0k ) k=1np . Parameter sensitivities allow for an assessment of , for instance , metabolic network behavior in response to small parametric perturbations [9] . However , as systems biology models are typically highly non-linear , and calibrations to noisy data may access parameter values that are far from p0 , the scope of local approximations is limited . For example , the response of the two-dimensional example model shown in Fig 1A appears ‘simple’ , but a first-order approximation of the response becomes increasingly inaccurate with increasing distance from the nominal parameter set ( Fig 1B ) . Sampling-based methods , in contrast , attempt to cover the entire parameter space . For large networks , high-dimensional parameter spaces need to be explored , and due to the so-called “curse of dimensionality” [10] , this entails sample numbers ( and thus , computation time ) that increase exponentially with the dimension of the parameter space . In addition , limited prior knowledge on parameter regimes and location of disconnected regions in parameter space often limit targeted or adaptive sampling strategies . State of the art Monte-Carlo methods have been reported to cope with up to 50 model parameters [8 , 11] , but present CRN models in systems biology may have several hundred parameters [12] . Hence , not only for the efficient computational forward and Bayesian inversion analysis of large-scale models representing entire cells [13] , but also for pathway models [14] , efficient computational methods with mathematically founded , favorable scaling of work versus accuracy with respect to the model size are lacking . One possible avenue for developing more efficient computational methods consists of exploiting specific features of the application domain ( models ) , which proved successful for determining local parameter sensitivities [15] . For CRN models which arise in systems biology such as Eq ( 1 ) , one can exploit that many cellular reaction networks are only weakly connected . This is reflected in sparse ( but not block diagonalizable ) stoichiometric matrices N , and in the scale-free structure of many large-scale networks that comprise a few hubs with many connections , whereas most species have few connections [16] . In addition , if one considers only mass-action kinetics , the reaction rates can be written as v ( x ( t ) , u ( t ) , p ) = diag ( p ) ρ ( x ( t ) ) + O u ( t ) , where O ∈ ℕ n r × n u defines the input-to-rate mapping and ρ ( x ( t ) ) is a vector of monomials in the states x ( t ) [17] , revealing an overall affine parameter dependence of f ( x ( t ) , u ( t ) , p ) . Here , we propose a novel , adaptive deterministic computational methodology for handling parametric uncertainty for high dimensional parameter spaces with particular attention to large , parametric nonlinear dynamical systems in CRN models . We exploit recent mathematical results [18] stating that responses of systems models with sparse and affine dependence on these parameters can be captured by sequences of polynomial approximations such that the approximated responses converge to the exact responses with rates that are independent of the dimensions of the parameter and state space . The presently proposed approach adaptively exploits this sparsity . It provably allows to adaptively scan system responses across the entire , high-dimensional parameter space with less instances of ( possibly costly ) forward simulations than with sampling methods to reach prescribed numerical accuracies of the responses . It also allows to build parsimonious parametric surrogate models that are valid over the entire parameter space . To demonstrate our methodology’s performance , we apply it to three published systems biology models , where the numerical results support the theoretical prediction of dimension-independent convergence rates beyond the rate 1/2 for Monte-Carlo sampling methods .
We propose an adaptive deterministic algorithm that relies on constructing sparse interpolation and quadrature grids in high-dimensional parameter spaces as outlined in Fig 1C . It relies on so-called Smolyak sparse grids [19] that exploit that for functions in high dimensions , not all parameter points are equally important to approximate the function . The Smolyak method can employ different sequences of univariate quadrature formulae; here , we focus on the generation of grid points using the Clenshaw-Curtis method ( CC; see S1 Text for details ) . Correspondingly , the principle of our adaptive Smolyak sparse grids method is to start from a single parameter point and to iteratively evaluate the effect of adding neighboring points in certain directions of the parameter space , until we fall below a predefined numerical error tolerance . Note that here and in the following , ‘error tolerance’ refers to numerical accuracy and not to model properties such as robustness . This principle is illustrated in Fig 1D for the two-dimensional example model , where k denotes the iteration . In particular , the directions in which the most points are added correspond to the parameters for which the model is the most responsive . Once the points to be added ( ‘activated’ ) are determined for one iteration , simulations to determine the function values are independent of each other , allowing for a parallelization of computations . Note , that the effect of adding points in more than one parameter space direction simultaneously is not evaluated , since this is ( often ) computationally intractable . However , for certain functions such as the example model , the approximation resulting from few ( five , in this case ) iterations may be highly accurate over the entire domain in parameter space ( Fig 1E ) . In the following , we focus on why subsets of CRN models allow for sparse interpolation and quadrature with dimension-independent convergence ( numerical error tolerance ) properties , and for mathematical details we refer the reader to the S1 Text and to [18 , 20] . Note also that an implementation of the method ( for model 1 discussed in the Results section ) is available as S1 File . We consider models of the form of Eq ( 1 ) with reactions based on mass-action kinetics . For physically realistic reactions with at most two educts and a bounded parameter domain , this implies: v j ( x , u , p ) = p j ρ j ( x ) + ∑ k = 1 n u o j k u k = p j x l x m + ∑ k = 1 n u o j k u k , j = 1 , … , nr , for some given indices ( depending on j ) l , m ∈ [1 , … , nx] , where the parameters pj ≥ 0 and pj ∈ [aj , bj] . To save space we write x ≔ x ( t ) and u ≔ u ( t ) . The right-hand-side of the ODE for state variable xi , i ∈ [1 , … , nx] , is: f i ( p , x , u ) = ∑ j ≥ 1 n i j v j = ∑ j ≥ 1 n i j p j ρ j ( x ) + ∑ j ≥ 1 n i j ∑ k = 1 n u o j k u k , ( 3 ) where nij and oij are the elements on row i and column j of N and O , respectively . For models of the form of Eq ( 3 ) , the solution x ( t , p ) may be approximated with a surrogate model based on truncated polynomial expansions in parameter space . The adaptive sparse quadrature approach requires parameter ranges that are of unit size , and symmetric about zero . To this end , we rescale the parameters by an affine reparametrization: p j = b j − a j 2 p ~ j + b j + a j 2 , where p ~ j ∈ [ − 1 , 1 ] . Then , with ϕ:j ( x ) =n:j ( bj−aj ) 2ρj ( x ) , denoting by n:j the jth column of N , Eq ( 3 ) takes the form: fi ( · ) =∑j≥1p˜jϕij ( x ) +∑j≥1nijbj+aj2ρj+∑j≥1nij∑k=1nuojkuk︸=:ϕi0 ( x , u ) ( 4 ) where the last two terms summarized by ϕi0 ( x , u ) are independent of the model parameters . The domain of the parameters is then given by the Cartesian product U = [ −1 , 1 ]np . Assume an infinite number of terms in Eq ( 4 ) . Now let σ be the maximal value of s for which ∑j=1∞∣Lj∣s<∞ holds , where Lj is the Lipschitz constant of ϕj ( i . e . , ‖ ϕ j ( x ) − ϕ j ( x ′ ) ‖ ‖ x − x ′ ‖ ≤ L j for ∀x ∈ U ( x′ ) , where U ( x0 ) is the neighborhood of any feasible state vector x0 ) . The approximation error ( difference between the original model and the computational surrogate model ) is then bounded by CM−r , where M denotes the number of forward simulations , r = 1 σ − 1 and 0 < σ < 1 and C > 0 is a constant that is independent of the system size [18] . Furthermore , the Lipschitz constants for ϕj ( x ) can be made arbitrarily small by adjusting the distance between aj and bj due to the rescaling of the parameter range . The performance of the adaptive Smolyak method typically improves once we constrain admissible parameter ranges to small neighborhoods near nominal values . For CRN models the number of reaction terms in Eq ( 3 ) is finite , but possibly ( very ) large . Then the error bound CM−r obtained in [18] in the infinite-dimensional case is valid , with C and r independent of the system size . Importantly , the convergence rate r is independent of the dimension of the parameter space ( the number of model parameters ) . It depends only on the sparsity σ ∈ ( 0 , 1 ) afforded by a system’s kinetic description . Here , the term sparsity does not refer to sparsity in the CRN connectivity graph , but to the frequency of appearance of large coefficients in ( generalized ) polynomial chaos expansions ( ‘gpc’ expansions , for short ) of the parametric systems’ responses; it is mathematically encapsulated as “p-summability of the gpc coefficient sequence” . This has recently been established for high-dimensional CRN models based on mass-action kinetics [18] . There , a large number of “almost” decoupled subsystems increases sparsity in polynomial expansions of parametrized system responses , which is favorable for performance of our adaptive Smolyak algorithms . This convergence rate should be compared to that of conventional tensor product interpolation methods , which decreases with the dimension np of the parameter space . For illustration , consider the following linear model ( see [20] for numerical experiments ) : d x d t = ∑ j = 1 ∞ p j j - s x + u , ( 5 ) where s > 1 , and the number of parameters is infinite . By comparing Eq ( 5 ) to Eq ( 3 ) we have that: ϕj ( x ) = j−s x . Therefore the Lipschitz constant Lj for ϕj ( x ) is j−s , and: ∑ j = 1 ∞ | L j | σ = ∑ j = 1 ∞ ( j - s ) σ = ∑ j = 1 ∞ j - s σ . ( 6 ) It is well known that the series ∑ j = 1 ∞ j − q converges for q > 1 [21] . Therefore the sum in Eq ( 6 ) converges for sσ > 1 and for σ > 1 s . Note that the larger the value of s , the smaller the potential values of σ , and the larger the convergence rate: r = 1 σ − 1 . With the final surrogate model , we can compute the expected value ( and possibly higher moments ) for modeled system properties . Typically , system properties that have not been ( or cannot be ) experimentally measured are of interest . The expected value of a quantity Φ ( p ) , in the rescaled parameter region U , reads: E [ Φ ( p ) ] = ∫ U Φ ( p ) p ( p | D ) d p = ∫ U Φ ( p ) p ( D | p ) p ( p ) p ( D ) d p ( 7 ) where D are the experimental data , p ( p∣D ) is the posterior distribution given data D , p ( D∣p ) is the likelihood , and p ( p ) is the prior distribution . We assume additive , Gaussian observation noise . The measurement model for K experimental observables and nt time instances is of the form: y = h ( p ) + η , η ∼ 𝓝 ( 0 , Γ ) . The likelihood then takes the form of a ( inverse ) covariance-scaled least squares functional p ( D∣p ) ∼ ∏k=1ntexp ( −12 ( yk−hk ( p ) ) TΓk−1 ( yk−hk ( p ) ) , where yk ∈ D is the data at observation time tk . Marginalizing over the parameter space , we compute the evidence p ( D ) as p ( D ) = ∫ U p ( D , p ) d p = ∫ U p ( D | p ) p ( p ) d p . ( 8 ) Such an explicit computation of the evidence is computationally inexpensive for surrogate models based on sparse gpc approximations ( it may not be necessary for all applications , however ) . Sparsity in the parametric solution of Eq ( 1 ) , with the right-hand-side defined in Eq ( 3 ) , implies sparsity in the parametric posterior distribution . Hence , the integral in Eq ( 8 ) ( and Eq ( 7 ) ) computed with an output-adapted sparse grid with M points converges with rate CM−r where C > 0 and r depends only on the sparsity σ , as discussed above . This should be compared to the Monte Carlo approach ( e . g . [22] ) . Here , the expected value of Φ ( p ) is estimated by the finite sample average E M [ Φ ( p ) ] ≔ 1 M ∑ i = 1 M Φ ( p i ) ( 9 ) where the sequence of parameter samples pi , i = 1 , … , M is i . i . d drawn from the posterior distribution p ( p∣D ) ( e . g . , see the randomized Metropolis-Hastings Markov chain Monte Carlo ( MH-MCMC ) method [23] ) . The asymptotic convergence rate of the sample average Eq ( 9 ) as the number M of samples ( i . e . , the number of forward simulations ) tends to ∞ is bounded by ∥ E M [ Φ ( p ) ] - E [ Φ ( p ) ] ∥ L 2 ≤ M - 1 / 2 ∥ Φ ( p ) ∥ L 2 . ( 10 ) The ( mean square w . r . t . the prior ) convergence rate 1/2 ( to be distinguished from the actual computational work , which increases linearly with the number of parameters ) Eq ( 10 ) is also independent of the dimension of the parameter space . However , this rate is low ( at most = 0 . 5 , implying in particular that error reduction by a factor 1/2 mandates four times as much work ) compared to the convergence rate afforded by the adaptive Smolyak process .
The availability of nutrients plays a major role for the survival , growth , and proliferation of microorganisms such as the yeast Saccharomyces cerevisiae . Glucose specifically is imported into the cells and directly processed in the glycolytic pathway . Yeast prefers glucose over other carbon sources such as fructose and mannose and it therefore possesses intricate mechanisms for glucose sensing . However , the initial mechanisms for glucose sensing and activation have often turned out to be more difficult to elucidate than downstream components and their functions [24] . A predictive model of glycolysis would therefore be of great interest and efforts have already been made in this direction [25] . However , although the stoichiometric properties of glycolysis are well characterized , the kinetics of individual reactions are difficult to infer . A model for the first steps of glycolysis , characterized by facilitated diffusion of glucose into S . cerevisiae cells , has been presented in [26] . In a detailed version of this model with 9 states and 10 parameters , which serves as our small-scale test case , glucose import is inhibited by glucose-6-phosphate ( G6P ) ( see Fig 2A , S1 Text for details , and S1 File for an implementation of the adaptive Smolyak method for this model ) . In the forward analysis , we focused on the effects of changes in parameters on the dynamics of metabolite concentrations ( internal and external glucose , internal G6P ) that can be measured with state of the art experimental methods such as mass spectrometry [27] . The adaptive Smolyak interpolation of the corresponding model states shows a convergence rate of 1 with respect to the number of ODE solves needed ( Fig 2B; see also S1 Text ) to achieve the given accuracy ( 2 × 10−5 ) in terms of the difference between the original and surrogate model uniformly over the parameter space . To investigate how the accuracy of our algorithm compares to a first-order approximation , we conducted a local sensitivity analysis and observed a gain in accuracy of two orders of magnitude , at comparable work ( Fig 2C and S1 Text ) . Importantly , with the Smolyak method it is also possible to efficiently compute other system characteristics on the entire parameter domain with a prescribed accuracy . We conducted numerical studies on the inverse problem in the context of Bayesian parameter estimation . In the glucose model , such estimation may aim at identifying the concentration of individual carrier complexes over time , which are significantly more difficult to measure with available experimental methods . For Bayesian inference , the adaptive Smolyak algorithm shows a similar convergence behavior as in the forward problem ( Fig 2D ) . However , for some levels of noise in the artificial data we observe a slightly worse convergence rate ( approximately 0 . 65 over 105 ODE solves; Fig 2E ) , because parameter sets with high posterior probability constitute a small part of the total parameter space . We also compared these results to those obtained from running a Metropolis-Hastings Markov chain Monte Carlo ( MH-MCMC ) algorithm on the same data , resulting in the same posterior distributions and showing that our implementation is accurate . Notably this was achieved with significantly less computational effort than with MH-MCMC ( Fig 2E ) . These results indicate the potential of the Smolyak algorithm for the efficient forward analysis and Bayesian inversion . However , the difference in performance between the algorithms can be expected to be significantly larger for high-dimensional applications . Finally , we focused on the biological interpretation of the numerical results with respect to the mechanisms for glucose transport that are most relevant ( under our particular choices of observations for the forward problem and the selected domain in parameter space ) . Fig 2F shows the activation of indices ( grid points ) per parameter dimension in the forward problem . Visually , it is apparent that different parameters required different numbers of interpolation points and interpolation orders . We quantified this behavior by an index activation , that is , the total order of active interpolants normalized by the number of iterations . While overall the index activation is rather homogeneous ( Fig 2G ) , the approximation of the model behavior depends substantially less on parameters k3 and k−3 , which relate to the forward and backward directions of the reaction for binding of intracellular G6P to the glucose bound carrier ( E-Glc ) at the inner region of the cell membrane ( see Fig 2A ) . This reaction is part of a hypothesized inhibition of glucose transport by G6P [26] , indicating that the reaction may not exist in reality ( under the conditions assumed for the numerical analysis ) . In contrast to first-order sensitivity analysis , this result is not pertinent to a nominal model parametrization only . More generally , this indicates that the proposed Smolyak sparse grid method can be employed for the detailed analysis of parameter dependencies ( and eventual model order reduction ) of systems biology models . To investigate how our method performs for larger , more typical current systems biology models , we applied it to a model of the EGFR pathway response for the first two minutes upon EGF stimulation [28] . This model was used to explain why EGFR phosphorylation peaks at ≈ 30s and returns to low levels at 1–2 min after stimulation , whereas the phosphorylation of other key proteins increases monotonically . Briefly , the model captures short-term signaling induced by EGF in an ‘upstream’ set of reactions leading from EGFR—EGF binding to active ( phosphorylated ) EGFR dimers . The interactions of the active receptor with its cytoplasmic target proteins consists of three coupled cycles of reactions involving Grb2 , Shc , and PLCγ , respectively . Theses cycles feed downstream signaling to targets such as Ras and PI3K [28] . The model has 50 kinetic parameters , whose values were determined based on previous reports and biochemical considerations , leading to a reasonable description of the experimental observations [28] . To identify potential targets for external modification of the pathway behavior ( e . g . , through drugs ) , it is interesting to investigate the sensitivity of the pathway response to the kinetic parameters . In [28] , the system behavior in response to parametric perturbations was reported to be stable “over a wide range of values” , but in the analysis all rate-constants were simultaneously multiplied by a constant factor ( ×2 ) , which only leads to a “scaling of the time” . With our method , it is possible to investigate the response to variations in any combination of the parameters . This is a major advantage , since information about the importance of parameters and all the possible response patterns is generated . The estimated error of the adaptive Smolyak interpolation suggests for this problem a convergence rate of 0 . 75 with respect to the number M of ODE solves needed ( Fig 3A ) . This rather moderate ( but still superior to Monte-Carlo sampling ) rate results from near isotropic refinement of the sparse interpolant in the 50-dimensional parameter space . This is indicated by the sets of activated indices for the adaptive Smolyak algorithm ( Fig 3B ) , where virtually all parameter dimensions require higher-order approximations . Over extended parameter domains , we again find that our method yields results that are approximately two orders of magnitude more accurate than those obtained by first-order parameter sensitivities ( Fig 3C ) . The isotropic refinement for sparse grids questions earlier beliefs on generally ‘sloppy’ models in systems biology and in other domains [5 , 29] that essentially relied on computing local parameter sensitivities . The analysis of ‘sloppy’ models uses a quadratic approximation of the average squared changes in the model states χ2 ( p ) at a nominal parameter point . More specifically , the metric for parameter influences proposed are the absolute eigenvalues λ of the ( quadratic ) Hessian matrix; high ( low ) eigenvalues indicate influential ( non-influential ) parameters . As illustrated in Fig 3D , however , compared to the exact χ2 ( p ) that can be computed with our proposed algorithm , the quadratic approximation may be inaccurate when the model response is asymmetric , or when it changes qualitatively distant from the nominal parameter point . The rank-ordered metrics ( eigenvalues λ for the quadratic approximation and index activation for the sparse grids , respectively ) for the EGFR signaling model correlate significantly , but only poorly ( Pearson rank correlation ρ = 0 . 29 , P = 0 . 04; Fig 3E ) . The most influential parameters identified by the adaptive Smolyak method , however , yield a biologically consistent interpretation . These parameters pertain to receptor autophosphorylation and dephosphorylation ( k3 , V4 , and K4 in the notation of [28] ) as well as active receptor interactions with its direct binding partners Shc ( k13 and k15 ) and PLCγ ( k5 and k7 ) . This indicates that control of active receptor by ( auto ) phosphorylation dominates the model behavior . In contrast , the quadratic approximation would allocate the control to upstream receptor- ligand interactions ( k1 , k−1 , k2 , k−2 are associated with the largest absolute eigenvalues ) . We find another suggested characteristic of ‘sloppy’ models , namely that eigenvalues spread across many decades [5] , also in the EGFR model , but global analysis with a narrowly distributed spectrum of index activations ( Fig 3F ) again questions the accuracy of local approximations , and interpretations thereof . Finally , in the Bayesian inverse problem , which consists of computing the conditional expectation of the first state , unbound EGF , under given ( artificial ) noisy , observational data , the convergence rate was improved to approximately 1 ( S1 Text ) . The improved convergence rate compared to the MH-MCMC method shows the potential of the proposed , adaptive Smolyak approach in particular for larger CRN models with several hundreds of state and parameter variables . We attribute a decrease in the convergence rate for larger parameter variations in the EGFR model ( see S1 Text ) to the more pronounced impact of nonlinearities in the model . In practical applications such as the Bayesian inference of pathway topologies for EGRF signaling in [8] using models of similar size , however , we expect substantial gains in performance compared to sampling-based methods . To investigate how the adaptive sparse Smolyak method performs in high-dimensional parameter spaces we analyzed a model of the epidermal growth factor ( EGF ) and heregulin ( HRG ) activated response in the mammalian ErbB signaling pathways and in the MAPK and Akt cascades [14] . Briefly , the model , formulated entirely in mass-action kinetics , can be seen as a substantial extension of the EGFR model [28] above . It encompasses all four receptor species ( ErbB1-4 ) and their complex interactions explicitly . Degradation pathways via endosomes are represented as well as downstream signaling through the mitogenic Ras/MAPK and the pro-survival PI3K/Akt pathways . Especially the detailed modeling of combinatorial interactions between and at receptor species lead to a model that encompasses 500 states and 229 parameters , making it one of the most complex systems biology models developed to date . In [14] , the authors focused again on short-term signaling , and they found that first-order parameter sensitivities are highly context ( molecular feature and stimulation condition ) specific . However , the model parameters were estimated in a region 2 . 5 orders below and above the nominal values in log-space . Due to the challenges of parameter identification in high-dimensional , nonlinear ODE models , Chen et al . [14] took a pragmatic approach: model parameters were repeatedly estimated , and patterns in the optimization results were then used to infer model properties in order to cope with the issue of identifying parameters in large parameter spaces , as well as the non-identifiability of the model given the experimental data . However , such an approach does not guarantee that the results represent true model properties—they could be strongly biased . A detailed sensitivity analysis of this model revealed extreme parameter sensitivities ( up to 1015 ) , which is summarized in the sensitivity profile Fig 4A . The sensitivity profile is an indicator for the sensitivity of the model w . r . t . each parameter , computed as the maximum absolute value of the sensitivity , at the nominal parameter point , over states and time . Such high sensitivity values render a computational forward analysis , as well as Bayesian inference , infeasible even for moderate parameter variations . To cope with such sensitivities , we therefore initially restricted the range of investigated values for each parameter to ±0 . 01 of the nominal parameter point ( p0 ) . In this region we observe a similar convergence behavior for the adaptive Smolyak interpolation and quadrature as for the two smaller models ( Fig 4B ) . For the computational forward analysis , a gain in accuracy of two orders compared to the first-order approximation and a convergence rate of 1 . 5 can be achieved ( see S1 Text ) . While we refrain from interpretation of the computational results because of the ill-conditioned model , these performance measures indicate that the proposed adaptive Smolyak method can also make large-scale systems biology models amenable to improved ( Bayesian ) parameter identification . We next generated noisy observational data of Akt , Erk , and ErbB phosphorylation at three to four time points for a parameter point in the investigated region . The estimated error of the algorithm indicates a convergence rate of 1–1 . 5 for the normalization constant of the Bayesian posterior ( see S1 Text ) . In this computation , the adaptive Smolyak algorithm identifies the indices with the largest estimated contribution to the quantity of interest , which can be used in subsequent steps to adaptively enlarge the scanned parameter regions for the less-significant parameters . Hence , we propose the following heuristic strategy for adaptations of the parameter domain: we simply enlarge the parameter variations for all parameters not activated at the current stage of the algorithm . In analyzing model 3 , many of the parameters were never activated by the algorithm , indicating that parameter ranges can be made even larger ( arbitrarily large for redundant parameters not affecting the response variables ) . As shown in S1 Text , we obtained promising results with our heuristic strategy , despite the underlying model’s sensitivity issues . We are not aware that sampling-based analysis of a systems biology model of the present scope has ever been achieved .
We propose a sparse , adaptive interpolation scheme for the efficient deterministic computational treatment of parametric uncertainty in complex , nonlinear systems . The methodology is particularly suitable for nonlinear parametric CRN models which commonly appear in computational systems and cell biology . Our numerical analysis of three CRN models that represent the scope of ( current ) model complexity indicates that the error convergence rate of our method is generically superior to that of Monte Carlo methods , in terms of the number of forward simulations required to reach a prescribed error tolerance . Moreover , MC approaches converge only in mean-square ( cf . Eq ( 10 ) ) , whereas the presently proposed methodology delivers “worst-case” , sup-norm convergence rates . As expected , the efficiency of our method increases when many parameters contribute insignificantly to the model response . When the feasible parameter ranges are narrowed to small neighborhoods of the nominal value due to high sensitivities , our adaptive sparse tensor sampling scheme is superior to the widely used ( local ) first-order approximations . Also for “well-behaved” models that are equally sensitive to all parameters we observe a higher convergence rate with the Smolyak based approach . In our test problems , the proposed method consistently achieves relative numerical accuracy of five to seven decimals in typical quantities of interest in prediction and Bayesian inference for CRNs . While this accuracy may be considered excessive given the often substantial levels of measurement uncertainty in available data , we assert that high , certified relative numerical accuracy is necessary to clearly distinguish computational ( e . g . , numerical ) errors from modeling errors ( e . g . , erroneous hypotheses on the CRN or on kinetic rate laws ) , and measurement noise . Our analysis of the EGFR model , for example , demonstrated that numerical parameter dependencies with certified accuracy imply a biological interpretation of sensitive network parts that is different from low-order approximations without such guarantees . Moreover , the postulated prevalence of ‘sloppy’ models in systems biology may need re-evaluation in the light of our findings of nearly isotropic model responses to parameter changes . Our adaptive Smolyak interpolation method also has several other attractive features . Our method as well as MCMC will exactly characterize parametric dependencies in the limit of infinite samples or grid points . However , unlike MCMC , the sparse interpolation process provides a reduced surrogate model upon termination . This model can be quickly evaluated at additional parameter points . Already for moderate-sized models , such as the ERK model , the proposed sparse grid evaluation uses 28 times less CPU time than the ODE solver . Since the surrogate model is based on tensorized polynomial expansions , the computation of distribution moments via ( Smolyak ) integration of the surrogate model over the parameter space is trivial , thereby overcoming a common computational bottleneck of Bayesian analysis . For example , future work could consider Bayesian parameter estimation for the coupled signaling model to increase the model’s realism . Further improvements of our Smolyak based method could focus on a systematic approach for increasing the feasible range of parameter values . We took first steps in this direction in the analysis of model 3 , where the parameter ranges were iteratively extended , with promising results . Another interesting direction is to construct reduced models in an automated fashion , based on sensitivity analysis and quasi-steady state approximations , in different parts of the parameter space . The glucose model provided one example of this approach , identifying mechanisms that are potentially not relevant for overall glucose transport kinetics . The reduced models could then be analyzed in greater detail , e . g . in larger parameter ranges than the original model . Our proposed methodology can extend the range of ( biochemical ) models that are amenable to computational analysis , and thereby the complexity of cellular networks that can be addressed with mathematical models . More generally , recent mathematical results on sparsity in gpc expansions of the parametric system responses for affine-parametric models predict that the proposed methodology can achieve convergence rates larger than 1/2 in terms of the number of forward simulations , free from the so-called “curse of dimensionality” [18] . Sparsity in polynomial chaos expansions of parametric responses due to sparse connectivity patterns in model descriptions appears also in nonlinear models of complex systems in other applications . Our presently proposed methodology for their efficient computational analysis extends , therefore , beyond CRNs from systems biology .
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In various scientific domains , in particular in systems biology , dynamic mathematical models of increasing complexity are being developed and analyzed to study biochemical reaction networks . A major challenge in dealing with such models is the uncertainty in parameters such as kinetic constants; how to efficiently and precisely quantify the effects of parametric uncertainties on systems behavior remains a question . Addressing this computational challenge for large systems , with good scaling up to hundreds of species and kinetic parameters , is important for many forward ( e . g . , uncertainty quantification ) and inverse ( e . g . , system identification ) problems . Here , we propose a sparse , deterministic adaptive interpolation method tailored to high-dimensional parametric problems that allows for fast , deterministic computational analysis of large biochemical reaction networks . The method is based on adaptive Smolyak interpolation of the parametric solution at judiciously chosen points in high-dimensional parameter space , combined with adaptive time-stepping for the actual numerical simulation of the network dynamics . It is “non-intrusive” and well-suited both for massively parallel implementation and for use in standard ( systems biology ) toolboxes .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks
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Nucleic acid sensing by cells is a key feature of antiviral responses , which generally result in type-I Interferon production and tissue protection . However , detection of double-stranded RNAs in virus-infected cells promotes two concomitant and apparently conflicting events . The dsRNA-dependent protein kinase ( PKR ) phosphorylates translation initiation factor 2-alpha ( eIF2α ) and inhibits protein synthesis , whereas cytosolic DExD/H box RNA helicases induce expression of type I-IFN and other cytokines . We demonstrate that the phosphatase-1 cofactor , growth arrest and DNA damage-inducible protein 34 ( GADD34/Ppp1r15a ) , an important component of the unfolded protein response ( UPR ) , is absolutely required for type I-IFN and IL-6 production by mouse embryonic fibroblasts ( MEFs ) in response to dsRNA . GADD34 expression in MEFs is dependent on PKR activation , linking cytosolic microbial sensing with the ATF4 branch of the UPR . The importance of this link for anti-viral immunity is underlined by the extreme susceptibility of GADD34-deficient fibroblasts and neonate mice to Chikungunya virus infection .
During their replication in host cells , RNA and DNA viruses generate RNA intermediates , which elicit antiviral responses mostly through type-I interferon ( IFN ) production [1] , [2] . Several families of proteins are known to sense double-stranded RNA ( dsRNA ) , including endocytic Toll-like receptor 3 ( TLR3 ) [3] , the dsRNA-dependent protein kinase ( PKR ) [4] and the interferon-inducible 2′-5′-oligoadenylates and endoribonuclease L system ( OAS/2-5A/RNase L ) [5] . Viral dsRNA and the synthetic dsRNA analog polyriboinosinic:polyribocytidylic acid ( poly I:C ) are also detected by different cytosolic DExD/H box RNA helicases such as the melanoma differentiation-associated gene 5 ( MDA5 ) , DDX1 , DDX21 , and DHX36 , which , once activated , trigger indirectly the phosphorylation and the nuclear translocation of transcription factors such as IRF-3 and IRF-7 , resulting predominantly in abundant type-I IFN and pro-inflammatory cytokines production by the infected cells [1] , [6] , [7] . Alphaviruses such as Chikungunya virus ( CHIKV ) are small enveloped viruses with a message-sense RNA genome , which are known to be strong inducers of type-I IFN in vivo [8] , [9] , a key response for the host to control the infection [10] , [11] , [12] . In vitro , however , response to RNA viruses is heterogeneous , since Sindbis virus ( SINV ) , do not elicit detectable IFN-α/β production in infected of murine embryonic fibroblasts ( MEFs ) [13] . The specific points of blockage of type-I IFN production during infection are still not well delineated , but SINV and other alphaviruses could antagonize IFN production by shut-off of host macromolecular synthesis in infected cells [14] , [15] , [16] . Recently , human fibroblasts infection by CHIKV was shown to trigger abundant IFN-α/β mRNA transcription , while preventing mRNA translation and secretion of these antiviral cytokines [13] , [15] . Contrasting with these reports , other groups using different CHIKV strains have observed abundant type-I IFNs release in the culture supernatants of CHIKV-infected human monocytes [17] , human lung cells ( MRC-5 ) , human foreskin fibroblasts and MEFs [10] . Type-I IFN stimulation of non-hematopoietic cells has also been shown to be essential to clear infection upon CHIKV inoculation in mouse , but CHIKV was found to be a poor inducer of IFN secretion by human plasmacytoïd dendritic cells [10] . Thus , great disparities regarding alphavirus-triggered IFN responses exist between viral strains and the nature of host cells or animal models . Once bound to their receptor on the cell surface ( IFNAR ) , type-I IFNs activate the Janus tyrosine kinase pathway , which induces the expression of a wide spectrum of cellular genes including Pkr [18] . These different genes participate in the cellular defense against the viral infection . PKR is a serine–threonine kinase that binds dsRNA in its N-terminal regulatory region and induces phosphorylation of translation initiation factor 2-alpha ( eIF2α ) on serine 51 [19] , [20] , leading to protein synthesis shut-off and apoptosis . PKR has been also been shown to participate in several important signaling cascades , including the p38 and JNK pathways [21] , as well as type-I IFN production [22] , [23] . Inhibition of translation , IFN responses and triggering of apoptosis combine to make PKR a powerful antiviral molecule , and many viruses have evolved strategies to antagonize it [24] , [25] . Interestingly , several positive RNA-strand viruses ( eg . Togaviridae or Picornaviridae ) have been shown to activate PKR , resulting in phosphorylation of eIF2α and host translation arrest [26] , while viral mRNA could initiate translation in an eIF2-independent manner by means of a dedicated RNA structure , that stalls the scanning 40S ribosome on the initiation codon [25] . Despite the existence of these viral PKR-evading strategies , the importance of PKR for type-I IFN production has been strongly debated over the years and even considered dispensable since the discovery of the innate immunity function of the DExD/H box RNA helicases [27] , [28] . However , several PKR-deficient cell types have reduced type-I IFN production in response to poly I:C [23] , [29] , [30] , while PKR was demonstrated to be required for IFN-α/β production in response to a subset of RNA viruses including Theiler's murine encephalomyelitis , West Nile ( WNV ) and Semliki Forest virus ( SFV ) , but not influenza , Newcastle disease , nor Sendai virus [31] , [32] , [33] , [34] . These studies raise therefore the possibility that some but not all viruses induce IFN-α/β in a PKR-dependent and cell specific manner . Infection of PKR or RNAse L deficient mice demonstrated that these enzymes were not absolutely necessary for type I IFN-mediated protection from alphaviruses such as SFV or WNV , but still contributed to levels of serum IFN and clearance of infectious virus from the central nervous system [25] , [35] . Similarly , deficient mice for both PKR and RNAse L showed no increase in morbidity following SINV infection , although , like during WNV infection , increased viral loads in draining lymph nodes were observed [35] , [36] . These observations support a non-redundant and cell specific role for these enzymes in the amplification of type-I IFN responses to viral infection more than in their initiation [31] , [32] , [35] . Nevertheless , the exacerbated phenotypes observed upon alphavirus infection of mice deficient for type-I IFN receptor ( IFNAR ) , underlines the limits of the individual contributions of PKR and RNAse L to the anti-viral resistance of adult animals [10] , [35] , [36] . In addition to dsRNA detection , different stress signals trigger eIF2α phosphorylation , thus attenuating mRNA translation and activating gene expression programs known globally as the integrated stress response ( ISR ) [37] . To date , four kinases have been identified to mediate eIF2α phosphorylation: PKR , PERK ( protein kinase RNA ( PKR ) -like ER kinase ) [38] , GCN2 ( general control non-derepressible-2 ) [39] , [40] and HRI ( heme-regulated inhibitor ) [41] , [42] . ER stress–mediated eIF2α phosphorylation is carried out by PERK , which is activated by an excess of unfolded proteins accumulating in the ER lumen [38] . Activated PERK phosphorylates eIF2α , attenuating protein synthesis and triggering the translation of specific molecules such as the transcription factor ATF4 , which is necessary to mount part of a particular ISR , known as the unfolded protein response ( UPR ) [43] , [44] . Interestingly DNA viruses , such as HSV , that use the ER as a part of its replication cycle , have been reported to interfere with the ER stress response through different mechanisms , such as the dephosphorylation of eIF2α by the viral phosphatase 1 activator , ICP34 . 5 [45] , [46] . We show here , using SUnSET , a non-radioactive method to monitor protein synthesis [47] , that independently of any active viral replication , cytosolic poly I:C detection in mouse embryonic fibroblasts ( MEFs ) promotes a PKR-dependent mRNA translation arrest and an ISR-like response . During the course of this response , ATF4 and its downstream target , the phosphatase-1 ( PP1 ) cofactor , growth arrest and DNA damage-inducible protein 34 ( GADD34 , also known as MyD116 and Ppp1r15a ) [48] , are strongly up-regulated . Importantly , although the translation of most mRNAs is strongly inhibited by poly I:C , that of IFN-ß and Interleukin-6 ( IL-6 ) is considerably increased under these conditions . We further demonstrate that PKR-dependent expression of GADD34 is critically required for the normal translation of IFN-ß and IL-6 mRNAs . We prove the relevance of these observations for antiviral responses using CHIKV as a model: we show that GADD34-deficient MEFs are unable to produce IFN-ß during infection and become permissive to CHIKV . We further show that CHIKV induces 100% lethality in 12-day-old GADD34-deficient mice , whereas WT controls do not succumb to infection . Our observations demonstrate that induction of GADD34 is part of the anti-viral response and imply the existence of distinct and segregated groups of mRNA , which require GADD34 for their efficient translation upon dsRNA-induced eIF2α phosphorylation .
We monitored protein synthesis in MEFs and NIH-3T3 cells after poly I:C stimulation , using puromycin labeling followed by immunodetection with the anti-puromycin mAb 12D10 [47] . Poly I:C delivery to MEFs and NIH-3T3 , rapidly and durably inhibited protein synthesis , concomitant with increased eIF2α phosphorylation ( P-eIF2α ) ( Fig . 1A and Fig . S1A ) . In MEFs , a strong eIF2α phosphorylation was observed after 4 h of poly I:C treatment , followed by a steady dephosphorylation at later times ( Fig . 1A ) . Protein synthesis arrest was confirmed in individual cells by concomitant imaging of poly I:C delivery , mRNA translation and P-eIF2α ( Fig . 1B and Fig . S1B ) , and with a wide range of dsRNA concentrations ( Fig . S1C ) . Poly I:C-induced eIF2α phosphorylation and subsequent translation arrest were not observed in PKR-deficient MEFs ( Fig . 1C and 1D ) , while eIF2α phosphorylation induced by the UPR-inducing drug thapsigargin ( th ) ( an inhibitor of SERCA ATPases ) or arsenite ( as ) was unchanged in PKR−/− cells ( Fig . 1C ) . PKR is therefore necessary to induce protein synthesis inhibition in response to cytosolic poly I:C . When levels of IFN-ß were quantified in culture supernatants and compared to total protein synthesis intensity , we found that most of the cytokine production occurred after 4 to 8 h of pIC delivery ( Fig . 1E , WT , and S1D ) , a time at which mRNA translation was already considerably decreased ( Fig . 1A and S1E ) . We measured the amount of cytokine produced in NIH-3T3 cells at a time ( 7 h ) at which translation was already strongly inhibited ( Fig . 1G and 1F ) . To prove that IFN-β production truly occurred during this poly I:C-induced translation arrest , cells exposed for 7 h to poly I:C were washed and old culture supernatants replaced with fresh media for 1 h ( with or without CHX ) , prior translation monitoring ( Fig . 1F , right ) and IFN-ß dosage ( Fig . 1G , right ) . We observed that close to 30% of the total IFN-ß produced over 8 h of poly I:C stimulation is achieved during this 1 h period , despite a close to undetectable protein synthesis in the dsRNA-treated cells ( Fig . 1F ) . The neo synthetic nature of this IFN was further demonstrated by the absence of the cytokine in CHX-treated cell supernatants . IFN-β production in response to poly I:C is therefore likely to be specifically regulated and occurs to a large extent independently of the globally repressed translational context . As previously observed in MEFs , IFN-ß production in response to poly I:C was independent of PKR ( Fig . 1E ) [31] . This suggests that although its production occurs during cap-mediated translation inhibition , it does not directly depend on a specialized open reading frame organization , as described for the translation of the mRNAs coding for the UPR transcription factor ATF4 or the SV 26S mRNA upon eIF2α phosphorylation [26] , [49] . This hypothesis is also supported by the ability of MEFs expressing the non-phosphorylatable eIF2α Ser51 to Ala mutant ( eIF2α A/A ) , to produce normal levels of IFN-ß in response to poly I:C ( Fig . 1E ) , while global translation was not inhibited by poly I:C in these cells ( Fig . S2 ) . We went on to investigate the molecular mechanisms promoting this paradoxical IFN-ß synthesis in an otherwise translationally repressed environment . Induction of eIF2α phosphorylation by PERK during ER stress promotes rapid ATF4 synthesis and nuclear translocation , followed by the transcription of many downstream target genes important for the UPR [50] . Similarly , in presence of PKR , nuclear ATF4 levels were found to be up-regulated in MEFs responding to cytosolic poly I:C , albeit less importantly than upon a bona fide UPR induced by thapsigargin ( Fig . 2A ) . One of the key molecules involved in the control of eIF2α phosphorylation is the protein phosphatase 1 co-factor GADD34 , which relieves translation repression during ER stress by promoting eIF2α dephosphorylation [50] , [51] , [52] . GADD34 is a direct downstream transcription target of ATF4 [53] . Expression of GADD34 was quantified by qPCR and immunoblot in WT and PKR−/− MEFs ( Fig . 2B ) . In WT cells GADD34 mRNA expression was clearly up-regulated ( 20 fold ) in response to poly I:C , while GADD34 protein induction was equivalent in poly I:C- and thapsigargin-treated cells . GADD34 mRNA transcription and translation were not observed in PKR−/− cells responding to poly I:C , but occurred normally upon thapsigargin treatment , paragoning eIF2α phosphorylation ( Fig . 2B , right ) . We next investigated the importance of ATF4 for GADD34 transcription by monitoring the levels of GADD34 mRNA in ATF4-deficient cells . ATF4−/− MEFs displayed higher basal levels of GADD34 mRNA than WT cells . However , in absence of ATF4 , MEFs were unable to efficiently induce GADD34 mRNA transcription in response to any of the stimuli tested ( Fig . S3 ) . GADD34 mRNA expression was induced only 2 fold in ATF4−/− MEFs exposed to poly I:C , suggesting that its transcription is mostly dependent on ATF4 in this context . We further investigated P-eIF2α requirement for GADD34 expression and found that eIF2α A/A expressing MEFs were incapable of up-regulating GADD34 in response to poly I:C ( Fig . 2C ) . Phosphorylation of eIF2α by PKR in response to cytosolic poly I:C induces therefore a specific integrated stress response ( ISR ) , that allows ATF4 translation , its nuclear translocation and subsequent GADD34 mRNA transcription . We next evaluated the relevance of GADD34 induction , by treating WT and GADD34ΔC/ΔC fibroblasts with poly I:C or with drugs known to induce ER stress , such as thapsigargin and the N-glycosylation inhibitor tunicamycin [52] . As expected , in WT cells eIF2α phosphorylation was rapidly increased in response to all ISR-inducing stimuli and decreased concomitantly with the expression of GADD34 over time ( Fig . 3A and S4 ) [52] . Consequently eIF2α phosphorylation was greatly increased in GADD34ΔC/ΔC MEFs in all the conditions tested ( Fig . 3A and S4A ) . In thapsigargin-treated cells , protein synthesis was reduced in the first hour of treatment and rapidly recovered ( Fig . 3B ) [54] . Poly I:C , however , nearly completely inhibited translation despite active eIF2α dephosphorylation . This was particularly obvious when poly I:C was co-administrated together with thapsigargin . Indeed , poly I:C dominated the response by preventing the translation recovery normally observed after few hours of drug treatment ( Fig . 3B ) . Surprisingly , in absence of functional GADD34 , although eIF2α phosphorylation induction by poly I:C was augmented dramatically , no further decrease in protein synthesis was observed upon treatment of GADD34ΔC/ΔC cells with the dsRNA mimic ( Fig . 3A and 3C ) . The functionality of GADD34 in translation restoration was , however , fully demonstrated , when the same cells were treated with thapsigargin , and protein synthesis was completely inhibited by this treatment [52] ( Fig . 3C ) . Thus , cytosolic dsRNA delivery induces a type of protein synthesis inhibition , which requires eIF2α phosphorylation for its initiation , but conversely cannot be reverted by GADD34 induction and subsequent GADD34-dependent eIF2α dephosphorylation . The potential contribution of the OAS/2-5A/RNAse L system to this P-eIF2α-independent inhibitory process was evaluated by investigating RNA integrity in MEFs exposed to poly I:C . We used capillary electrophoresis to establish precise RNA integrity numbers ( RIN ) computed from different electrophoretic traces ( pre- , 5S- , fast- , inter- , precursor- , post-region , 18S , 28S , marker ) and quantify the degradation level of mRNA and rRNA potentially resulting from the activation of this well characterized anti-viral pathway . No major RNA degradation could be observed upon poly I:C delivery ( Fig . S5 ) , suggesting that global RNA degradation does not contribute extensively to the long term translation inhibition observed upon poly I:C delivery in our experimental system . We have observed that GADD34 expression counterbalances PKR activation by promoting eIF2α dephosphorylation , however it has little impact on reversing the global translation inhibition initiated by poly I:C . We next monitored the production of specific proteins and cytokines in WT and GADD34ΔC/ΔC MEFs ( Fig . 4 ) . Cystatin C , a cysteine protease inhibitor was chosen as a model protein , since its secretion ensures a relative short intracellular residency time so that its intracellular levels directly reflect its synthesis rate [55] . This is confirmed by the N-glycosylated- and total Cystatin C accumulation in cells treated with brefeldin A ( Fig . 4A , left panel ) . Cystatin C levels were found to follow a similar trend to that observed with total translation , being strongly reduced upon poly I:C exposure and not profoundly influenced by GADD34 inactivation ( Fig . 4A , right panel ) . Thapsigargin treatment induced a brief drop in cystatin C levels , prior to some levels of GADD34-dependent recovery . 6 hours of tunicamycin treatment affected more cystatin C accumulation than anticipated ( Fig . 4A , right panel ) , probably due to interference with the N-glycosylation and associated folding of this di-sulfide bridge containing protein [55] , thereby promoting its degradation by endoplasmic reticulum-associated protein degradation ( ERAD ) [56] . We next turned towards PKR , which displayed a pattern of expression completely different from cystatin C ( Fig . 4B ) . As expected from its IFN-inducible transcription , levels of PKR were increased in poly I:C-treated MEFs ( Fig . 4B ) , despite the strong global translation inhibition observed in these cells ( Fig . 3 ) . GADD34 inactivation appeared to influence the accumulation of PKR , since the cytoplasmic dsRNA sensor levels were not up-regulated and even decreased in poly I:C-treated GADD34ΔC/ΔC MEFs ( Fig . 4B ) . Control treatment with tunicamycin and thapsigargin did not alter significantly PKR levels ( Fig . 4B ) , suggesting that ER stress did not influence the kinase expression . The absence of PKR up-regulation in the poly I:C-treated GADD34ΔC/ΔC MEFs led us to investigate the capacity of these cells to produce anti-viral and inflammatory cytokines , which normally drive PKR expression through an autocrine loop . We ruled out any interference from the UPR in triggering IFN-ß production in our experimental system , since , as anticipated from PKR expression , tunicamycin and thapsigargin treatments were not sufficient to promote cytokine production in MEFs ( Fig . S6 ) [43] , [44] . We therefore investigated IFN-ß and IL-6 production in response to dsRNA in WT , GADD34ΔC/ΔC and CReP−/− MEFs . CReP−/− MEFs were used as a control , since CReP ( Ppp1r15b ) is a non-inducible co-factor of PP1 and displays some functional redundancy with GADD34 [57] . Although basal levels of eIF2α phosphorylation were higher in CReP−/− , PKR expression and translation inhibition upon poly I:C delivery were equivalent in WT and CReP−/− MEFs ( Fig . S7A and S7B ) . Quantification of IFN-ß and IL-6 levels in culture supernatants indicated that , although abundant and comparable amounts of these cytokines were secreted by WT and CReP−/− cells , they were both absent in poly I:C-treated GADD34ΔC/ΔC MEFS ( Fig . 4C and S7C ) . Quantitative PCR analysis revealed that , IFN-ß , IL-6 and PKR transcripts were potently induced in poly I:C treated GADD34ΔC/ΔC MEFs ( Fig . 4D ) , thus excluding any major transcriptional alterations in these cells , as confirmed by the normal levels of cystatin C mRNA , which remained constant in all conditions studied . Moreover , using confocal immunofluorescence microscopy , we could not detect intracellular IFN-β in poly I:C-stimulated GADD34ΔC/ΔC MEFs , in contrast to WT cells , which abundantly expressed the cytokine , despite the global translation arrest ( Fig . S8 ) . Thus , we could attribute the deficit in cytokine secretion of the GADD34ΔC/ΔC MEFs to a profound inability of these cells to synthesize cytokines , rather than to a defect in transcription or general protein secretion . GADD34 induction by poly I:C is therefore absolutely necessary to maintain the synthesis of specific cytokines and probably several other proteins in an otherwise translationally repressed context . Importantly , GADD34 exerts its rescuing activity only on a selected group of mRNAs including those coding for IFN-ß and IL-6 , but not on all ER-translocated proteins , since cystatin C synthesis was strongly inhibited by poly I:C in all conditions tested . Interestingly , in GADD34ΔC/ΔC MEFs , PKR mRNA strongly accumulated in response to poly I:C ( Fig . 4D ) , despite the absence of detectable IFN-ß production and PKR protein increase ( Fig . 4B ) . This continuous accumulation of PKR mRNA in response to poly I:C suggests the existence of alternative molecular mechanisms , capable of promoting PKR mRNA transcription and stabilization independently of autocrine IFN-β detection . Nevertheless in these conditions PKR expression , like IFN-β , was found to be dependent on the presence of GADD34 for its synthesis ( Fig . 4B ) . Recent results indicate that PKR participates to the production of IFN-α/ß proteins in response to a subset of RNA viruses including encephalomyocarditis , Theiler's murine encephalomyelitis , and Semliki Forest virus [31] . Even though IFN-α/ß mRNA induction is normal in PKR-deficient cells , a high proportion of mRNA transcripts lack their poly ( A ) tail [31] . As GADD34 induction by poly I:C was completely PKR-dependent , we wondered whether the phenotypes observed in PKR−/−cells and GADD34ΔC/ΔC MEFs could be related . Oligo-dT purified mRNA extracted from cells exposed to poly I:C were therefore analyzed by qPCR . PolyA+ mRNAs coding for IFN-ß and IL-6 were equivalently purified and amplified from WT and GADD34ΔC/ΔC MEFs ( Fig . S9 ) . This confirms that albeit the phenotypes of PKR−/− and GADD34ΔC/ΔC cells might be linked , mRNA instability is not the primary cause of the cytokine production defect observed in GADD34ΔC/ΔC . Taken together these observations suggest the existence of a specific mRNAs pool , encompassing cardinal immune effectors such as IFN-ß , IL-6 , and PKR , which are specifically translated in response to dsRNA sensing and increased levels of P-eIF2α . This mRNAs pool requires GADD34 for their translation during the global protein synthesis shut-down triggered by dsRNA detection . We verified that GADD34 inactivation , and no other deficiency , was truly responsible for the loss of cytokine production by complementing GADD34ΔC/ΔC MEFs with GADD34 cDNA prior poly I:C delivery . IFN-ß secretion was partially restored in transfected GADD34ΔC/ΔC cells while eIF2α was efficiently dephosphorylated in both WT and GADD34ΔC/ΔC transfected MEFs ( Fig . 4E ) . To further demonstrate that the phosphatase activity of GADD34 controls cytokine production upon dsRNA detection , we treated WT MEFs with guanabenz , a small molecule , which selectively impairs GADD34-dependent eIF2α dephosphorylation [58] . Upon treatment with this compound , a dose dependent inhibition of IFN-ß secretion was observed in poly I:C-treated MEFs , confirming the importance of GADD34 in this process ( Fig . S10 ) . Fibroblasts of both human and mouse origin constitute a major target cell of Chikungunya virus ( CHIKV ) during the acute phase of infection [59] . In adult mice with a totally abrogated type-I IFN signaling , CHIKV-associated disease is particularly severe and correlates with higher viral loads . Importantly , mice with one copy of the IFN-α/ß receptor ( IFNAR ) gene develop a mild disease , strengthening the implication of type-I IFN signaling in the control of CHIKV replication [59] . Recently , human fibroblasts infection by CHIKV was shown to induce IFN-α/ß mRNA transcription , while preventing mRNA translation and secretion of these antiviral cytokines . CHIKV was found to trigger eIF2α phosphorylation through PKR activation , however this response is not required for the block of host protein synthesis [15] . We tested the importance of PKR during CHIKV infection by infecting WT and PKR−/− MEFs with CHIKV-GFP , at a multiplicity of infection ( MOI ) of 10 and 50 . Productive infection was estimated by GFP expression ( Fig . 5A , left panel ) , while culture supernatants were monitored for the presence of IFN-β ( 5A , right panel ) . PKR was found to be necessary to control CHIKV infection in vitro , since at least 60% of PKR–inactivated cells were infected after 24 of viral exposure , compared to only 15% in the control fibroblasts population . WT MEFs produced efficiently IFN-β , while the hypersensitivity to infection of the PKR−/− MEFs was correlated to a reduced type-I IFN production capacity after infection . Thus , during CHIKV infection , PKR is required for normal IFN production by MEFs . We also monitored protein synthesis in infected WT and PKR−/− fibroblasts using puromycin labeling followed by immunofluorescence confocal microscopy ( Fig . 5B ) . CHIKV-GFP positive PKR−/− MEFs were found to incorporate efficiently puromycin , while in their infected WT counterpart protein synthesis was efficiently inhibited . Thus CHIKV , in this experimental model , induces a PKR-dependent protein synthesis inhibition and is therefore particularly relevant to further confirm our observations on the role of GADD34 in controlling type-I IFN production during response to viral RNAs . GADD34ΔC/ΔC MEFs were exposed to CHIKV-GFP ( MOI of 10 or 50 ) for 24 and 48 h . Productive infection was estimated by GFP expression and virus titration ( Fig . 6A ) , and culture supernatants monitored for the presence of type-I IFN ( Fig . 6B , left ) . Only minimal CHIKV infection ( 15% ) could be observed at maximum MOI in WT MEFs ( Fig . 6A , left ) , while robust IFN- β amounts were already produced at the lowest MOI ( Fig . 6B ) . Contrasting with WT cells and regardless of the MOI used , a higher level of viral replication was observed in GADD34ΔC/ΔC MEFs ( Fig . 6A ) . The GADD34-inactivated cells were clearly more sensitive to CHIKV , displaying a 50% infection rate after 24 h of infection ( MOI 50 ) and a log more of virus titer in culture supernatants ( Fig . 6A , right ) . Correlated with their susceptibility to CHIKV infection , IFN-β production was nearly undetectable in GADD34ΔC/ΔC MEFs ( Fig . 6B ) . Such observation confirms the incapacity of GADD34-deficient cells to produce cytokines in response to cytosolic dsRNA , a deficiency likely to facilitate viral replication . This interpretation is further supported by the abrogation of viral replication in both WT and GADD34ΔC/ΔC MEFs briefly treated with IFN-β ( Fig . 6C ) . Thus , GADD34 inactivation does not favor viral replication per se , but is critical for type-I IFN production . Interestingly infection levels were found to be higher in PKR−/− than in GADD34 ΔC/ΔC MEFs , although this difference could be attributed to clonal MEFs variation , it more likely suggests that PKR-dependent translation arrest could be key in preventing early viral replication in this system . In addition , the relatively lower permissivity of GADD34ΔC/ΔC MEFs to infection at high MOI could indicate the existence of GADD34-dependent defense mechanisms , which could be independent from IFN production and eIF2-α dephosphorylation . To strengthen and generalize these observations , we treated a different strain of WT MEFs with guanabenz and examined the consequences for CHIKV infection . Biochemically , GADD34 expression was induced upon CHIKV infection , and guanabenz treatment resulted in a clear increase in eIF2α phosphorylation , demonstrating the importance of GADD34 in limiting this process during infection ( Fig . 6D , right ) . As observed with GADD34ΔC/ΔC cells , pharmacological and RNAi inhibition of GADD34 was found to increase significantly the sensitivity of MEFs to infection , while reducing their IFN-β production ( Fig . 6D and S10 ) . Thus , induction of GADD34 and its phosphatase activity during CHIKV infection , in vitro , participates to normal type-I IFN production and control of viral dissemination . Several components of the innate immune response have been shown to impact on the resistance of adult mice and to restrict efficiently CHIKV infection and its consequences in vivo [10] . We decided to investigate the importance of GADD34 upon intradermal injections of CHIKV to WT ( FVB ) and GADD34ΔC/ΔC mice . Neither strain of adult mice was affected by intradermal injections of CHIKV , with little statistically significant differences in the virus titers found in the different organs . Thus , GADD34 deficiency does not annihilate all the sources of type-I IFN in the infected adult animals , a situation exemplified by the capacity of GADD34ΔC/ΔC bone-marrow derived dendritic cells to produce reduced , but measurable IFN-β in response to poly I:C [60] . This also infers that the light impact of GADD34 inactivation on mouse development [61] does not render these animals more sensitive to CHIKV infection . As in Humans , CHIKV pathogenicity is strongly age-dependent in mice , and in less than 12 day-old mouse neonates , CHIKV induces a severe disease accompanied with a high mortality rate [59] . GADD34 function was therefore evaluated in this more sensitive context by injecting intradermally CHIKV to FVB ( WT ) and GADD34ΔC/ΔC neonatal mice . As previously observed for C57/BL6 mice [59] , when CHIKV was inoculated to FVB neonates , a rate of 50% of mortality was observed 3 days after the infection of 9-day-old mice , while 12-day-old pups were found essentially resistant to the virus lethal effect ( Fig . 7A ) . Strongly contrasting with these results , all CHIKV infected GADD34ΔC/ΔC neonates died within 3–5 days post inoculation whatever their age ( Fig . 7A ) . When infection was monitored 5 days post-inoculation of 12-day-old mice at , GADD34ΔC/ΔC pups displayed considerably more elevated CHIKV titers ( 10–100 folds ) in most organs tested , including liver , muscle , spleen and joints , the later being primarily targeted by the virus ( Fig . 7B , left ) . As expected , and in full agreement with the in vitro data , infected GADD34ΔC/ΔC tissues showed a considerably reduced IFN-ß production ( 40–50% ) compared to control tissues ( Figure 7B , right ) , while serum levels were reduced by 20% ( not shown ) . Although Infectious virus was poorly detected in the heart of WT animals , elevated titers of virus were observed in the heart of GADD34-deficient pups , matching the limited production of IFN in this organ . We further investigated the possible pathological consequences of cardiac tissue infection by carrying-out comparative histopathology . Hearts of infected GADD34-deficient animals displayed severe cardiomyocytes necrosis with inflammatory infiltrates by monocytes/macrophages and very important calcium deposition ( Fig . 8 ) , all being indicative signs of grave necrotic myocarditis . As a consequence , the left ventricles were strongly dilated , being probably the cause of acute cardiac failures and of the important death rate observed in GADD34ΔC/ΔC infected pups . Histology of infected FVB mice hearts was , however , normal with only few inflammatory cells ( mainly lymphocytes ) observed in the close vicinity of capillaries . GADD34 expression is therefore necessary to allow normal type-I interferon production during viral infection and to promote the survival of young infected animals . We could circumvent the age-related acquisition of viral resistance in GADD34ΔC/ΔC mice to 17 days , since mice inoculated at that age survived CHIKV inoculation . In these animals , 3 days post-infection , enhanced viral replication was observed in the spleen and muscles , matching the relatively low level of type-IFN production in these tissues ( Figure 7C ) . Functional GADD34 is therefore required to mount a normal innate response against the virus , but in older mice type-I IFN production by non-infected innate cells is probably capable to gradually overcome GADD34-deficiency and limit viral proliferation in vital organs , such as the heart .
Translation inhibition occurs in response to stress , when other cellular activities have to be reassigned or suspended momentarily . We demonstrate here that the activation of PKR by cytosolic dsRNA results in a stress response , leading to ATF4 and GADD34 induction . GADD34 expression has been observed during the infection of cells by different types of viruses [62] or intracellular bacteria such as Listeria monocytogenes [63] . Our observations demonstrate that GADD34 expression is a direct consequence of PKR activation and dsRNA sensing . Interestingly , although GADD34 induction by poly I:C promotes eIF2α dephosphorylation , this is not sufficient to prevent global protein synthesis arrest . The uncoupling of efficient eIF2α dephosphorylation from global translation recovery in response to cytosolic poly I:C implies therefore the existence of additional mechanisms inhibiting global translation . The 2-5A/RNAse L pathway does not seem to be sufficiently active in our experimental setting to explain this prolonged protein synthesis inhibition . The cleavage or the inactivation of other translation factors could work in concert with eIF2α to block or affect the efficiency of other individual steps of mRNA translation [64] . For instance , the phosphorylation of translation elongation factor 2 ( eEF-2 ) is also controlled by eIF2α phosphorylation . Thus , Thr56 phosphorylation of eEF-2 , which is known to inhibit its translational function by reducing its affinity for ribosomes , could contribute directly to the protein synthesis inhibition induced by PKR activation [65] . Independently of general protein synthesis inhibition , eIF2α dephosphorylation is necessary for the production of specific proteins upon dsRNA-induced translation inhibition . As demonstrated for ATF4 , translation of a given mRNA during stress could rely on the structure and organization of its coding sequence , as well as the presence of multiple alternative initiation codons [49] . Surprisingly , functional GADD34 expression was found necessary for the translation of IL-6 , IFN-β , and PKR . This observation points to the existence of a distinct group of mRNAs efficiently translated upon dsRNA detection and dependent on GADD34 activity . GADD34 is extremely short lived and has been shown to accumulate on the ER , when over-expressed [51] . GADD34 could mediate its activity at the ER level and influence differently eIF2α sub-cellular distribution according to the type , localization , and level of activity displayed by the different eIF2α kinases . The strong eIF2α phosphorylation mediated by PKR in response to poly I:C or viral infection and leading to the initiation of translation inhibition , could be circumvented through GADD34 activity solely at the ER level , thereby allowing local cytokine production in absence of other functional protein synthesis . This selectivity for translation of several specific mRNAs among other ER-secreted molecules suggests further that GADD34 dependent mRNAs might display specific features allowing their efficient identification by GADD34 and associated molecules , as well as allowing their translation in presence of minimal levels of active guanine nucleotide exchange factor eIF2B . GADD34 and PKR are necessary to produce anti-viral cytokines during CHIKV infection , and probably other types of infection . PKR , ATF4 and GADD34 should therefore be considered as an essential module of the innate anti-viral response machinery . The importance of PKR in anti-viral type-I IFN responses has been the object of contradictory reports [30] , [31] , [66] , [67] . Our observations , however , suggest that PKR function should be re-evaluated by integrating the impact of viral detection on cellular translation . In eIF2A/A and PKR−/− cells , cytokine transcription is induced normally following poly I:C detection by DExD/H box RNA helicases , while as expected in these cells , no eIF2α phosphorylation and subsequent host translation inhibition are observed . This lack of translation arrest in the absence of potent eIF2α phosphorylation allows for normal cytokine production during dsRNA detection , with no requirement for an operational GADD34 feedback loop . The importance of PKR and GADD34 for IFN-β and other cytokines production could therefore be directly linked to the efficiency of the cellular translation inhibition induced by RNA viruses , as exemplified here with CHIKV , which in MEFs strongly activates PKR and subsequent protein synthesis inhibition . GADD34ΔC/ΔC neonates are extremely sensitive to CHIKV infection and display signs of acute myocarditis and ventricles dilatation probably causing recurrent cardiac failures . CHIKV cardiac tropism is not normally observed in WT mouse and inability of heart tissues to produce sufficient type-I IFN in GADD34ΔC/ΔC could allow abnormally high viral replication , myocarditis and dilated cardiomyopathy . Interestingly many cases of myopericarditis induced by CHIKV and leading to dilated cardiomyopathies in infected patients have been reported since the 1970s after the different western Indian Ocean islands and Indian subcontinent disease outbreaks [68] , [69] . These particular symptoms and complications might therefore be the consequences of great variation in the tissue-specific type-I IFN levels induced in CHIKV-infected patients , who might display particular polymorphisms in their innate viral sensing pathways increasing their peculiar susceptibility to viral dissemination in the heart . Importantly , our data reveal a link between pathogen-associated molecular patterns ( PAMPs ) and the UPR through the activation of the eIF2-α/ATF4 branch [70] . Similarly , several laboratories have reported that TLR stimulation activates the XBP-1 branch of the UPR and that XBP-1 production was needed to promote a sustained production of inflammatory mediators , including IL-6 [71] , [72] . Here , we identify GADD34 as a novel functional link between ISR and PAMPs detection in MEFs , required for the production of cytokines including type-I IFN . It will now be important to explore the therapeutic potential of targeting GADD34 to reduce cytokines overproduction during inflammatory conditions .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals the French Ministry of Agriculture and of the European Union . The protocol was approved by the Committee on the Ethics of Animal Experiments of the Institut Pasteur and Région PACA ( Autorisation # 13 . 116 issued by DDSV/Préfecture des Bouches du Rhône , Marseille , France ) and were performed in compliance with the NIH Animal Welfare Insurance #A5476-01 issued on 02/07/2007 . All experiments were performed under isoflurane anesthesia ( Forene , Abbott Laboratories Ltd , United-Kingdom ) , and all efforts were made to minimize suffering . Animals were housed in the Institut Pasteur and CIML animal facilities accredited by the French Ministry of Agriculture to perform experiments on live mice . Matched wild-type ( 129 SvEv ) , and PKR−/− MEFs ( Yang et al . , 1995 ) were a gift from Caetano Reis e Sousa ( Cancer Research UK , London ) ; primary eIF2α S/S and eIF2α A/A MEFs were a gift from Randal J . Kaufman ( Department of Biological Chemistry , University of Michigan Medical Center , USA ) ; Matched wild-type ( 129 SvEv ) , ATF4−/− , GADD34ΔC/ΔC and CReP−/− MEFs were a gift from David Ron ( Skirball Institute of Biomolecular Medicine , New York ) . All MEFs were cultured in DMEM , 10% FCS ( HyClone , Perbio ) , 100 units/ml penicillin , 100 µg/ml streptomycin , 2 mM glutamine , 1× MEM non-essential amino acids and 50 µM 2-mercaptoethanol . NIH3T3 cells were cultured in RPMI 1640 ( Gibco ) supplemented with 10% FCS ( HyClone , PERBIO ) , 100 units/ml penicillin and 100 µg/ml streptomycin . All cells were cultured at 37°C and 5% CO2 . MEFs and NIH3T3 were treated for the indicated time with 10 µg/ml poly I:C ( InvivoGen ) in combination with lipofectamine 2000 ( Invitrogen ) . Thapsigargin , tunicamycin , sodium arsenite , and guanabenz ( all from SIGMA ) were used at 200 nM , 2 µg/ml , 0 . 5 mM , and 10 µM respectively . The plasmid GADD34 ( FLAG epitope tagged at N-terminus , CMV2-based mammalian expression ) was a kind gift from David Ron ( Institute of Metabolic Sciences , University of Cambridge , UK ) . Puromycin labelling for measuring the intensity of translation was performed as previously described [47] . For immunoblots , 10 µg/ml puromycin ( Sigma , min 98% TLC , cell culture tested , P8833 , diluted in PBS ) was added in the culture medium and the cells were incubated for 10 min at 37°C and 5% CO2 . Where indicated , 25 µM cycloheximide ( Sigma ) was added 5 min before puromycin . Cells were then harvested , centrifuged at 4°C and washed with cold PBS prior to cell lysis and immunoblotting with the 12D10 antibody . Cells were lysed in 1% Triton X-100 , 50 mM Hepes , 10 mM NaCl , 2 . 5 mM MgCl2 , 2 mM EDTA , 10% glycerol , supplemented with Complete Mini Protease Inhibitor Cocktail Tablets ( Roche ) . Protein quantification was performed using the BCA Protein Assay ( Pierce ) . 25–50 µg of Triton X-100-soluble material was loaded on 2%–12% gradient or 8% SDS-PAGE before immunoblotting and chemiluminescence detection ( SuperSignal West Pico Chemiluminescent Substrate , Pierce ) . Nuclear extraction was performed using the Nuclear Complex Co-IP kit ( Active Motif ) . Rabbit polyclonal antibodies recognizing ATF4 ( CREB-2 , C-20 ) , GADD34 ( C-19 ) , Lamin A ( H-102 ) and eIF2-α ( FL-315 ) were from Santa Cruz Biotechnology , as well as mouse monoclonal anti-PKR ( B-10 ) . GADD34/PPP1R15A ( Catalog No . 10449-1-AP ) rabbit polyclonal antibody was purchased from PROTEINTECH . Rabbit polyclonal anti-eIF2α[pS52] and Cystatin C were from Invitrogen and Upstate Biotechnology , respectively . Mouse monoclonal antibodies for β-actin and HDAC1 ( 10E2 ) were purchased from Sigma and Cell Signaling Technologies . Secondary antibodies were from Jackson ImmunoResearch Laboratories . MEFs and NIH3T3 were grown on coverslips overnight and stimulated for the indicated time with poly I:C complexed with Lipofectamine 2000 . Cells were fixed with 3% paraformaldehyde in PBS for 10 min at room temperature , permeabilized with 0 , 5% saponin in 5% FCS PBS with 100 mM glycine , for 15 min at room temperature and stained for 1 h with indicated primary antibodies . Anti-P-eIF2α was from BioSource; anti-dsRNA ( clone K1 ) from English & Scientific Consulting Bt . ; anti-IFN-β-FITC-conjugated from PBL Interferon Source; anti-puromycin ( clone 2G11 , mouse IgG1 ) has been previously described [47] . Alexa-conjugated secondary antibodies ( 30 min staining ) were from Molecular Probes ( Invitrogen ) . Coverslips were mounted on a slide and images taken with a laser-scanning confocal microscope ( LSM 510; Carl Zeiss MicroImaging ) using a 63× objective and accompanying imaging software . When PKR WT and PKR−/− were infected with CHIKV , protocol was performed as follows: cells were fixed with 4% paraformaldehyde in PBS for 20 min , then permeabilized for 30 min in 0 . 1% Triton 100X ( Sigma ) and blocked in 10% of normal goat serum ( Vector Laboratories ) . Cells were stained with a mouse monoclonal antibody directed against CHIKV capsid coupled to Alexa-488 and a mouse antibody against puromycin coupled to Alexa-555 and a rabbit antibody anti-eIF2α[pS52] ( Invitrogen ) and a Cyanin-3 secondary antibody , and finally counterstained with Hoechst ( Vector Lab ) . Cells were observed with an AxioObserver microscope ( Zeiss ) . Pictures and Z-stacks were obtained using the AxioVision 4 . 5 software . IFN-β and IL-6 quantification in culture supernatant was performed using the Mouse Interferon Beta ELISA kit ( PBL InterferonSource ) and Mouse Interleukin-6 ELISA kit ( eBioscience ) respectively , according to manufacturer instructions . Total RNA was isolated from cells using the RNeasy miniprep kit ( QIAGEN ) combined with a DNA digestion step ( RNase-free DNase set , QIAGEN ) . cDNA was synthesized using the Superscript II reverse transcriptase ( Invitrogen ) and random hexamer primers . Quantitative PCR amplification was carried out using complete SYBR Green PCR master mix ( Applied Biosystems ) and 200 nM of each specific primer . 5 µl of cDNA template was added to 20 µl of PCR mix , and the amplification was tracked via SYBR Green incorporation by an Applied Biosystems thermal cycler . cDNA concentration in each sample were normalized by using HPRT . A nontemplate control was also routinely performed . The primers used for gene amplification ( designed with Primer3 software ) were the following: GADD34 ( S 5′-GACCCCTCCAACTCTCCTTC-3′ , AS 5′-CTTCCTCAGCCTCAGCATTC-3′ ) ; HPRT ( S 5′-AGGCCAGACTTTGTTGGATTT -3′ , AS 5′-GGCTTTGTATTTGGCTTTTCC -3′ ) ; IFN-β ( S 5′-CCCTATGGAGATGACGGAGA-3′ , AS 5′-ACCCAGTGCTGGAGAAATTG-3′ ) ; IL-6 ( S 5′-CATGTTCTCTGGGAAATCGTG-3′ , AS 5′-TCCAGTTTGGTAGCATCCATC-3′ ) ; PKR ( S 5′-CCGGTGCCTCTTTATTCAAA -3′ , AS 5′-ACTCCGGTCACGATTTGTTC-3′ ) ; Cystatin C ( S 5′-GAGTACAACAAGGGCAGCAAC-3′ , AS 5′-TCAAATTTGTCTGGGACTTGG-3′ ) . ATF4 ( 5′-GGACAGATTGGATGTTGGAGA-3′ , AS 5′-AGAGGGGCAAAAAGATCACAT3-′ ) . mRNA isolation from total RNA was performed with oligodT columns ( Genelute mRNA miniprep kit ( Sigma ) . Data were analyzed using the 7500 Fast System Appled Biosystems software . RNA integrity upon poly I:C stimulation was measured by capillary electrophoresis using the the Agilent RNA 6000 Pico Chip kit ( Agilent Technologies ) in an Agilent 2100 Bioanalyser , according to manufacturer instructions . GADD34ΔC/ΔC and the corresponding WT control MEFs were infected at a multiplicity of infection ( MOI ) of 10 or 50 with CHIKV-GFP generated using a full-length infectious cDNA clone provided by S . Higgs [71] . By 24 h and 48 h post infection , 30 000 cells were analyzed in triplicate by FACS for expression of GFP . At the same time-points , culture supernatants were collected and IFN-β protein assessed by ELISA . In experiments with exogenous IFN-β , cells were treated with mouse IFN-β ( PBL InterferonSource ) for 3 h before infection with CHIKV-GFP . When guanabenz was used to specifically inhibit GADD34 , MEFs cells were treated for 2 h with 10 µM of Guanabenz or DMSO and then infected in the same medium . Three hours post infection the inoculum was removed and fresh medium with Guanabenz or DMSO was added and maintained all along the experiment . RNAi for GADD34 was performed as described in [60] . FVB WT mice were obtained from Charles River Laboratories ( France ) . GADD34ΔC/ΔC FVB mice were obtained from L . Wrabetz ( Milan ) . Mice were anesthetized and inoculated via the intradermal route with 106 PFU of CHIKV-21 isolate [72] . Viral titers in tissues and serum were determined as described before [59] , and expressed as tissue cytopathic infectious dose 50 ( TCID50 ) /g or TCID50/ml , respectively . Organs including heart , liver , skeletal muscles and spleen were collected for histopathological procedures . organs were then fixed in 4% paraformaldehyde solution , paraffin-embedded , sectioned coronally in 5–10 µm thickness and stained with hematoxylin-eosin .
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Nucleic acids detection by multiple molecular sensors results in type-I interferon production , which protects cells and tissues from viral infections . At the intracellular level , the detection of double-stranded RNA by one of these sensors , the dsRNA-dependent protein kinase also leads to the profound inhibition of protein synthesis . We describe here that the inducible phosphatase 1 co-factor Ppp1r15a/GADD34 , a well known player in the endoplasmic reticulum unfolded protein response ( UPR ) , is activated during double-stranded RNA detection and is absolutely necessary to allow cytokine production in cells exposed to poly I:C or Chikungunya virus . Our data shows that the cellular response to nucleic acids can reveal unanticipated connections between innate immunity and fundamental stress pathways , such as the ATF4 branch of the UPR .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology"
] |
2012
|
Induction of GADD34 Is Necessary for dsRNA-Dependent Interferon-β Production and Participates in the Control of Chikungunya Virus Infection
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Chagas disease is a neglected disease caused by the intracellular parasite Trypanosoma cruzi . Around 30% of the infected patients develop chronic cardiomyopathy or megasyndromes , which are high-cost morbid conditions . Immune response against myocardial self-antigens and exacerbated Th1 cytokine production has been associated with the pathogenesis of the disease . As IL-17 is involved in the pathogenesis of several autoimmune , inflammatory and infectious diseases , we investigated its role during the infection with T . cruzi . First , we detected significant amounts of CD4 , CD8 and NK cells producing IL-17 after incubating live parasites with spleen cells from normal BALB/c mice . IL-17 is also produced in vivo by CD4+ , CD8+ and NK cells from BALB/c mice on the early acute phase of infection . Treatment of infected mice with anti-mouse IL-17 mAb resulted in increased myocarditis , premature mortality , and decreased parasite load in the heart . IL-17 neutralization resulted in increased production of IL-12 , IFN-γ and TNF-α and enhanced specific type 1 chemokine and chemokine receptors expression . Moreover , the results showed that IL-17 regulates T-bet , RORγt and STAT-3 expression in the heart , showing that IL-17 controls the differentiation of Th1 cells in infected mice . These results show that IL-17 controls the resistance to T . cruzi infection in mice regulating the Th1 cells differentiation , cytokine and chemokine production and control parasite-induced myocarditis , regulating the influx of inflammatory cells to the heart tissue . Correlations between the levels of IL-17 , the extent of myocardial destruction , and the evolution of cardiac disease could identify a clinical marker of disease progression and may help in the design of alternative therapies for the control of chronic morbidity of chagasic patients .
Trypanosoma cruzi is an intracellular protozoan parasite that causes Chagas' disease , the major cause of infectious heart disease in Latin America . It is estimated that 13 million people are infected with T . cruzi in the Central and South America , and 75 million are at potential risk of infection ( WHO , 2005 ) . In non-endemic countries , blood transfusions , organ transplantations , and mother-to-child infection represent real risks for disease transmission , due to high numbers of immigrants and the autochthonous transmission of T . cruzi in the USA has been reported [1] . During chronic phase , around 10% and 20% of infected patients develop digestive ( megaesophagus and megacolon ) and cardiac ( cardiomegaly ) form of Chagas disease , respectively . The myocarditis that occurs as a result of infection is thought to be due to parasites in the lesions , although immune-mediated mechanisms also appear to be involved in heart pathology [2] . Of note , the immune hyperactivity that is deleterious to the host is governed by the imbalanced production of cytokines in response to the parasite [3] . The pro-inflammatory cytokines IL-12 , IFN-γ , and TNF-α act in concert to activate macrophages to kill the parasites through the production of nitric oxide and nitrogen free radicals [4] . In addition , these cytokines also stimulate the differentiation and proliferation of Th1-biased CD4+ T cells , which orchestrate a CD8+ T-cell response that causes tissue destruction and fibrosis [5] . As expected , the inflammatory response is down-regulated by the anti-inflammatory cytokines IL-10 and TGF-β [6] , [7] , regulatory T cells [8]–[10] , and CTLA-4+ cells [11] , [12] . Lymphocytes of patients with chronic chagasic cardiopathy ( CCC ) produce higher amounts of IFN-γ , TNF-α , and IL-6 , but little or no IL-4 or IL-10 compared to asymptomatic individuals [3] , [13] . For years , the balance of immune inflammation was explained by the dichotomy of cytokines produced . However , the Th1-Th2 paradigm has been reconsidered following the discovery of a novel lineage of effector CD4+ T helper lymphocytes , called Th17 cells , which produce interleukin 17 ( IL-17 ) -A and F , IL-21 , IL-22 , and TNF-α [14] . Th17 differentiation is thought to be mediated by the combined effects of the transcription factors RORγt and RORα , which are dependent on STAT-3 , and requires IL-1β , IL-6 , IL-21 , TGF-β , and the expression of the CCR6 chemokine receptor [15] , [16] . In addition to Th17 cells , other cells produce IL-17 , including CD8+ T cells , γδ T cells , neutrophils , monocytes , and NK cells [17] . IL-17 has pro-inflammatory properties and induces fibroblasts , endothelial cells , macrophages , and epithelial cells to produce several inflammatory mediators , such as GM-CSF , IL-1 , IL-6 , TNF-α , inducible nitric oxide synthase ( iNOS ) activation , metalloproteinases , and chemokines ( CXCL1 , CXCL2 , CXCL8 , CXCL10 ) , leading to the recruitment of neutrophils and inflammation [18]–[20] . The Th17 response has been linked to the pathogenesis of several inflammatory and autoimmune diseases , such as multiple sclerosis , psoriasis , rheumatoid arthritis , colitis , autoimmune encephalitis [21] , schistosomiasis [22] , and toxoplasmosis . Infection with T . cruzi also leads to the production of several chemokines and cytokines [23] as well as iNOS [4] and metalloproteinase activation [24] . Also , lymphocytes from infected mice and chagasic patients recognize self-epitopes [2] , suggesting a possible autoimmune response . Altogether , these observations suggest the possible involvement of IL-17 in the pathogenesis of T . cruzi infection . Therefore , we investigated IL-17 production in T . cruzi-infected mice and its role in the modulation of the immune response . Our results show that IL-17 is produced during the acute phase of T . cruzi infection and controls cardiac inflammation by modulating the Th1 response .
BALB/c female mice , 6 weeks old , were cared for according to institutional ethical guidelines and the Ethics Committee in Animal Research of the FMRP-USP approved all experimental protocols . Mice were infected via the i . p . route with 100 blood trypomastigotes of T . cruzi , Y strain . For in vitro experiments , the trypomastigotes were grown and purified from a fibroblast cell line ( LLC-MK2 ) . Balb/c mice were treated one day before inoculation , and on days four and eight with i . p . injections of 100 µg of normal rat IgG or rat anti-mouse IL-17 ( IgG2a , clone M210 , Amgen , Seattle , WA ) . Parasitemia was measured in 5 µL of blood obtained from the tail vein , and mortality was evaluated with five mice per treatment group . Sera , heart tissue , livers , and spleens were collected 14 days p . i from five infected and treated mice . To determine if the parasite induces IL-17 production , naive splenocytes ( 5×106 cells/ml ) from Balb/c mice were cultured for 48 h with trypomastigotes ( 2 . 5×107/ml ) in 48-well plates ( final volume of 0 . 5 ml ) and intracellular IL-17A in CD4+ , CD8+ , and NK cells was determined . To shed light on the mechanism that controls IL-17 production , splenocytes ( 1×106 cells/ml ) from normal or T . cruzi-infected Balb/c mice ( 14 days p . i . ) were cultured with Con-A ( 5 µg/ml ) ( Sigma-Aldrich , St . Louis ) and T . cruzi antigen ( 10ηg/well ) , with or without antibodies against IL-17 or IFN-γ ( IgG1 , clone R46A2 ) ( 10 µg/well ) , and cytokine production was determined . Cytokine production was assayed in sera , tissue heart , liver , and spleen . Tissue fragments were added to vials containing PBS ( 50 mg/ml ) with a protease inhibitor cocktail ( Complete , Roche ) . The tissue fragments were macerated , centrifuged , and the supernatants collected for cytokine quantification . The ELISA sets were IL-1β , IL-4 , IL-6 , IL-10 , IL-12 , IL-17 , IL-25 , IFN-γ , TNF-α , and TGF-β ( R&D , Minneapolis , MN ) , and procedures were performed according to the manufacturers' instructions . Optical densities were measured at 450 ηm . Results are expressed as picograms per milliliter . The limits of sensitivity for the different assays were as follows: IL-4 , IL-17 , IL-10 , TGF-β and TNF-α: 15 pg/ml; IL-12: 10 pg/ml; IFN-γ: 50 pg/ml , IL-1β: IL-25 and IL-6: 20 pg/ml . To isolate mononuclear cells from myocardial tissues , the hearts from five mice were removed at 14 days post infection ( p . i . ) , washed ( to remove blood clots ) , pooled , minced with scissors into small fragments , extensively washed , and subjected to enzymatic digestion with a 500 mg/ml liberase solution ( Roche Applied Science , Indianapolis , IN ) for 30 min at 37°C . The tissues were processed with RPMI 10% FCS and 0 , 05% DNAse ( Sigma-Aldrich ) using Medmachine ( BD ) for 4 min , the cell suspension was spun , the supernatant removed , and the pellet suspended in RPMI 10% FCS . Suspensions of total spleen cells were washed and the leukocyte purification from these samples and from cardiac cells was done in Ficoll Hypaque ( d = 1077 g/ml , Sigma - Aldrich ) gradient by centrifuging at 400×g by 30 min at room temperature . The leukocytes obtained were evaluated by flow cytometry . The expression of IL-17 and IFN-γ in leukocytes ( 1×106/well ) from heart and spleen were assayed after incubation with monensin ( 2 µg/ml ) for 6 h in RPMI 1640 supplemented with fetal bovine serum ( 10% ) . The cells were washed in cold PBS and samples of 5×105 cells/tube were incubated for 30 min at 4°C with 0 . 5 µg of anti-CD16/CD32 mAb ( FC block ) , followed by the addition of 0 . 5 µg of PERCP- or FITC-labeled antibodies against CD3 , CD4 , CD8 , or PanNK ( all from BD Pharmingen , San Diego , CA ) for an additional 30 minutes at 4°C in the dark . To detect intracellular IL-17 and IFN-γ , the cells were fixed with cytofix and cytoperm solution for 15 min at room temperature , washed , and then stained with FITC- or PE-labeled antibodies at 4°C in the dark and incubated overnight . Subsequently , the cells were washed twice and suspended in 200 µL of PBS/1% formaldehyde . For each assay 50 , 000 events were acquired and data acquisition was performed using a FACSorter . Multivariate data analysis was performed using FlowJo software . The data was exported from the histogram and were processed in Prism Software for statistical analysis and graphics . DNA from the hearts of mice at 14 days p . i . was purified using the SV Total DNA Isolation System kit ( Promega , Madison , WI ) according to the manufacturer's instructions . Real-time PCR was performed using the Platinum SYBR Green qPCR SuperMix UDG with ROX reagent ( Invitrogen , Carlsbad , CA ) with 100 ηg of total gDNA . The sequences of primers used were TCZ-F 5′-GCT CTT GCC CAC AMG GGT GC-3′ and TCZ-R 5′-CCAAGCAGCGGATAGTTCAGG-3′ . The samples were amplified in a thermal cycler ABI PRISM 7000 Sequence Detection System ( Applied Biosystems , Foster City , CA ) with the following PCR conditions: first step ( 2 min at 50°C ) , second step ( 10 min at 95°C ) and 40 cycles ( 30 s at 95°C , 30 s at 60°C , and 1 min at 72°C ) , followed by a dissociation stage . The results were based on a standard curve constructed with DNA from culture samples of T . cruzi trypomastigotes ( n = 3 ) . Total RNA from cardiac tissue was isolated using the TRIZOL reagent ( Invitrogen ) and SV Total RNA Isolation System ( Promega , Madison , WI ) according to the manufacturers' instructions . cDNA was synthesized using 1 µg of tRNA through a reverse transcription reaction ( ImProm-IITM Reverse Transcriptase , Promega ) . Real-time PCR quantitative mRNA analyses were performed in an ABI Prism 7000 SDS ( Applied Biosystems ) using the Platinum SYBR Green qPCR SuperMix UDG with ROX reagent ( Invitrogen ) for quantification of amplicons . The standard PCR conditions were as follows: 50°C ( 2 min ) , 95°C ( 10 min ) ; 40 cycles of 94°C ( 30 s ) , 58°C ( 30 s ) , and 72°C ( 1 min ) ; followed by a standard denaturation curve . The sequences of primers were designed using the Primer Express software package ( Applied Biosystems ) utilizing nucleotide sequences present in the GenBank database ( Table 1 ) . Platinum SYBR Green qPCR SuperMix UDG with ROX reagent ( Invitrogen ) , 1 µg/µl of each specific primer , and a 1:20 dilution of cDNA were used in each reaction . The mean Ct values from triplicate measurements were used to calculate expression of the target gene , with normalization to an internal control ( β-actin ) using the 2–ΔCt formula . The total number of nucleated cells was counted in fifty microscopic fields in at least four representative , nonconsecutive , HE-stained sections ( 5 µm thickness ) from each mouse . Sections were examined using a Zeiss Integrationsplatte II eyepiece ( Zeiss Co , Oberkochen , Germany ) reticule , using a microscope at a final magnification of 400X . Data are expressed as means ± SEM . Student's t test was used to analyze the statistical significance of the observed differences in infected vs . control assays . In time course studies , one-way ANOVA was used followed by Tukey-Kramer post-hoc analysis . The Kaplan-Meier method was used to compare survival curves of the studied groups . All analyses were performed using PRISM 3 . 0 software .
First , we evaluated IL-17 production by spleen cells from naive mice incubated with live trypomastigotes . We found that the percentage of IL-17+ splenocytes in cultures containing T . cruzi ( 13 . 86±4 . 59% ) was higher than in the controls ( 5 . 45±2 . 87% ) ( Figure 1A ) . The mean fluorescence intensities of intracellular IL-17 staining in the absence ( 6 . 02±1 . 15 ) and presence of parasites ( 11 . 29±1 . 54 ) indicated that culture with parasites increased the degree of IL-17 production . The major IL-17 expressing cells were CD4+ and CD8+ T lymphocytes as well as NK cells ( Figure 1B ) . IL-17+ cells were significantly increased in spleens of mice during the course of acute infection ( Figure 2A ) . The percentage of IL-17+ lymphocytes on day 14 p . i . was also significantly increased compared with uninfected control mice ( Figure 2B ) . On day 14 p . i . , the main IL-17+ lymphocytes were CD4+ ( 4 . 79±0 . 87 ) followed by CD8+ T cells ( 1 . 92±0 . 79 ) ( Figure 2C ) . These results clearly indicate that infection with T . cruzi leads to IL-17 production . Since IL-17 is produced during the acute phase of T . cruzi infection , we investigated its role in parasite control . We treated mice with anti-IL-17 neutralizing antibody and evaluated the course of infection and mortality . We did not find significant differences in the course of parasitemia of anti-IL-17 treated mice compared with the control group ( Figure 3A ) . However , there was a significant reduction in cardiac parasitism of anti-IL-17 treated mice ( day 14 p . i . ) compared with infected mice treated with normal rat IgG ( Figure 3B ) . Importantly , infected mice treated with anti-IL-17 exhibited significantly earlier mortality compared to controls . The anti-IL-17 treated mice survived only until day 18 p . i . , whereas the control group survived until 24 days p . i . ( Figure 3C ) . Inhibition of IL-17 also resulted in more inflammatory cells in the heart tissue of T . cruzi-infected mice on day 14 p . i . ( Figure 4A and B ) . The total number of nuclei per 50 µm section of heart tissue was higher in infected mice treated with anti-IL-17 ( 677 . 25±89 . 36 ) than in controls ( 439 . 75±54 . 87 ) ( Figure 4A ) . It is noteworthy that by microscopy analysis , the inflammatory infiltrate found in all infected mice was characterized by mononuclear cells , and scarce presence of polymorphonuclear cells , and no difference were detected in the cellular composition of the myocarditis between the groups at day 14 pi . We conclude that IL-17 plays a role in resistance to the infection , modulating the inflammatory reaction that occurs in infected mice . To investigate the mechanism by which IL-17 neutralization changes the course of infection , we first compared cytokine production in T . cruzi-mice treated or not with anti-IL-17 . As expected , we detected significant levels of IFN-γ , TNF-α , IL-10 , and IL-12 in the sera and tissue heart of T . cruzi-infected mice ( Figure 5 ) . Inhibition of IL-17 resulted in significantly higher levels of IFN-γ and IL-12 in the sera and heart tissue , and increased TNF-α level in the heart tissue of infected mice compared with mice treated with normal rat IgG . Treatment with anti-IL-17 did not , however , change the levels of IL-10 . IL-17 was not detected in the sera but , consistent with previous results , infection increased IL-17 levels in the heart tissue ( Figure 5 ) , where it was produced by CD4+ , CD8+ and NK cells ( Figure 6A ) . Again , treatment with anti-IL-17 decreased IL-17 levels ( Figure 5 ) and production by CD4+ and CD8+ cells in the heart ( Figure 6A ) , and increased IFN-γ production by CD4+ cells ( Figure 6B ) . IL-17 neutralization did not changed the frequency of CD4+ ( 29 . 32±2 . 14 vs 24 . 37±1 . 87% ) , CD8+ ( 52 . 01±5 . 38 vs 43 . 52±2 . 90% ) and NK ( 0 . 7±0 . 12 vs 1 . 01±0 . 19% ) cells in the heart . Low levels of IL-25 were detected in the heart tissue and spleens of normal mice , but not in infected mice . IL-1β , IL-4 , IL-6 , and TGF-β were detected in the sera , myocardium , spleens , and livers of T . cruzi-infected mice , but did not change with anti-IL-17 treatment ( data not shown ) . Next , we investigated the mechanisms that regulate IL-17 production and the means by which IL-17 modulates Th1 cytokine production . IL-17 was detected only when leukocytes from normal or infected mice were cultured with Con-A , and it was significantly increased when endogenous IFN-γ was inhibited ( Figure 7 ) . IL-12 , IFN-γ , and TNF-α production by leukocytes from acutely infected mice were also regulated by IL-17 , as addition of anti-IL-17 to the cultures significantly increased the production of these cytokines in the presence of parasite antigen . In the presence of mitogen , we found increased production of IL-12 and IFN-γ by the addition of anti-IL-17 ( Figure 7 ) . These results show that IL-17 produced during T . cruzi infection modulates IFN-γ , TNF-α , and IL-12 production . To understand how IL-17 regulates the pattern of cytokine production , we examined the expression of the transcription factors Foxp3 , T-bet , GATA-3 , and STAT-3/RORγt . T . cruzi infected mice showed enhanced of Foxp3 , T-bet , GATA-3 , STAT-3 , and RORγt mRNA expression in heart tissue compared to uninfected animals . IL-17 neutralization in T . cruzi-infected mice significantly increased T-bet but decreased STAT-3 and RORγt mRNA expression in the heart when compared to infected control animals ( Figure 8A ) . Concomitant to the enhanced expression of the Th1 transcription factor ( T-bet ) and reduction of Th17 transcription factors ( STAT-3 and RORγt ) , the cardiac tissue of IL-17 neutralized mice exhibited higher expression of CCR5 , and decreased expression of CCR3 and CCR4 chemokine receptors , both involved in Th2 and regulatory T cell migration [25] , [26] . The expression of CCR6 , a chemokine receptor expressed by Th17 cells , was markedly reduced in the heart tissue of T . cruzi infected mice treated with anti-IL-17 ( Figure 8B ) . The expression of CCL2 , CCL3 , CCL4 , CCL11 , and CXCL9 , which are involved in T-cell migration to the heart tissue of infected mice , but not CCL5 , CCL17 , CCL22 , or CXCL10 , was increased in the heart tissue of anti-IL-17 treated mice ( Figure 8C ) . Therefore , IL-17 inhibits T-bet and Th1 chemokine expression in the heart tissue of T . cruzi infected mice .
The results shown here demonstrate that T . cruzi infection results in the production of IL-17 , which regulates the immune response as well as the development of heart lesions during the course of infection . Moreover , IL-17 regulates the expression of the transcription factors T-bet , RORγt , and STAT-3 , the production of inflammatory cytokines and chemokines , and the expression of their receptors in the heart . First , we showed that trypomastigotes induce IL-17 production by CD4+ , CD8+ , and NK cells from naïve mice , and that an increased number of IL-17+ cells is observed during the acute phase of infection . Low IL-17 production was previously described for splenic CD4+ T cells from infected mice cultured with T . cruzi antigen [27] . In vivo the amount of leukocytes expressing IL-17 is very relevant , mainly if we consider that the number of lymphocytes increase dramatically during the infection [28] . The mechanism by which the parasites can trigger IL-17 production is unknown , but it could involve antigen recognition by pathogen associated pattern receptors . Parasite compounds lead to the production of IL-12 [29] , TNF-α [30] , and IL-10 [31] , and they activate TLR2 [32] , TLR4 [33] , and TLR9 [34] . The glycoinositolphospholipid from T . cruzi is a TLR4 agonist with proinflammatory effects [35] , and TLR4 activation is important for Th17 cell survival through the induction of IL-23 production by dendritic cells [36] . Moreover , mice deficient in TLR4 have markedly lower numbers of Th17 cells and a reduced capacity to produce IL-17 in an experimental model of arthritis [37] . IL-17 production and Th17 differentiation can be induced by IL-1β , IL-6 , and TGF-β[15] , which are all produced during T . cruzi infection [7] , [38] , [39] . Additionally , the phagocytosis of apoptotic leukocytes [40] , a frequent occurrence in T . cruzi-infected mice [41] , induces TGF-β synthesis [7] , [42] and IL-6 production [39] , thereby setting the stage for Th17 differentiation . Thus , it is possible that IL-17 production and Th17 driving do occur early after innate immune recognition of T . cruzi . We are currently performing additional experiments to study the possible role of TLR2 and TLR4 in T . cruzi-driven IL-17 production showed herein . Since trypomastigotes induced IL-17 production by spleen cells in vitro , it is possible that other strains with different tropism also induce similar amount of this cytokine production . However , this important question has to be addressed . Neutralization of IL-17 using an anti-IL-17 monoclonal antibody [43] did not change the parasitemia of T . cruzi-infected mice but resulted in decreased parasitism in heart tissue , and led to earlier mortality . As the Y strain is infective to several cell types , many tissues besides heart can act as parasite reservoirs and consequently can contribute to the peripheral blood parasitemia . IL-17 neutralization also resulted in increased production of IFN-γ and TNF-α in tissues and sera; both cytokines are known to activate macrophages to produce nitric oxide and promote the killing of intracellular amastigotes [4] . In fact , neutralization of IL-17 resulted in significantly less parasite DNA in the heart tissue , probably due to an increase in iNOS production ( data not shown ) . Unlike in rheumatoid arthritis , colitis , and autoimmune encephalitis ( EAE ) [21] , where IL-17 induces and sustains inflammation , in T . cruzi infection , IL-17 exhibits a regulatory effect . Some Th17 cells generated via TGF-β and IL-6 [15] , cytokines produced during T . cruzi infection [7] , [39] , produce high levels of IL-10 and prevent lesions in EAE [44] , consistent with our data suggesting an anti-inflammatory role for IL-17 . In accordance , scarce neutrophils were found in the inflammatory substrate of heart of T . cruzi-infected mice , independent of treatment or not with anti-IL-17 . Our data also show that IL-17 controls IFN-γ production , as previously observed in mice with EAE [45] . To further understand the regulatory effects of IL-17 , cultures of spleen cells from normal or infected mice were treated with anti-IL-17 . As observed in vivo , the cells exhibited increased intracellular expression of IL-12 , IFN-γ , and TNF-α , confirming a regulatory role for IL-17 in the immune response of T . cruzi infected mice . Since a high level of IFN-γ and TNF-α production can generate undesirable side effects [11] , [30] , [31] , IL-17 could be important in modulating these cytokines during acute T . cruzi infection , resulting in fewer heart lesions and delayed mortality . The increased IFN-γ production observed in mice treated with anti-IL-17 could be due to enhanced IL-12 secretion and increased T-bet expression , a transcription factor crucial for Th1 differentiation [46] . Therefore , IL-17 seems to act indirectly on the differentiation of Th1 lymphocytes through the control of IL-12 production . In contrast , the expression of RORγt and STAT-3 , which lead to Th17 differentiation [15] , were decreased in infected mice treated with anti-IL-17 . Analogous to Th1 and Th2 cell responses , Th17 cell responses are amplified by a positive feedback loop [47] . The expression of GATA-3 , a Th2 transcription factor [48] , remained unchanged . Of note , T . cruzi infection induces markedly increased expression of RORγt and STAT-3 , clearly indicating the induction of Th17 differentiation . One important question regards the mechanism of IL-17 modulation of the migration of inflammatory cells to the heart tissue of infected mice . Chemokines , including the ligands of CCR5 [49] and CCR2 [50] , [51] , are important to the mechanism that leads to myocarditis . In the hearts of mice treated with anti-IL-17 , we found increased expression of CCL2 , CCL3 , CCL4 , CXCL9 , and CCL11 , which , except for the last , clearly favor the Th1 type response . As the expression of chemoattractants is under the control of IFN-γ and TNF-α , it is possible that increased IFN-γ and TNF-α production , as a result of IL-17 neutralization and elevated IL-12 production , is responsible for the increased myocarditis . Accordingly , we found decreased expression of CCR3 ( a Th2-associated receptor ) and increased expression of CCR5 , a receptor crucial for the development of myocarditis [49] . This may explain the decrease in heart tissue parasitism , since the chemokines CCL2 , CCL3 , CCL4 , CCL5 , and CXCL9 as well as IFN-γ can induce iNOS activation in macrophages and cardiac myocytes [51] , [52] . Also , since CCR4 and CCR6 , but not CCR5 , are expressed by Th17 cells [53] , the decreased expression of CCR4 and CCR6 but increased expression of CCR5 clearly indicates a reduction in Th17 cells in the inflammatory lesions , confirming that treatment with anti-IL-17 was effective . In summary , our results show that IL-17 plays a role in the pathogenesis of T . cruzi-induced myocarditis . We propose that IL-17 elicited during the infection reduces IL-12 production and T-bet expression , which , as a consequence , decreases the production of IFN-γ , TNF-α , and chemokines . Therefore , IL-17 controls Th1 differentiation in T . cruzi-infected mice . The role of IL-17 in the pathogenesis of Chagas' disease is a question that deserves further investigation . Correlations between the levels of IL-17 , the extent of myocardial destruction , and the evolution of cardiac disease could identify a clinical marker of disease progression and may help in the design of alternative therapies for the control of chronic morbidity of chagasic patients .
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Chagas disease is caused by the intracellular parasite Trypanosoma cruzi . This infection has been considered one of the most neglected diseases and affects several million people in the Central and South America . Around 30% of the infected patients develop digestive and cardiac forms of the disease . Most patients are diagnosed during the chronic phase , when the treatment is not effective . Here , we showed by the first time that IL-17 is produced during experimental T . cruzi infection and that it plays a significant role in host defense , modulating parasite-induced myocarditis . Applying this analysis to humans could be of great value in unraveling the elements involved in the pathogenesis of chagasic cardiopathy and could be used in the development of alternative therapies to reduce morbidity during the chronic phase of the disease , as well as clinical markers of disease progression . The understanding of these aspects of disease may be helpful in reducing the disability-adjusted life years ( DALYs ) and costs to the public health service in developing countries .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"immunology/immunomodulation",
"immunology/immunity",
"to",
"infections",
"immunology/immune",
"response",
"infectious",
"diseases/protozoal",
"infections"
] |
2010
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IL-17 Produced during Trypanosoma cruzi Infection Plays a Central Role in Regulating Parasite-Induced Myocarditis
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Mycetoma is caused by the subcutaneous inoculation of filamentous fungi or aerobic filamentous bacteria that form grains in the tissue . The purpose of this study is to describe the epidemiologic , clinic , laboratory , and therapeutic characteristics of patients with mycetoma at the Oswaldo Cruz Foundation in Rio de Janeiro , Brazil , between 1991 and 2014 . Twenty-one cases of mycetoma were included in the study . There was a predominance of male patients ( 1 . 3:1 ) and the average patient age was 46 years . The majority of the cases were from the Southeast region of Brazil and the feet were the most affected anatomical region ( 80 . 95% ) . Eumycetoma prevailed over actinomycetoma ( 61 . 9% and 38 . 1% respectively ) . Eumycetoma patients had positive cultures in 8 of 13 cases , with isolation of Scedosporium apiospermum species complex ( n = 3 ) , Madurella mycetomatis ( n = 2 ) and Acremonium spp . ( n = 1 ) . Two cases presented sterile mycelium and five were negative . Six of 8 actinomycetoma cases had cultures that were identified as Nocardia spp . ( n = 3 ) , Nocardia brasiliensis ( n = 2 ) , and Nocardia asteroides ( n = 1 ) . Imaging tests were performed on all but one patients , and bone destruction was identified in 9 cases ( 42 . 68% ) . All eumycetoma cases were treated with itraconazole monotherapy or combined with fluconazole , terbinafine , or amphotericin B . Actinomycetoma cases were treated with sulfamethoxazole plus trimethoprim or combined with cycles of amikacin sulphate . Surgical procedures were performed in 9 ( 69 . 2% ) eumycetoma and in 3 ( 37 . 5% ) actinomycetoma cases , with one amputation case in each group . Clinical cure occurred in 11 cases ( 7 for eumycetoma and 4 for actinomycetoma ) , and recurrence was documented in 4 of 21 cases . No deaths were recorded during the study . Despite of the scarcity of mycetoma in our institution the cases presented reflect the wide clinical spectrum and difficulties to take care of this neglected disease .
Mycetoma is a chronic subcutaneous infections caused by the inoculation of filamentous fungi ( eumycetoma ) or aerobic filamentous bacteria ( actinomycetoma ) that form grains in the affected tissues [1] . It´s considered a neglected disease by the World Health Organization ( WHO ) since 2016 and remains without any control program for prevention or surveillance [1 , 2] . Mycetoma occurs worldwide and prevails in tropical and subtropical regions , especially in sub-Saharan areas of Africa , India , and Mexico [3 , 4] . In South America , cases have been reported in Venezuela , Colombia , Brazil , and Argentina [1 , 3 , 5] . The incidence and prevalence of mycetoma in Brazil are unknown , since it is not considered a public health problem , as its frequency is smaller than other diseases such as sporotrichosis , tuberculosis , leprosy , and dengue ( the latter two are classified as neglected diseases by the WHO ) [6] . Mycetoma evolves slowly in its clinical manifestation . Laboratory diagnosis and treatment are difficult , presenting significant medical , occupational and socioeconomic impacts [2 , 7] . In this study , we describe the epidemiological , clinical , laboratory , and therapeutic aspects of patients treated at a reference hospital in Rio de Janeiro , Brazil , between 1991 and 2014 .
The study was approved by the Research Ethics Committee of the INI / Fiocruz , on November 25 , 2013 under the number 477 . 037 . All participants gave their written consent , with the exception of those who died before the study . In all cases the identity and information of each patient were preserved . Histological examination was performed using haematoxylin-eosin , Grocott’s methenamine silver , Periodic acid–Schiff , and Gram-Brown-Brenn stains . Biopsy specimens were submitted for direct microscopic examination with 10% potassium hydroxide where grains were classified according to their size , shape , colour , consistency and presence of hyphae or filamentous bacteria . Culture on Sabouraud's Dextrose Agar 2% and Mycobiotic Agar was performed for eumycotic grains and in/on Lowenstein-Jensen medium , defibrinated sheep blood agar chocolate agar and thioglycolate medium with resazurina for actinomycotic grains . Bacterial and fungal etiologic agents were identified by examination of the colonies in culture . Ultrasound , radiography , computerized tomography ( CT ) , and magnetic resonance imaging ( MRI ) were performed to determine deep tissue and bone involvement and presence of grains . Actinomycetoma patients were treated with oral sulfamethoxazole-trimethoprim ( SMX-TMP ) 800/160 mg BD , alone or in combination with alternate cycles of 15 mg/kg/day intravenous amikacin for three weeks in cases with bone destruction . Other antimicrobials were given in case of secondary infections . Eumycetoma patients were treated with itraconazole ( 200 mg , BD ) alone or , in cases without consistent clinical response after six months , in combination with fluconazole 200 mg/day , terbinafine 250 mg/day , or amphotericin B 1mg/kg . Surgical treatment was indicated for small and delimited lesions and in cases of bone destruction . Amputation was indicated in cases lacking a satisfactory antimicrobial response associated to severe bone destruction of the affected segment . The patients were followed-up bimonthly at the outpatient clinic to assess clinical responses to treatment and drug side effects . A complete cure was defined with the healing of lesions , bone remodelling , and absence of grains upon imaging examination . After the determination of the clinical cure , outpatient follow-up turned annual , to assess the possibility of recurrence . Data retrieved from patients records were analysed using descriptive statistics with the Statistical Package for the Social Sciences , version 20 . 0 . Data were summarized as percentages for categorical variables and mean , median , and range for continuous variables .
A total of 21 mycetoma cases were included in the present study: 13 eumycetoma and 8 actinomycetoma patients . The main sociodemographic aspects of the mycetoma patients are summarised in Table 1 . In brief , the male to female ratio was 1 . 3:1 , and the mean age was 46 years old ( range 28–93 years ) . However , the mean age for eumycetoma was 51 . 3 years old and 38 . 6 years old for actinomycetoma . The non-white ethnicity/race predominated with 66 , 66% . Most patients ( 71 . 43% ) came from the southeast region of Brazil , and 28 , 57% came from the northeast region . These regions correspond to the possible original infection sites . Comorbidities occurred in 10 patients . Eight of them presented a single comorbidity and the others had two comorbidities . In general , high blood pressure , diabetes mellitus , HIV positive ( Figs 1 and 2 ) , and asthma were found . The time from onset of signs and symptoms to medical care ranged from 2 to 420 months ( mean = 77 . 68 months ) . The average time was higher for eumycetoma ( mean = 105 . 76 months ) than actinomycetoma ( mean = 36 . 75 months ) . The foot ( Figs 3 , 4 and 5 ) was affected in 17 cases ( 80 . 9% ) , the thigh was affected in two cases , and the hand and ankle were affected in one case each . A history of trauma was reported in 17 ( 80 . 9% ) cases . The grains were mainly identified through histopathological examination with 90 . 4% positivity in these methods and 9 . 6% through direct microscopy . We retrieved the etiological agents in 61 . 5% eumycetoma cases ( Table 2 ) and in 75% of actinomycetoma cases ( Table 3 ) . In the eumycetoma group , the Scedosporium apiospermum species complex was identified in three cases , Madurella mycetomatis was isolated from two cases and Acremonium sp . was isolated from one case . From the remaining two patients , the isolated filamentous fungi could not be identified , as they only produced hyphae without any conidia or spores , and therefore they were named Mycelia sterilia ( cases 7 and 8 , Table 2 ) . It is important to note that these two organisms were consistently isolated as pure cultures in at least three consecutive mycological examinations . In the actinomycetoma group we isolated Nocardia spp . from three cases , Nocardia brasiliensis from two cases and Nocardia asteroides from one case ( Table 3 ) . In two cases the culture were negative . All patients underwent radiography of the affected site with exception of patient 7 of Table 3 , who underwent complete excision with security margin of the lesion during the diagnostic procedure ( Fig 6 ) . Ultrasonography was performed in 18 cases , with the observation of subcutaneous nodules in all of them . Ten patients underwent CT scans and seven patients underwent MRI . Bone involvement was present in 9 cases , five from eumycetoma and four from actinomycetoma . Secondary bacterial infection was diagnosed in four cases , two of them had Staphylococcus aureus associated infection treated with systemic antibiotics guided by susceptibility tests . The other two cases were treated empirically . Patients with eumycetoma received 200 mg BD itraconazole alone ( 8/13 ) or in combination with 200 mg/day fluconazole ( 3/13 ) , or 250 mg/day terbinafine ( 2/13 ) . In case 1 ( Table 2 ) , when the patient became pregnant during itraconazole treatment , this drug was suspended and we tried to use liposomal amphotericin B due to clinical worsening , without success . Actinomycetoma patients received 800/160 mg sulfamethoxazole-trimethoprim BD in most of cases ( 75% ) . As monotherapy in five cases , one case with cycles of 15 mg/kg/day amikacin sulphate and another case received 500 mg cephalexin four times a day . The used of cephalexin occurred because of the repeated secondary bacterial infection . The case 7 ( Table 3 ) with a small and well delimited lesion in lower limb underwent complete excision with security margin and therefore was not treated with antimicrobials . The case 8 ( Table 3 ) , who presented with multiple foci of bone destruction , was submitted to amikacin cycles , which had to be stopped after the fifth cycle due to changes in audiometry and increased creatinine levels , without lifelong clinical consequences . Itraconazole was used in all cases and combined with another antifungal agents ( 38% ) in refractory cases . The average treatment time was 35 . 04 months ( range 6–144 months ) . The mean treatment time was 42 . 53 months for eumycetoma and 22 . 87 months for actinomycetoma . The average treatment time for patients with bone destruction was 70 months ( median 55 months ) for eumycetoma cases and 33 months ( median 36 months ) for actinomycetoma cases . Amputation was recommended for three patients with eumycetoma , one of them accepted the procedure and the other two remain receiving drug treatment until now . In the actinomycetoma group , one patient accepted amputation . Surgical excision of small lesions were performed in nine eumycetoma patients and three actinomycetoma patients . Clinical cure occurred in 11 ( 52 . 38% ) of all cases . Of the 13 eumycetoma patients , seven were cured , two abandoned follow up and another two patients are still under antifungal treatment . Of the five eumycetoma patients with bone involvement , one underwent amputation , two remained in treatment , one remain under observation and one abandoned treatment . Of the eight actinomycetoma patients , four were cured , one abandoned the treatment and three are under treatment . Of the four actinomycetoma cases with bone involvement , one patient was underwent amputation , two remained in treatment and one abandoned treatment . If we consider the cure without sequelae ( amputation ) , the rate falls to 42 . 8% . Recurrence of infection was observed in four patients: one with actinomycetoma and three with eumycetoma . The time to recurrence was 24 months for the actinomycetoma case and ranged from 8 to 96 months ( mean = 36 . 6 months ) for eumycetoma cases . Treatment dropouts was high ( 23% ) and recurrence was also frequent ( 19% ) and prevailed in patients that had undergone surgery , especially in the eumycetoma group . The broad range of treatment duration until clinical cure ( 6–114 months ) was a striking observation of this study .
The 21 mycetoma cases diagnosed in the 24-year period of this study demonstrate the low frequency of mycetoma in our institution at Rio de Janeiro , Brazil . Most reports of mycetoma in Brazil describe one or a few cases , reinforcing the scarcity of the disease in this country . To achieve a better comprehension on this subject we performed a search of articles on PubMed ( from 1980 to 2014 ) using the MESHterms “Mycetoma” , “Actinomycetoma” , and “Eumycetoma” alone or in combination with “Brazil” . During this period , 272 mycetoma cases were reported ( Table 4 ) . This number is smaller than that observed in Sudan and Mexico [8 , 9 , 10] . For instance , in Mexico , where 483 mycetoma cases were diagnosed at a single hospital during the same period [11] . In 2013 , van de Sande et al . [1] estimated the prevalence of mycetoma cases in Mexico and the Sudan as 0 . 15 and 1 . 81 cases per 100 , 000 inhabitants , respectively , compared to the prevalence of less than 0 . 001 per 100 , 000 inhabitants in Brazil . The predominance of eumycetoma in our study might not represent the real scenery of mycetoma in Brazil , as the Brazilian literature reveals a higher frequency of actinomycetoma ( Table 4 ) [12 , 13 , 14 , 15] . The involvement of male individuals above 30 years old with an acral location likely due to increased risk exposure during labour activity without safety equipment is in accordance with mycetoma characteristics [5 , 16 , 17] . Although eumycetoma and actinomycetoma share similar clinical aspects , we noted that eumycetoma cases usually tend to be more silent and chronic , while actinomycetoma cases were more inflammatory and painful . This fact may explain why patients with eumycetoma take longer to seek medical care . We noted that six of our patients moved from the Northeast region of Brazil to the Rio de Janeiro state , in the Southeast region , probably attracted for job possibilities in a state with higher socio-economic index , higher urbanization of population and better health infrastructure . For this reason , we assume that , for these patients , the place where infection occurred was not in Rio de Janeiro . Comorbidities are not associated to more severe or atypical forms of mycetoma and there are no changes in the course of mycetoma in the HIV infected patient [18 , 19 , 20] . Although it requires further investigation , pregnancy may be linked to more severe clinical course of mycetoma [21 , 22 , 23 , 24] as in case 1 ( Table 2 ) that developed severe bone destruction during pregnancy , resulting in amputation of the affected limb [25] . The mycetoma agents identified in our study are consistent with previous reports . In the actinomycetoma group , Nocardia spp . , particularly N . brasiliensis , predominated and in the eumycetoma group , Scedosporium apiospermum . From 1980 to 2014 , the main bacterial agents identified in Brazil were Nocardia brasiliensis [15 , 26 , 27–32] , Nocardia asteroides [15 , 33] , Nocardia caviae [34] , Actinomadura madurae [13 , 35 , 36] , Actinomadura pelletieri [14] , and Streptomyces somaliensis [15] . For eumycetoma were Madurella mycetomatis [15 , 25 , 37 , 38] , Madurella grisea [39–45] , Acremonium falciforme [46] , Acremonium kiliense [47] , Scedosporium apiospermum [12 , 18 , 48 , 49 , 50] , Fusarium solani [51] , Exophiala jeanselmei [44 , 52 , 53] and Aspergillus sp . [12] . In our series of cases the diagnosis of mycetoma was made mainly by histopathological examination of affected tissues with visualization of the grains ( approximately 91% of cases ) , while the isolation of the etiologic agent by culture was obtained in 66 . 6% of cases [15] . Implementation of molecular tools have recently demonstrated an improvement in the sensitivity and specificity in diagnosing mycetoma [16] . Radiography and ultrasonography were the most often used imaging because of their low cost and accessibility . Ultrasonography was crucial in identifying the presence of grains before diagnosis , during and after the therapeutic follow-up . Magnetic resonance imaging is the gold standard imaging method for mycetoma diagnosis and was important to delineate the involvement of internal structures and surgical planning [54] . CT scan was used if no bone involvement was detected by radiography . Mycetoma treatment is challenging and usually requires long periods of drug therapy with or without surgical procedures ( complete excision of the lesion , bone curettage , amputation ) [1 , 5 , 8 , 10] . Itraconazole is the most common antifungal agent used for eumycetoma treatment [2] . Voriconazole and posaconazole have been indicated for refractory cases of mycetoma [58] primarily caused by S . apiospermum and Acremonium sp . [48 , 59–62] . They are expensive in underdeveloped countries and are not available in our institution . Isavuconazole and ravuconazole seem to be satisfactory against M . mycetomatis [63 , 64] but their effectiveness against other eumycetoma agents need to be investigated . The first patient in this series of cases was evaluated in 1991 , and because of this , the combination of drugs used was based on the available drugs at that time in our institution . The combined itraconazole/fluconazole , and itraconazole/terbinafina treatment in this study was chosen because of our good experience in treating extensive cutaneous lesions of chromoblastomycosis caused by Fonsecaea pedrosoi [55] . However , currently the itraconazole/fluconazole combination for mycetoma is not effective . Although liposomal amphotericin B are no longer recommended for first-line eumycetoma treatment , due to the high minimum inhibitory concentrations required for most eumycetoma agents [16 , 17 , 21 , 56 , 57] , we tried to use only in one case due to clinical worsening during pregnancy , without success [25] . The recommended treatment for actinomycetoma is SMX/TMP as monotherapy or in combination with amikacin sulphate [10] . The association usually gives a cure rate above 90% [2 , 65 , 66] . Laboratory tests are required to assess possible adverse effects , as ototoxicity ( cochlear lesions ) and nephrotoxicity , which are permanent injuries , but are not progressive when treatment is suspended . In case 8 of Table 3 a combination with amikacin sulphate was used due to bone destruction . Amoxicillin and clavulanate are alternative drugs during pregnancy , for resistant cases or for patients with adverse effects from aminoglycoside [3] . Rifampicin can be used , but in Brazil it is reserved for tuberculosis and leprosy treatment , diseases with a high burden in our country . Minocycline and moxifloxacin are also treatment options for actinomycetoma [2 , 67] . Surgery is indicated for small well localised lesions or in patients who are not responding to medical therapy or to reduce disease burden in massive lesions to allow a better response to medical therapy . [68] . Usually , actinomycetoma require less surgery management then eumycetoma [10] . Amputation are indicated for those patients with massive disease with no response to medical treatment or with massive bone destruction or in case with severe secondary bacterial infection not responding to medical treatment or with severe drug side-effects . [3] Although our institution has provided all antimicrobials necessary for the treatment free of cost to all patients , the cure rate in this study was low , which reflects the difficulties in treating this disease . Besides the inconvenience to take pills every day for a long period , the total cost of mycetoma treatment is unaffordable for people living in poor regions where the disease commonly occurs . We suggest that the low rate of cure in our study is multifactorial , including the delay to obtain a correct diagnosis , and the scarcity of specialized surgical services with knowledge about this disease that allow the management of the most advanced cases . The postponement of diagnosis favours the occurrence of severe cases that are refractory to the treatment due to the low bioavailability and efficiency of some drugs in advanced lesions . Some patients of our study took more than a year to obtain a correct diagnosis and initiate adequate treatment . In our cases , treatment dropouts was high and they were likely related to delayed clinical responses and the prolonged treatment times . Recurrence was also frequent [56] and prevailed in patients that had undergone surgery , especially in the eumycetoma group [38] . The reasons are unknown , but may be likely due to the existence of undiagnosed subclinical lesions fungal defence mechanisms against antifungal drugs or incomplete surgical procedures . It is interesting to note that in case 2 ( Table 2 ) , the patient was considered clinically cured , but presented recurrence at the eighth year of follow-up [38] . In this case , however , exogenous reinfection cannot be ruled out . We did not observe a relationship between recurrence and a specific etiologic agent . In rarely cases , mycetoma can spread along the lymphatics to the regional lymph node [6 , 68] . Few blood-spread mycetoma cases [7 , 16 , 69 , 70 , 71 , 72] and deaths related to the infection were reported [4 , 9 , 70] , but they were not observed in our study . Although with few cases , this study , highlights the wide spectrum of clinical manifestations of mycetoma , such as localized lesions , bone disease , worsening with pregnancy , recurrence and amputation cases . We also emphasize the challenges to treat and control this neglected disease . The accurate management of each case requires multiple experts including clinicians , surgeons , microbiologists , radiologists working together to assess the best therapeutic approach , which includes a prolonged treatment followed by a long follow up after achieving clinical cure . Rehabilitation is necessary in cases of deformity and amputation , unacceptable sequelae in the 21th century .
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Mycetoma is a major health problem in tropical areas and is prevalent among people of low socio-economic status . As in many other regions of the world , the incidence and prevalence of mycetoma in Brazil is unknown . This study describes some aspects of mycetoma patients in 24 years of experience at the National Institute of Infectious Diseases at the Oswaldo Cruz Foundation , Rio de Janeiro , Brazil and contribute to the knowledge on mycetoma epidemiology globally .
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2017
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Review of 21 cases of mycetoma from 1991 to 2014 in Rio de Janeiro, Brazil
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Despite decades of community-based mass drug administration ( MDA ) for neglected tropical diseases , it remains an open question as to what constitutes the best combination of community medicine distributors ( CMDs ) for achieving high ( >65%/75% ) treatment rates within a village . Routine community-based MDA was evaluated in Mayuge District , Uganda . For one month , we tracked 6 , 148 individuals aged 1+ years in 1 , 118 households from 28 villages . Praziquantel , albendazole , and ivermectin were distributed to treat Schistosoma mansoni , lymphatic filariasis , and soil-transmitted helminths . The similarity/diversity between CMDs was observed and used to predict the division of labour and overall village treatment rates . The division of labour was calculated by dividing the lowest treatment rate by the highest treatment rate achieved by two CMDs within a village . CMD similarity was measured for 16 characteristics including friendship network overlap , demographic and socioeconomic factors , methods of CMD selection , and years as CMD . Relevant variables for MDA outcomes were selected through least absolute shrinkage and selection operators with leave-one-out cross validation . Final models were run with ordinary least squares regression and robust standard errors . The percentage of individuals treated with at least one drug varied across villages from 2 . 79–89 . 74% . The only significant predictor ( p-value<0 . 05 ) of village treatment rates was the division of labour . The estimated difference between a perfectly equal ( a 50–50 split of individuals treated ) and unequal ( one CMD treating no one ) division of labour was 39 . 69% . A direct tie ( close friendship ) between CMDs was associated with a nearly twofold more equitable distribution of labour when compared to CMDs without a direct tie . An equitable distribution of labour between CMDs may be essential for achieving treatment targets of 65%/75% within community-based MDA . To improve the effectiveness of CMDs , national programmes should explore interventions that seek to facilitate communication , friendship , and equal partnership between CMDs .
For public interventions , the first-informed individuals influence the spread of information and uptake within the target population [1] . Understanding who should be the first-informed individuals or the deliverers of an intervention is a widespread challenge for any area of public policy , but in particular for global health programmes [2–4] . Little is known about how best to introduce and to maintain global health programmes in resource-poor settings where access to formal medical care and health-seeking behaviours are limited . Effective global health programmes rely on identifying the appropriate starting points for an intervention , e . g . who should deliver drugs , who should be treated first , and who should act as health promoters . One successful and extensively used model for identifying the starting points for global health programmes is mass drug administration ( MDA ) [5] . MDA is the blanket , diagnosis-free distribution of single dose preventive chemotherapies to individuals within at-risk areas for neglected tropical diseases ( NTDs ) [6] . The frequency and implementation of MDA is determined by the prevalence of infection within a geographical catchment , school , or community and varies by disease [6] . Several methods of MDA implementation exist , utilizing communities , primary schools , or child health days . The most common method of implementation is through community-based MDA , which is used to treat schistosomiasis , lymphatic filariasis , trachoma , and onchocerciasis with some communities also benefiting from the treatment of soil-transmitted helminths ( STHs ) because of coendemicity with lymphatic filariasis . To promote local ownership of MDA , national programmes instruct individuals within NTD-endemic areas to select local community medicine distributors ( CMDs ) through open , community-wide meetings [7] . CMDs serve as volunteers , apart from the reimbursement for travel costs to attend annual training sessions , and are tasked with either moving from home-to-home ( e . g . schistosomiasis ) [8] or with mobilizing individuals to retrieve drugs from a central post ( e . g . lymphatic filariasis ) [9] . Progress towards NTD control , including community-based MDA , has been proposed as a platform for measuring access to universal health coverage [10] . In 2017 , nearly 1/3rd of school-aged children , who are included in the World Health Organization ( WHO ) Roadmap for NTDs [11] and require preventive chemotherapies for schistosomiasis or STHs remained untreated [12] . For example , after 10 years of community-based MDA in Mayuge District , Uganda , CMDs treated only 56 . 66% of eligible individuals with at least one drug for schistosomiasis , lymphatic filariasis , or STHs [13] . Therefore , a better understanding is needed of how to increase the effectiveness of CMDs . The context in which CMDs have been studied in order to improve MDA includes 1 ) how best to alleviate the opportunity costs of time volunteered [14 , 15] , 2 ) how to reduce capacity constraints resulting from a limited number of CMDs [14 , 16 , 17] , 3 ) the impact of financial or in-kind incentives for CMDs [18] , 4 ) the role of knowledge , attitudes , and practice as well as available health system support in promoting CMD motivation [19] , 5 ) the social biases that manifest in the CMD’s decision on whom to treat [13 , 20] , and 6 ) the personal characteristics of CMDs that determine their performance during MDA [21] . Despite the wide variation of treatment rates across communities [4] , there is a limited understanding from both national MDA programmes and communities of how and whether CMDs should be replaced before they choose to resign , and specifically of what combination of CMDs is best for achieving the highest treatment rates . Two aspects of CMD selection have been studied and associated with increased treatment rates: exploiting local social network structures to choose well-placed CMDs [4 , 22] and including CMDs with diverse kinship affiliations so that a CMD treats only individuals with a shared clan membership [16 , 17] . The kinship studies [16 , 17] identify similarity between CMDs and MDA recipients rather than measure the similarity between CMDs , and do not consider network or socioeconomic similarity . Thus , it remains an open question as to if/how the similarity of CMDs affects MDA outcomes . To identify the best combination of CMDs , there is a need to understand how network , demographic , and socioeconomic similarity between CMDs affects their performance during MDA . Similarity has been widely shown elsewhere to determine peer effects , i . e . how one person influences another person ( either directly or indirectly ) [23–28] . Yet , how CMDs influence one another or how shared CMD affiliations affect MDA , to our knowledge , has not been studied . Here we conduct the first analysis of CMD similarity by comparing the networks and personal attributes of CMDs to identify what combination of CMDs best facilitates the reach of MDA . Moreover , to further delve into peer effects , we provide the first study of how CMD similarity influences the division of labour between CMDs . We answer the following question . How does CMD similarity affect the division of labour and treatment rates achieved during MDA ?
This study was reviewed and approved by the Uganda National Council of Science and Technology ( SS4077 ) , and the University of Cambridge School of Humanities and Social Sciences ( HSSREC2016 . 6 ) . Written informed consent was obtained from all respondents . For respondents who indicated they were unable to write or who preferred to provide fingerprints , verbal informed consent and a fingerprint signature were obtained . Using methods described and validated in Chami et al . [4 , 20] , routine community-based MDA was tracked in 31 villages in Mayuge District , Uganda from mid-July to mid-August 2016 . The study area predominantly comprises fishing villages along Lake Victoria , which are hyperendemic ( >50% prevalence ) with Schistosoma mansoni [29] . To remove administrative barriers that may delay the start of MDA , researchers provided local District Vector Control Officers—the individuals responsible for routinely training CMDs—with cars to start MDA within three days in July for all study villages . Study surveys were conducted after one month of MDA . Preventive chemotherapies were only available from the community-based MDA programmes during the study period . Two CMDs were tasked with approaching all households , i . e . moving door-to-door , and administering preventive chemotherapies . There were no limits on treatment rates achievable by CMDs due to insufficient medicine supplies . Researchers provided the Vector Control Officers with enough pills/tablets for all CMDs to treat all eligible individuals within their villages . Survey teams conducted surprise checks of CMD homes after the one-month MDA tracking and pills/tablets for all medicines remained with all CMDs . Praziquantel was distributed to treat school-aged children and adults ( individuals aged 5+ years ) for potential infections with S . mansoni . Albendazole and ivermectin were administered to treat school-aged children and adults ( all individuals aged 5+ years old ) for potential lymphatic filariasis infections . Due to hookworm endemicity , albendazole was provided to pre-school aged children , school-aged children and adults ( all individuals aged 1+ years old ) , although albendazole was not donated for treating hookworm through community-based MDA [29] . The most common method of MDA implementation for schistosomiasis and STHs in Uganda is the distribution of medicines through primary schools , i . e . excluding adults for treatment and using schoolteachers as distributors instead of CMDs [8] . The high prevalence of S . mansoni infections and the endemicity of lymphatic filariasis enabled community-wide treatment for schistosomiasis and STHs , respectively . When lymphatic filariasis treatment stops in our study area then community-wide treatment may stop for STHs . MDA was community-based as opposed to community-directed in that communities did not lead the design of MDA , which was completed by the national programmes . Communities selected CMDs , but did not choose the dates , time period , or method of distribution for MDA . Communities also were not formally involved in the monitoring of CMDs , which was the task of the District Vector Control Officers . The national MDA programme instructed communities to select systematically two CMDs through a community-wide meeting and to choose individuals who were literate and able to fill in NTD registers . Communities also were encouraged to have gender balance between CMDs , i . e . one female and one male CMD per village . No other instructions for the selection or replacement of CMDs were provided by the national MDA programme . Communities did not strictly follow national recommendations . Village leaders ( local government members or village health team members ) directly selected more than half of the CMDs instead of holding community-wide meetings [21] . Systematic random sampling of households was conducted [21] . Village registers of households—ordered by year of settlement—were used to select 40 households per study village . Household heads and lead wives were interviewed to provide information on all members of the household aged 1+ years—the minimum criteria for MDA eligibility . In addition to the systematic random sampling , all CMDs and their household heads were interviewed . Households of CMDs only were included in the calculation of treatment outcomes if selected by chance through the systematic random sampling . Using a structured questionnaire [21] , two sets of treatment outcomes were examined for participants who were selected through systematic random sampling: village treatment rates and the division of labour between CMDs . Village treatment rates comprised the overall level of treatment within a village , i . e . the work of both CMDs , and were calculated at both the individual and household levels . Treatment responses were recorded by an independent team of surveyors who conducted surprise visits to villages after one month of undisturbed MDA , as described in Chami et al . [4 , 20] . At the individual level , treatment rates were measured as the percentage of eligible individuals who were offered and had ingested at least one drug of praziquantel , albendazole , or ivermectin . This indicator most closely aligns with the WHO’s indicator of surveyed coverage [4] . We used a conservative measure of treatment with at least one drug to reduce the dimensionality of the analysis ( number of models run ) and to account for endogeneity that arises with individual drug outcomes , i . e . drug-specific treatment rates are strongly positively correlated [21] . At the household level , treatment rates were measured as the percentage of households with at least one eligible person who was offered and had ingested at least one drug of praziquantel , albendazole , or ivermectin . Household-level treatment rates were of interest as the Uganda MDA programme instructed CMDs to move from home-to-home within our study area and treatment rates for individuals are strongly positively correlated within a home [20] . Also , household-level treatment rates represent the percentage of homes approached by CMDs [4] . WHO disease-specific treatment targets include treatment of 75% of eligible individuals with praziquantel for schistosomiasis and albendazole for hookworm , and 65% of eligible individuals with albendazole plus ivermectin for lymphatic filariasis . As a note , treatment outcomes were determined by drug delivery efforts from CMDs since only less than 1% of MDA recipients refused to ingest offered medicines [21] . Hence , in our study , the offer and ingestion of medicine ( treatment ) also can be thought of as indicative of evidence of contact with CMDs . We undertook a proof-of-principle investigation into the relevance of the division of labour for predicting village treatment rates , which are used to assess progress towards WHO treatment targets [30] . To develop the first measure of the division of labour for CMDs , we sought a simple indicator that 1 ) did not interfere with routine MDA , 2 ) captured the primary objective of MDA , i . e . maximizing the number of people treated , and 3 ) could be applied in various geographical or social contexts . In this respect , the division of labour was outcome-based and focused on the number of people treated by each CMD . Importantly , neither the national MDA programme nor local health facilities provided instructions to CMDs for dividing labour . The national MDA programme only indicated to CMDs that they should treat all eligible individuals within their village . Hence , CMDs were not pre-allocated households or geographical areas of a village . CMDs did not hold discussions with their communities about the division of labour . Consequently , we assumed here that how best to divide labour to meet programmatic goals was the sole decision of CMDs . The entire village was considered for assessing the labour of each CMD . In Uganda , the village is the lowest administrative unit; there are no further formal subdivisions that could have been exploited for the division of labour . Moreover , no intra-community spatial divisions were considered due to the small size of study villages and previously shown irrelevance of the number of homes for explaining village treatment rates in our study area [4] . On average , there were only 238 homes per village ( range 87–535 homes ) . Concerning spatial aspects , the village ecology , such as the number of roads or swamps , has been shown to be uninformative for MDA in our study area [4] . The spatial spread/diameter of the study villages also has been shown to be unrelated to village treatment rates [4] . The furthest distance in metres between two homes has been shown to be on average only 1 . 26 km ( std . dev . 428 . 29 m ) [4] . The mean distance between two homes within our study villages has been measured at 400 . 11 m ( std . dev . 142 . 27 m ) [4] . For the division of labour , the percentage of eligible individuals or eligible households treated by each CMD was examined . For drugs offered to eligible individuals , the household respondent provided the name of the CMD who offered treatment . All respondents knew who treated whom . Few eligible individuals ( <12% ) were offered treatment by both CMDs , which included either CMDs separately approaching the same individual or both CMDs being present at the same time to treat the same individual . More detailed methods on the calculation/attribution of individual CMD treatment rates are provided in Chami et al . [21] . ‘Treated’ was defined as described for the village-level treatment rates . The division of labour was calculated as a ratio of treatment rates for the two CMDs in each village . For the two CMDs , the lowest treatment rate was divided by the highest treatment rate to create a normalized village-level outcome . The division of labour was an indicator from 0–1 where 0 was a perfectly unequal division of labour ( one CMD treated no one ) and 1 was a perfectly equal division of labour ( CMD treatment rates were equal ) . There were no villages where both CMDs treated no one . The division of labour was calculated for treatment rates at both the individual and household levels . Although MDA consists of separate tasks such as registering households , sensitizing individuals , and mobilizing the community , CMDs in our study area perform these tasks whilst they treat individuals [4 , 13 , 21] . Thus , in our study context , the division of labour for treatment outcomes also represents the division of labour dedicated to diverse MDA tasks . CMDs were interviewed and asked to provide the names of their close friends , using the following structured prompt [4 , 13 , 22] . The individuals named as close friends by CMDs also were interviewed . The friends of CMDs were provided with a list of names , which included all individuals who were named by both CMDs as well as the names of the CMDs . Friends of CMDs were then asked to indicate with whom they had close friendships . Hence , CMDs could belong to the same network component , i . e . a path could exist between the two CMDs , due to either a friendship between CMDs or a friendship between the friends of CMDs . Moreover , a CMD could have more than 10 ties due to the friends of the other CMD naming the CMD of interest . All friendship networks were analyzed as undirected; if an individual was named or had named someone then there was a tie between those two individuals . Sixteen indicators of similarity between the two CMDs in each village were examined , which included three network characteristics and 13 personal attributes . For network similarity ( structural equivalence ) , three variables that captured both direct and indirect ties were calculated using NetworkX in Python v2 . 7 [31] . A direct tie was a binary indicator of friendship between CMDs . The Jaccard index captured indirect ties between CMDs and the similarity of their network neighbourhood , i . e . common friends . It was calculated as the number of common friends divided by the total number of friends across both CMDs . To gain insight into the cohesion between CMDs and to account for the fact that influence between CMDs may travel further than two network steps ( beyond common neighbours ) [27 , 32] , the minimum node cut was calculated . The minimum number of nodes ( friends ) that would need to be removed from the network to disconnect CMDs , i . e . to remove all paths between CMDs , was counted then normalized by dividing by the total number of friends for both CMDs . A comparison of direct ties versus indirect ties was of interest to understand by what means could peer effects occur between CMDs [24] . With direct ties , peer effects occur due to an existing channel of communication between two individuals [28] . Alternatively , indirect ties have been shown to influence two individuals of interest through either competition or comparison [25] . For competitive influences , it has been shown that two individuals vie for the attention of the same friends ( due to the overlapping friendship group ) and this competition is what drives similar behaviours [25 , 26] . Alternatively , indirect ties may signal shared friendships that are used as a reference for behaviours , i . e . an individual compares themself to their group of friends and two individuals with shared friends will compare themselves to the same group [4 , 26 , 27] . CMDs were interviewed using a structured questionnaire [21] in order to observe 13 personal attributes . The method of CMD selection was recorded as a categorical variable and included nominations from a community-wide meeting , the village health team , or a local council ( village government ) member . The total number of years as a CMD was noted . Eleven demographic and socioeconomic characteristics were observed . Age was rounded to the nearest year . Gender was a binary variable and equal to one if the CMD was female . Education was measured as a categorical variable to represent the highest level of education attained and included primary school , secondary school , or post-secondary school diplomas . Binary indicators for majority tribe and religion were equal to one if the CMD belonged to the majority tribe or religion of their village , respectively . Occupation was a categorical variable that included values for farmer , fisherman/fishmonger , and ‘other’ jobs; occupation was coded to capture the main occupations in the study area [29] . Formal status was a binary indicator that was equal to one if the CMD was a religious/tribe/clan leader , on the beach management committee , or a member of the local council ( village government ) . Two binary indicators of preventative health behavior were measured using WHO and United Nations International Children’s Emergency Fund ( UNICEF ) guidelines [33] . A CMD belonged to a household that used a protected water source if drinking water was retrieved from piped water , village taps , boreholes , or protected wells . Private home latrine ownership included only covered pit latrines with privacy . Home quality score was the total sum of scores ( min . 3 , max . 12 ) for the floor , walls , and roof materials ( four for each category , ranked 1–4 ) [13] . The ‘years in village’ was a count of the total years since the CMD’s household had settled in the current village . To calculate attribute similarity between CMDs , binary indicators were constructed for all 13 CMD attributes , including MDA-related variables , and equal to one if CMDs were similar [34] . For all binary or categorical variables , if CMDs shared the same value/category then the attribute indicator was equal to one . For the number of years settled in the village , CMDs were coded as similar if their years of settlement were within +5/-5 . For years as CMD , age , and home quality score variables , CMDs were classified as similar if their values were within +3/-3 . In addition to the CMD similarity indicators , we accounted for variation in village and network size [4 , 27] . The natural log of total homes in the village and the natural log of the average CMD degree ( total friendship ties for each CMD ) were calculated . Statistical analyses were completed at the village level and conducted in R v3 . 2 . 3 and Stata v13 . 1 . With a limited number of village observations and no previous work on CMD similarity , we employed an unsupervised approach . Leave-one-out-cross-validation ( LOOCV ) with least absolute shrinkage and selection operators ( LASSO ) [35 , 36] were used to select the predictors of the division of labour and village treatment rates . This approach is a commonly used method for dimension reduction in statistical analyses . LOOCV LASSO was run with simple ordinary least squares ( OLS ) [35 , 36] . For the selection of predictors for the division of labour , all 16 CMD variables as well as the village and network sizes described in the previous section were candidates . In addition to these 18 variables , the division of labour was included for the selection of predictors of village treatment rates . The predictors that were selected through LOOCV LASSO were then entered in OLS regressions with robust standard errors [37] . To test for potential endogeneity of the division of labour and village treatment rates , i . e . an incorrectly specified direction of association where village treatment rates may determine the division of labour or more generally an association of the two outcome equations through the error terms , a Durbin-Wu-Hausman test was conducted and seemingly unrelated regressions were run [38] . For the Durbin-Wu-Hausman test , no evidence was found to indicate that the two outcome equations were correlated ( F-stat = 2 . 73 , p-value = 0 . 111 for individual-level outcomes and F-stat = 0 . 74 , p-value = 0 . 399 for household-level outcomes ) . Similarly , no support for simultaneous equations was found from the seemingly unrelated regressions ( Chi2 = 0 . 498 , p-value = 0 . 481 for individual-level outcomes , and Chi2 = 0 . 602 , p-value = 0 . 438 for household-level outcomes ) . Thus , separate OLS regressions were run . LOOCV was run for both the selection of the predictors and for the final models . For the statistical analyses , three villages ( IDs 20 , 24 , 30 ) were not included because one CMD in each of those villages was missing network information . Thus , 56 CMDs from 28 villages had complete data . For the target population , two households were excluded due to having no eligible individuals for MDA or missing information regarding treatment . In total , 1 , 118 households and 6 , 148 eligible individuals within those households were observed .
In 28 villages , 47 . 87% ( 2943/6148 ) and 24 . 77% ( 1523/6148 ) of eligible individuals were treated with at least one drug and all three drugs , respectively . Only 56 . 71% ( 634/1118 ) of households had at least one eligible person treated with praziquantel , albendazole , or ivermectin . Treatment rates achieved by individual CMDs ranged from 0–84 . 25% ( std . dev . 22 . 09% ) and 0–87 . 50% ( std . dev . 23 . 49% ) for individuals and households , respectively ( Obs . 56 ) . Village treatment rates also varied widely within the study area . The percentage of eligible individuals treated in each village varied from 2 . 79–89 . 74% ( std . dev . 26 . 15% ) . Similarly , the percentage of households with at least one eligible person treated ranged from 7 . 50–97 . 50% ( std . dev . 25 . 10% ) . WHO treatment targets for each disease were not necessarily met when village treatment rates ( as measured here ) met WHO-recommended levels . For schistosomiasis and hookworm , five communities had village treatment rates of at least 75% ( Village IDs 1 , 14 , 18 , 21 , 31 ) and three communities had praziquantel and albendazole treatment rates of at least 75% ( Village IDs 1 , 18 , 31 ) . For lymphatic filariasis , 10 communities ( Village IDs 1 , 2 , 12 , 14 , 17 , 18 , 21 , 25 , 26 , 31 ) had village treatment rates of at least 65% yet only three communities had both albendazole and ivermectin treatment rates of at least 65% ( Village IDs 1 , 18 , 31 ) . Therefore , only 10 . 71% ( 3/28 ) of villages met WHO-recommended treatment targets for each disease . The division of labour across villages was highly unequal . The average division of labour for both the percentage of individuals and households treated was 0 . 327 ( std . dev . 0 . 277 for individuals and 0 . 285 for households ) . In other words , when two CMDs were compared within the same village , one CMD treated on average only one third as many people or households as their counterpart . Wide variation in the division of labour was observed . The divisions of labour for individual and household level outcomes ranged from perfectly unequal to nearly a perfectly equal 50–50 split ( range 0–0 . 967 , std . dev . 0 . 278 for individuals; and 0–0 . 957 , std . dev . 0 . 285 , for households ) . There were six villages with one CMD who treated no one . Despite the wide variation in the division of labour , high inequality between CMDs was most common . For example , for the percentage of individuals treated , 75% of villages had a division of labour between CMDs where one CMD treated twice as many people as the other CMD . The unequal division of labour was not due to both CMDs treating few people , i . e . one CMD treating marginally more individuals ( e . g . 10% versus 5% ) . The average absolute difference in the percentage of eligible individuals treated between CMDs within a village was 30 . 23% ( std . dev . 20 . 85% ) . A summary of personal attributes of CMDs , similarities between CMDs , and village sizes are presented in Tables 1–3 . Within the study area , there was no single characteristic that was shared by all CMDs . When two CMDs within the same village were compared by personal/observable characteristics , CMDs were most often similar with respect to preventative health behaviours and socioeconomic status . A large majority ( 81 . 14% ) of villages had two CMDs with the same ownership status of private home latrines; in all but one of these villages ( 22/23 ) both CMDs owned a private home latrine . In 75 . 00% of villages , both CMDs had the same formal status ( where 4/21 villages had both CMDs with formal status ) and similar home quality scores . Approximately 50% or more of villages had two CMDs who differed with respect to gender , educational attainment , membership in the majority tribe , and the number of years settled in the village . There were 25 . 00% ( 7/28 ) and 17 . 86% ( 5/28 ) of villages , respectively , where CMDs were either both females or both males . With respect to MDA-related characteristics , only 53 . 57% of villages had CMDs who were selected through the same means and only 42 . 86% of villages had two CMDs who had volunteered for MDA for a similar number of years . CMDs selected in the same manner were not necessarily selected through community-wide meetings . Only 40 . 00% ( 6/15 ) of villages who selected both CMDs in the same manner did so through community-wide meetings whilst the remainder of those villages had both CMDs selected by a member of the local council ( village government ) . Network similarity between CMDs is illustrated in Fig 1 and summarized in Table 3 . On average , each CMD had 8 . 43 friends . CMDs were close friends in only 5 of 28 villages ( 17 . 86% ) . Yet , CMDs did not belong to distinct friendship groups . Amongst the total number of friends named by both CMDs , an average of 84 . 40% of friends were shared between the two CMDs . Moreover , in every village , each CMD was no further than two steps apart , i . e . each CMD had at least one common friend . Over an average of seven friends had to be removed from the network to completely disconnect CMDs . The cohesiveness of CMDs was maintained through indirect ties because the friends of CMDs also were well connected . When both CMDs were removed from the network , density remained high amongst the friends . Here density is defined as the proportion of ties that exist amongst the maximum possible number of ties . The density amongst friends of CMDs was on average 0 . 784 ( std . dev . 0 . 135 , range 0 . 526–1 ) . There was only one village ( ID 28 ) where the removal of CMDs resulted in one friend becoming isolated from the network . Table 4 presents the determinants of the percentage of individuals treated at the village level . Neither CMD similarity nor the sizes of the friendship networks and villages were associated with village treatment rates . Only one variable—the division of labour—was selected through LOOCV LASSO as a potential predictor of village treatment rates . The division of labour was positively correlated ( p-value = 0 . 008 ) with village treatment rates . For the percentage of eligible individuals treated at the village level , there was a remarkable absolute difference of 39 . 69% between the treatment rates of CMDs with a perfect division of labour compared to CMDs with a perfectly unequal division of labour . The predicted village treatment rates against the range of values for the division of labour , i . e . the marginal effects of increasing equity in the division of labour , are shown in Fig 2 . When the percentage of households treated was examined , the results for the division of labour were upheld despite LOOCV LASSO selecting two predictors in addition to the division of labour ( Table 5 ) . The discrepancies amongst village treatment rates at the household level for CMDs with and without equal divisions of labour were as large as 50 . 51% ( p-value = 0 . 006 ) . The additional predictors of the percentage of households treated included the similarity in the number of years spent as CMD and a village-level variable of the total homes . The total number of homes in the village was negatively related to village treatment rates at the household level ( p-value = 0 . 001 ) , although this effect was modest . A 10% increase in the total number of homes in a village was estimated to decrease the percentage of households treated by only 1 . 86% . Tables 6 and 7 present the predictors of the division of labour between CMDs . For both individual and household level outcomes , the only predictor selected by LOOCV LASSO was the friendship between CMDs . A direct tie between CMDs was predicted to substantially increase the division of labour by 0 . 444 and 0 . 393 , respectively , at the individual or household level when compared to CMDs without a direct tie . Hence , workload equity between CMDs was estimated to increase by just under twofold . Notably , the friendship between CMDs only predicted the division of labour and was not associated with village-level treatment rates ( Tables 4 and 5 , and ρ = 0 . 144 , p-value = 0 . 464 at the individual level; and ρ = 0 . 098 , p-value = 0 . 619 at the household level ) . There were no missed effects of other characteristics of CMD similarity influencing the division of labour due to indirectly affecting the presence of a direct tie ( Table 8 ) .
In the context of rapidly expanding community-based MDA and WHO disease-specific goals of elimination [39–41] , there is an urgent need to increase the effectiveness of CMDs . In accord with previous work [4] , here we showed that treatment rates varied widely across villages in rural Uganda . The percentage of eligible individuals treated varied from 2 . 79–89 . 74% across 28 villages and worsened from an average of 59 . 05% per village in 2013 [4] to 47 . 77% in 2016 ( our study ) . To gain a better understanding of how CMDs work and cooperate , we examined the influences of CMD similarity on the division of labour and village treatment rates . The division of labour was highly unequal between CMDs , with one CMD treating on average only one third as many eligible individuals as the other CMD within the same village . The equality in the division of labour was positively associated with overall village treatment rates . The estimated difference between a perfectly equal ( a 50–50 split of individuals treated ) and unequal ( one CMD treating no one ) division of labour was remarkable . An equal division of labour was associated with the treatment of an additional 39 . 69% more of the eligible population or 50 . 51% more households approached when compared to an unequal division of labour . Considering that WHO-recommended treatment rates for effective morbidity control are 65% for lymphatic filariasis and 75% for schistosomiasis or STHs , our results suggest that treatment targets are not achievable without an equitable distribution of labour between CMDs . These findings also highlight that a discussion of CMD capacity constraints [14] is unfounded within villages where one CMD treats few or no people . It is unknown whether adding more volunteer CMDs would facilitate MDA or simply contribute to an even more unequal division of labour and idle labour . Efforts to reduce CMD attrition rates [42] may be counterproductive if the result is that poorly performing CMDs are retained [21] . National MDA programmes should focus on the quality rather than the numbers of CMDs . Characteristics that may be used to improve the selection of CMDs can be gleaned from hardworking CMDs—defined here as CMDs who treated many individuals . In our study area , hardworking CMDs have been shown to be individuals who engage in good preventative health behaviours , belong to high-risk groups for endemic NTDs ( e . g . fishermen for schistosomiasis ) , are male , and have supportive friendship networks [4 , 21] . Ultimately , the selection/replacement criteria for CMDs should align with factors that are of interest to NTD-endemic communities . The only determinant of the division of labour was a direct tie , i . e . close friendship between CMDs . Friendship was neither indicative of contact between CMDs nor of who knew whom . CMDs belonged to villages that were small with respect to population size and geographical spread . The variation in village size ( total homes and total population ) was uncorrelated to the presence of a direct tie . This result might suggest that village size also was unrelated to the frequency of contact between CMDs , assuming that the presence of a direct tie was partially determined by the frequency of in-person contact . CMDs knew each other well; they were both selected by individuals within the same village , trained together annually for MDA , and shared many common friends . Hence , CMDs had similar social networks . Yet , few villages ( 5/28 ) had CMDs who themselves were friends . Therefore , improving the division of labour is not as trivial as introducing two CMDs . Communication between CMDs was essential to improving the equity in the distribution of work related to MDA [28] . Here we only examined the ties between CMDs within the same village . Future research is needed to understand how CMDs are connected across villages . National MDA programmes do not hold regular meetings to bring together CMDs apart from the annual training . Encouraging CMDs both within and perhaps across villages to meet more frequently—maybe monthly—to compare and submit data , collect additional MDA supplies ( registers , medicines , etc . ) , and simply to socialize may lead to new friendship connections that could facilitate communication between CMDs . CMD friendship was only associated with village treatment rates indirectly , i . e . through the division of labour . This finding accords with previous research that tracked MDA in our study area [4] and found that a friendship tie between CMDs was not directly correlated with village treatment rates . Surprisingly , similar CMDs were not more likely to be friends than CMDs with different attributes . Conventional wisdom on social networks [23] suggests that direct ties exist between individuals in part due to homophily , which is the tendency of individuals to connect with others most like themselves . However , here we showed that shared socioeconomic characteristics did not predict the presence of a direct tie between CMDs . The presence of a direct tie is one indicator of CMDs belonging to the same cluster within the broader village social network . Stifling of an intervention hypothetically could occur by trapping its spread ( information or uptake ) within a confined set of closely-knit , clustered individuals [43] . There was no support that the reach of MDA was stifled when two CMDs were within the same network cluster . In contrast , we found that implementing MDA with CMDs in the same network cluster indirectly was positively associated with village treatment rates by improving the division of labour . It remains an open question as to whether negative ties ( active dislike ) exist between CMDs who are not close friends . The positive effect of direct ties on the division of labour suggests that CMDs are complements rather than substitutes with respect to their labour input . Complementary inputs result in additive or multiplicative effects on cooperation and equality between CMDs whereas substitutes might suggest a crowding out effect of one CMD working harder thereby causing the other CMD to work less . We cannot rule out that CMDs planned their division of labour , perhaps trading off efforts where one CMD agrees to take on the majority of responsibility for MDA this year whilst the other CMD resumes duties in the following year . If CMDs negotiated the division of labour then we would expect a direct tie , which represents a channel of communication between CMDs , to be negatively related to village treatment rates . Yet , no such association was observed . There remains the possibility of a motivational imbalance between CMDs [19] that is unrelated to CMD similarity . Regardless of the reason for a highly unequal division of labour , this inequality undermined community-based MDA and was correlated with low village treatment rates . Our definition of the division of labour captured a number of features relevant to our study area and similar contexts . There were two CMDs . MDA was conducted in rural , small villages that did not have further geographical sub-units . There were no MDA programme stipulations for if/how labour between CMDs should be divided . Importantly , our measurement of the division of labour was outcome-based , i . e . dependent on the number of people to be treated . This definition is in light of WHO treatment targets and thus , from the perspective of the agency rather than from the view of the community . To investigate the division of labour in other MDA contexts , there is a need to develop a typology of the division of labour that includes a range of options for dividing the population as well as methods for evaluating labour/effort from MDA volunteers . A provisional typology of the division of labour might include , as studied here , the number of people to be treated or instead could be focused on tasks , community geography , or social groups . CMDs performed MDA tasks of registering households , sensitizing individuals , and mobilizing the community whilst administering treatment instead of before treatment as instructed by the national programmes [4 , 13 , 20] . Thus , in our study area , there was no distinction between tasks and treatment . However , it remains an open question as to if creating a distinction between tasks and treatment , and dividing by task increases CMD productivity . Community participation is needed to understand the value of each MDA task and to identify tasks that are conducted by CMDs and the wider community but not recognized by national MDA programmes . The identified tasks might be enumerated in a form that is used to monitor CMDs by both national programmes and NTD-endemic communities . Beyond MDA , CMDs are members of the village health team , which is responsible for a wide range of primary health care tasks including bed net distribution , the management of childhood illnesses , and individual referrals to government health services [44] . There is a need to better understand the process in which CMDs divide responsibilities for MDA tasks versus other primary health care activities . In our study , we did not have information on whether CMDs who treated no one during MDA were actively engaged in other primary health care interventions . Although village size did not affect the division of labour in our study area [4] , spatial dimensions , population size , and local ecology will need to be considered in urban or peri-urban settings . A system could be devised using local knowledge of ecological or administrative divisions to assign the population to be treated . Local knowledge is critical , as—anecdotally in our study district—government records of villages did not accord with existing villages . This misalignment was not due to inaccuracies in reporting but rather the quickly change shape of local boundaries as determined by the communities themselves . Potential social groups that could be used to divide labour include kinships , friendship groups , gender , occupations , burial societies , saving cooperatives , tribes , clans , or religious associations . In our study area , both kinships [16] and friendship networks [4 , 13] have been shown to affect treatment rates , whereas gender has not been a barrier to who treats whom [21] . Women and men are just as likely to treat individuals from another gender when compared to how many people they treat of the same gender [21] . We found no support that similarities/differences in demographic or socioeconomic characteristics affected the division of labour or village treatment rates . This result was surprising in that social imbalances did not lead to a highly unequal division of labour , i . e . there was no evidence that high status CMDs were free riding off the efforts of lower status CMDs [13] . Diversity between CMDs did not translate into higher village treatment rates [16] . Villages with CMDs who represented more social groups , assuming CMD attributes were indicative of social group memberships , did not achieve higher village treatment rates than villages with CMDs from the same social groups . Although CMD diversity did not translate into higher village treatment rates , future work should examine whether CMD diversity affects the treatment rates of marginalized , underrepresented populations [20] . The usefulness of social divisions for the treatment of marginalized populations will depend foremost on the homogeneity of the communities to be treated and personal attributes of CMDs . Homogenous populations will not have natural social divisions . Additional research is needed to 1 ) develop indicators for assessing the variation of homogeneity between NTD-endemic communities , 2 ) measure if/how homogeneity impacts treatment rates , 3 ) develop methods to ‘match’ CMDs with individuals to be treated using a wide range of socioeconomic characteristics , and 4 ) analyze how aligning CMDs with similar social groups affects their treatment rates . Developing a typology for dividing labour is only the first step in fully evaluating CMD equity . A limitation of our study is that we did not directly assess the effort expended by CMDs . For example , we require an understanding of how difficult it is to complete a particular task or traverse different terrains . The number of hours spent per task has been enumerated elsewhere from the perception of CMDs [14] . If time constraints were determined by occupation or familial demands ( related to age and gender ) then we would expect differences in CMD attribute similarity to capture differences in CMD time constraints . In that case , our results might suggest that differences in time constraints between CMDs did not explain the division of labour . Additional studies are needed to assess differences in time spent on MDA versus other primary health care tasks and the effect on the division of labour and MDA treatment rates . All CMDs received the same remuneration for attending MDA training and no other payments from MDA programmes . However , CMDs who engage in other primary health care tasks may be remunerated and these additional sources of income might affect CMD effort during MDA . To identify indicators in addition to time and remuneration for evaluating effort , community expectations for CMDs also should be examined . Improving the division of labour between CMDs is a social problem . The Declaration of Alma-Ata in 1978 emphasized the need to account for the social determinants of treatment and disease [45] . Our study emphasizes the importance of the message of Alma-Ata for community-based MDA . Social relations between CMDs should be improved and , to do so , community involvement in MDA must be increased . The shift in terminology from ‘community-directed’ MDA , which originated in the 1970s , to the now widely used ‘community-based’ MDA suggests a reduction in the role of NTD-endemic communities [46] . Reducing the role of communities from actively designing/monitoring treatment programmes ( community-directed ) to nominating CMDs ( community-based ) risks turning communities into passive actors during MDA . Even a slight deviation from community-based MDA towards community-directed MDA , whereby CMDs involve their close friends in monitoring or disseminating information , has been associated with increased treatment rates [21] . To move from passive to active community involvement , there is a need to reimagine the concept of equity as posed in this study . Here we examined the equity between CMDs in light of their ability to meet programmatic goals ( treatment targets ) . Equity instead could be explored by examining to what extent CMDs and communities , or communities and the national programme are equal partners in designing and running MDA . An ultimate goal for MDA might be the integration into local health systems to empower communities to manage their own health [46] . To improve the effectiveness of community-based MDA , national programmes should facilitate the equal division of labour between CMDs . The similarity of personal attributes of CMDs was unrelated to the best combination of CMDs with the most equal division of labour and , in turn , highest treatment rates . National MDA programmes may instead aim to foster friendships between CMDs or to encourage the selection of CMDs who already are close friends in order to promote open communication between CMDs . Alternatively , guidelines might be trialed where an equal division of labour is encouraged between CMDs; treatment rates may be recorded for each CMD and monitored to identify inactive CMDs . Future interventions also might seek to explore other avenues to increase community involvement in the design of MDA . A more equal division of labour between CMDs may assist NTD programmes with achieving treatment targets .
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Community-based mass drug administration ( MDA ) uses volunteers within at-risk communities to distribute preventive chemotherapies en masse for neglected tropical diseases . Treatment rates achieved by community medicine distributors ( CMDs ) vary widely and can undermine morbidity control . We studied routine community-based MDA in 28 villages near Lake Victoria in Uganda . There were two CMDs per village who were tasked with moving from home-to-home to administer praziquantel , albendazole , and ivermectin for schistosomiasis , lymphatic filariasis , and soil-transmitted helminths . We observed treatment outcomes for 6 , 148 eligible individuals aged 1+ years . Here we identified the best combination of CMD characteristics for achieving high village-level treatment rates . We found that a more equal division of labour ( e . g . 50–50 split between how many people each CMD treated ) was associated with higher treatment rates when compared to CMDs with an unequal division of labour ( e . g . one CMD treating no one ) . CMDs who were friends were more likely to have a division of labour that was nearly twofold more equal than CMDs who were not friends . The similarity of CMDs with respect to network , demographic , or socioeconomic characteristics did not influence village treatment rates . National programmes should explore interventions that seek to facilitate communication , friendship , and equal partnership between CMDs .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"behavioral",
"and",
"social",
"aspects",
"of",
"health",
"sociology",
"tropical",
"diseases",
"social",
"sciences",
"anthropology",
"parasitic",
"diseases",
"filariasis",
"pharmaceutics",
"global",
"health",
"neglected",
"tropical",
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"cultural",
"anthropology",
"lymphatic",
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"religion",
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"helminth",
"infections",
"schistosomiasis",
"drug",
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"soil-transmitted",
"helminthiases"
] |
2019
|
The division of labour between community medicine distributors influences the reach of mass drug administration: A cross-sectional study in rural Uganda
|
The improved characterisation of risk factors for rheumatoid arthritis ( RA ) suggests they could be combined to identify individuals at increased disease risks in whom preventive strategies may be evaluated . We aimed to develop an RA prediction model capable of generating clinically relevant predictive data and to determine if it better predicted younger onset RA ( YORA ) . Our novel modelling approach combined odds ratios for 15 four-digit/10 two-digit HLA-DRB1 alleles , 31 single nucleotide polymorphisms ( SNPs ) and ever-smoking status in males to determine risk using computer simulation and confidence interval based risk categorisation . Only males were evaluated in our models incorporating smoking as ever-smoking is a significant risk factor for RA in men but not women . We developed multiple models to evaluate each risk factor's impact on prediction . Each model's ability to discriminate anti-citrullinated protein antibody ( ACPA ) -positive RA from controls was evaluated in two cohorts: Wellcome Trust Case Control Consortium ( WTCCC: 1 , 516 cases; 1 , 647 controls ) ; UK RA Genetics Group Consortium ( UKRAGG: 2 , 623 cases; 1 , 500 controls ) . HLA and smoking provided strongest prediction with good discrimination evidenced by an HLA-smoking model area under the curve ( AUC ) value of 0 . 813 in both WTCCC and UKRAGG . SNPs provided minimal prediction ( AUC 0 . 660 WTCCC/0 . 617 UKRAGG ) . Whilst high individual risks were identified , with some cases having estimated lifetime risks of 86% , only a minority overall had substantially increased odds for RA . High risks from the HLA model were associated with YORA ( P<0 . 0001 ) ; ever-smoking associated with older onset disease . This latter finding suggests smoking's impact on RA risk manifests later in life . Our modelling demonstrates that combining risk factors provides clinically informative RA prediction; additionally HLA and smoking status can be used to predict the risk of younger and older onset RA , respectively .
Rheumatoid arthritis ( RA ) is a common chronic inflammatory disorder . It results in substantial morbidity and disability alongside high medical and societal costs [1] , [2] . There is therefore growing interest in preventing its development . Such prevention requires an ability to reliably predict who will develop RA . Advances in characterising genetic and environmental risk factors for RA together with developments in modelling methodology make predicting its development a realistic possibility . RA is a clinical syndrome spanning multiple subsets [3] . The commonest subdivision is by the presence or absence of rheumatoid factor ( RF ) /anti-citrullinated protein antibodies ( ACPA ) , termed seropositive and seronegative RA respectively . Risk factor evaluation has mainly focussed on seropositive RA with nearly half its genetic architecture known . HLA-DRB1 alleles , in particular those encoding the shared epitope , dominate genetic risk accounting for approximately 36% of heritability [4]; 45 non-HLA variants explain approximately 15% of heritability [4] . Smoking is the main environmental risk factor [5]; it predisposes to seropositive RA and has a synergistic relationship with the shared epitope [6] , [7] . Although single factors do not provide sufficient risk stratification , combining multiple factors within a prediction model may identify clinically relevant high- and low-risk groups . The large risks conferred by HLA make such modelling an attractive prospect in RA despite limited success in other complex disorders [8]–[10] . RA develops over many years prior to clinical presentation [11] . Initially , individuals with genetic susceptibility variants are exposed to environmental risks; some may develop autoantibodies ( RF/ACPA ) [12] . A proportion will subsequently develop arthralgia , which may progress to an unclassified arthritis followed by a fully expressed RA phenotype . Pilot studies in unclassified arthritis indicate that secondary prevention may be possible with corticosteroids [13] , [14] , methotrexate [15] and biologics [16] attenuating the progression to RA . Although preventive treatments may be more effective before immune dysregulation and symptoms develop , primary prevention is not currently possible as no reliable method exists to identify asymptomatic high-risk individuals . Prevention is likely to have a larger impact in younger onset RA ( YORA ) due to the increased health costs associated with a longer disease duration [17] . Genetic susceptibility factors may influence RA's age of onset with HLA-DRB1*04 alleles [18]–[21] and multiple single nucleotide polymorphisms ( SNPs ) such as those tagging VEGFA [22] , RANKL [19] , [23] , MMP1-3 [22] and PTPN22 [24] , [25] loci associating with YORA . One group has published two reports outlining predictive models for RA . Their models , built using 8 HLA alleles , 14–31 SNPs and clinical factors , generated an aggregate weighted genetic risk score ( wGRS ) formed from the product of individual-locus odds ratios ( ORs ) [26] , [27] . They were reasonably accurate at determining disease status in approximately 1 , 200 cases and 1 , 200 controls , with a maximal area under the curve ( AUC ) of 0 . 752 . They also demonstrated a better ability to predict erosive RA ( a more severe phenotype ) . However , only a minority of the studied populations had significantly elevated risks for RA . We report an alternative modelling approach to predicting RA . Our novel modelling method uses computer simulation to categorise risk profiles; our models also incorporate a larger number of HLA risk variants . The risk factors included in our modelling comprise 15 four-digit/10 two-digit HLA-DRB1 alleles , 31 SNPs and male ever-smoking status ( as ever-smoking is a significant risk for RA in males only ) . We applied our models to two large cohorts of European ancestry: the Wellcome Trust Case Control Consortium ( WTCCC ) and the UK RA Genetics Group ( UKRAGG ) Consortium . Our primary aim was to determine if our approach would generate clinically relevant predictive values . Our secondary aim was to determine if our modelling better identified YORA . We demonstrate that clinically informative RA risk prediction is possible and that the risk of younger and older onset RA can be predicted using information on HLA and smoking status , respectively .
All participants in WTCCC and UKRAGG were recruited after providing informed consent . UKRAGG was approved by the North West Multi-Centre Research Ethics Committee ( MREC 99/8/84 ) . Authors gained written permission and approval from WTCCC to undertake this work in the publically available WTCCC1 collections . The WTCCC dataset contains SNP data on 1 , 999 RA cases and 3 , 004 controls [28] . Controls were obtained from the 1958 British Birth Cohort and UK Blood Services . Genotyping was performed on the Affymetrix GeneChip 500k Mapping Array Set . Quality control ( QC ) procedures were undertaken excluding individuals with <97% SNP call rates , high heterozygosity , non-European ancestry or relatedness , discordance between genotype and phenotype data and duplicate samples . In the post-QC dataset information was available on 490 , 031 SNP markers; the total genotyping rate was 1 . 00 . Two- or four-digit resolution HLA-DRB1 tissue typing data were available on 1 , 837 cases and 1 , 647 controls . The UKRAGG dataset contains SNP data on 5 , 024 RA cases and 4 , 281 controls from 6 UK centres [29] . Genotyping was performed using the Sequenom platform . Four hundred and four SNPs were genotyped over 8 staggered plexes; for each plex separate QC was undertaken excluding individuals and SNPs with <90% data present . In the post-QC dataset total genotyping rates were 0 . 73 owing to systematic differences in samples run on each plex . Two- or four-digit resolution HLA-DRB1 tissue typing data were available on 3 , 420 cases and 1 , 500 controls . Both datasets contained cases fulfilling the 1987 ACR classification criteria for RA [30] . HLA-DRB1 tissue typing was undertaken ( at two-digit or four-digit resolution ) at individual centres , using commercially available semiautomated polymerase chain reaction-sequence-specific oligonucleotide probe ( PCR-SSOP ) typing techniques ( or research assays based on PCR-SSOP linear array technology ) [29] . Two-digit typing includes the allele group ( Field 1 ) only; four-digit typing includes both the allele group and the allele subtype encoding a specific HLA protein ( Field 2 ) ( http://hla . alleles . org/nomenclature/naming . html ) . We undertook prediction modelling in seropositive cases and controls with HLA-DRB1 tissue typing data available with or without additional SNP and smoking data ( as most replicated risk loci are for seropositive RA and genetic risk is dominated by HLA ) [4] , [31] . The final cohorts comprised 1 , 516 cases and 1 , 647 controls from WTCCC and 2 , 623 cases and 1 , 500 controls from UKRAGG ( Table 1 ) . Our modelling was performed within the R package , REGENT ( Risk Estimation for Genetic and Environmental Traits ) , developed within our unit . This program incorporates published gene-environment risk factor and disease statistics to categorise risk using a confidence interval ( CI ) -based approach within a simulated population . The methodology underlying REGENT has previously been described in detail [32] , [33] . Genetic and environmental risk factors for input into REGENT are selected from the literature . Genetic risk factors require allelic ORs , allele frequencies , and sample sizes from relevant studies , in order to estimate precision . Environmental risk factors require ORs , standard errors and the proportion of the population exposed to the risk factor . Data on these risk factors are entered into REGENT as summary statistic input files , which are processed in two stages: the first develops the prediction model and the second runs the prediction model in real life data . In the first stage REGENT simulates a population-distribution of disease risk . Risk profiles are simulated based on the frequency of each risk factor in the general population . Summary ORs for each risk profile are generated through combining the ORs for each genetic and environmental risk factor in a multiplicative model that assumes risk factor independence . CIs are generated using information on the variability of genetic risk factors ( derived from the sample size of the risk variant discovery cohort ) and environmental risk factors ( standard error of the effect size ) . Each simulated risk profile's OR is initially calculated relative to a profile with no risk factors present; these are subsequently adjusted to ensure correct disease prevalence in the population , assigning a risk profile with a mean OR as having a baseline risk of 1 . 0 . CIs are used to classify risk profiles into four risk categories ( reduced , average , elevated and high-risk ) . Starting with the risk profile of baseline risk ( OR = 1 . 0 ) , any risk profile whose CI overlaps with this baseline CI is classified as being of average-risk ( as this profile is not statistically different from baseline ) . Any risk profile whose CI resides fully below the baseline CI is classified as reduced-risk . Profiles with CIs above the baseline CI are classified as elevated-risk . Furthermore , a high-risk group is determined by profiles whose CIs reside completely above the CI of the first risk profile classified as elevated-risk . An example of how this process is undertaken in a simplified model using 3 SNPs is provided in Figure S1 . In the second stage REGENT applies this simulated population profile to individual level data . Genotypes and environmental risk factor exposure data on each individual in the dataset of interest ( WTCCC and UKRAGG ) are entered into REGENT , which generates two measures of disease risk . Firstly , each individual's summary OR ( 95% CI ) for RA is calculated ( relative to the baseline individual with an OR of 1 . 0 ) ; as with the simulated population , risk factors are combined in a multiplicative model . This summary OR informs the individual of their risk of developing RA . Secondly , each individual is assigned a risk category for RA . This is undertaken through comparing the CI of each individual's summary OR to those of the simulated risk distribution in the same manner as described in stage 1 . This risk category informs an individual whether they are at an increased or reduced risk of disease , relative to the average person in the general population . Two-digit or four-digit HLA-DRB1 tissue typing data were available in all evaluated individuals . In WTCCC 1 , 342 seropositive cases , 966 ACPA-positive cases and 1 , 126 controls had four-digit resolution data available on both alleles; 29 seropositive cases , 14 ACPA-positive cases and 159 controls had two-digit resolution data available on both alleles; 145 seropositive cases , 81 ACPA-positive cases and 362 controls had mixed-digit resolution data ( one HLA-DRB1 allele known at four-digit and the other at two-digit resolution ) available . In UKRAGG 1 , 534 seropositive cases , 1 , 108 ACPA-positive cases and 735 controls had four-digit resolution data available on both alleles; 312 seropositive cases , 66 ACPA-positive cases and 205 controls had two-digit resolution data available on both alleles; 777 seropositive cases , 334 ACPA-positive cases and 560 controls had mixed-digit resolution data available . We excluded 4 SNPs attaining PGWAS in the meta-analysis for the following reasons: 1 ( rs11676922 ) was in high linkage disequilibrium ( r2>0 . 9; HapMap release 22 CEU population panel ) [39] with another ( rs10865035 ) – in this case the latter SNP was included due to a previous association with RA – and 3 SNPs/proxy SNPs were unavailable ( rs10488631 , rs6859219 and rs934734 in UKRAGG; rs6822844 , rs874040 and rs951005 in WTCCC ) . Eleven and two proxy SNPs were used in WTCCC and UKRAGG respectively ( Table S2 ) [39] . Data on ever-smoking status were available in 287 male cases and 739 male controls in WTCCC and 529 male cases and 322 male controls in UKRAGG . To examine the contribution of each gene-environment component to prediction we constructed several models . These comprised a SNP model ( with 31 SNPs ) , an HLA model ( 10 two-digit and 15 four-digit HLA-DRB1 alleles ) , an HLA-SNP model ( combining HLA and SNP model components ) , an HLA-smoking model ( combining HLA-DRB1 alleles with ever-smoking status ) and an HLA-SNP-smoking model ( combining HLA-DRB1 alleles , 28 SNPs and ever-smoking status ) . Only the 28 SNPs present in both WTCCC and UKRAGG were incorporated in the last model . The latter two models , which included smoking , were evaluated in males only . The decision to combine two-digit and four-digit HLA-DRB1 alleles in the HLA model was undertaken to avoid removing the substantial number of individuals with mixed resolution typing . Preliminary analyses confirmed the validity of this approach with no significant differences seen in the discriminative abilities of HLA models incorporating ( 1 ) two-digit alleles only; ( 2 ) four-digit alleles only and ( 3 ) a mixed resolution of alleles ( Table S3 ) . Within our mixed resolution modelling the risks for each HLA allele were included only once per individual at the highest resolution at which they were known . Only individuals with available data on relevant risk factors were included in models incorporating those risk factors . Therefore only males with available smoking data were included in the HLA-smoking and HLA-SNP-smoking models . Similarly only individuals with data available on the modelled SNPs could be included in the HLA-SNP and HLA-SNP-smoking models . Owing to missing data the number of individuals evaluated in each prediction model fell as more risk factors were included ( Figure 1 ) . We undertook modelling separately for seropositive ( RF and/or ACPA present ) RA and ACPA-positive RA since HLA-DRB1 allelic ORs were obtained from a meta-analysis evaluating ACPA-positive RA [35] , and the shared epitope alleles , non-HLA SNPs and smoking predominantly associate with ACPA-positive disease [4] , [48]–[50] . We therefore hypothesised our modelling would perform better for ACPA-positive RA . As this was confirmed in the risk categorisation results we restricted further analyses ( AUC and lifetime risk calculations , examining modelling associations with age of RA onset ) to ACPA-positive RA .
In WTCCC the HLA model summary OR score was the only significant predictor of age of RA onset ( Table 4 ) . The hazard ratio ( HR ) was 1 . 034 ( P<0 . 0001 ) , which indicated that the hazard ( the rate at which RA occurred ) was greater in individuals with higher HLA derived ORs than those with lower ORs . Therefore a higher HLA model generated risk score associated with RA occurring at a faster rate and thus YORA . Conversely ever-smoking was associated with older onset RA: the HR of 0 . 902 indicated a smaller hazard ( RA occurred at a slower rate ) in ever-smokers compared with never-smokers , although this was not significant ( P = 0 . 1301 ) . In UKRAGG the HLA model summary OR score , gender and smoking status were significant independent predictors of age of onset . An increasing HLA summary OR score associated with YORA ( P = 0 . 0003 , HR 1 . 026 ) ; ever-smoking ( P = 0 . 0041 , HR 0 . 848 ) and male gender ( P = 0 . 0465 , HR 0 . 885 ) associated with older onset RA . We considered that the non-significant relationship between smoking and age of onset in WTCCC reflected a limited sample size with our power to detect a 0 . 88 HR in the 962 WTCCC cases approximately 51% compared with 65% for the 1 , 361 UKRAGG cases . We therefore undertook a pooled analysis of both datasets ( incorporating an additional “study” variable to account for dataset median age of onset differences ) . This confirmed that HLA derived risk scores significantly associated with YORA ( P<0 . 0001 , HR 1 . 030 ) and ever-smoking significantly associated with older onset RA ( P = 0 . 0489 , HR 0 . 889 ) . Kaplan-Meier curves of age of onset stratified by HLA model risk categorisation further demonstrate the association of HLA risk profiles with YORA ( Figure 5 ) with cases classified high-risk having significantly younger onset ages compared to those classified reduced-risk . In WTCCC the difference in the median time to RA ( time point at which half the cases have developed RA ) was 3 years between those classed high- and reduced-risk ( Log-Rank = 11 . 43; P = 0 . 0007 ) . In UKRAGG a stronger association was seen ( Log-Rank = 27 . 33; P<0 . 0001 ) with a difference in median time to RA onset between risk groups of 6 years . Further stratification by ever-smoking status demonstrated a trend towards an older onset age in ever-smokers . In WTCCC the median time to onset difference between high-risk never-smokers and reduced-risk ever-smokers was 7 years ( Log-Rank = 14 . 42; P = 0 . 0024 ) ; a larger disparity was seen in UKRAGG with a difference of 12 years observed ( Log-Rank = 46 . 2505; P<0 . 0001 ) . Examining which four-digit resolution HLA-DRB1 alleles influenced onset age revealed significant associations between age of onset and *03:01 ( P = 0 . 0313 ) , *04:01 ( P = 0 . 0001 ) , *04:08 ( P = 0 . 0032 ) and *13:02 ( P = 0 . 0097 ) in WTCCC and *04:01 ( P<0 . 0001 ) and *04:04 ( P = 0 . 0243 ) in UKRAGG . Three of these alleles ( *04:01 , *04:04 and *04:08 ) are shared-epitope alleles .
We have demonstrated that predicting RA development is possible with our prediction models able to identify individuals with clinically relevant increased risks for seropositive RA . Our modelling indicates that most prediction is provided by HLA-DRB1 alleles and , to a lesser extent , smoking in males; non-HLA susceptibility SNPs provide only minor predictive benefits . These findings are consistent with the estimations of heritability variance conferred by different genetic components . We have also shown it is possible to predict the age of RA onset , using information on HLA and smoking to identify those at risk of younger and older onset RA , respectively . Whilst our novel modelling approach , which uses computer simulation-based categorisation alongside a greater number of HLA alleles , significantly improves upon the discriminative abilities of existing models [26] , [27] it remains unsuitable for population screening with only a minority at significantly increased lifetime risks for RA . Our approach provides some potential advantages over existing RA prediction modelling [26] , [27] . Firstly , by using a simulated population to generate risk profiles we do not require an entire population of real-life data to stratify risks . In contrast existing approaches categorise wGRS scores using their Gaussian distribution in control groups . Secondly , our CI-based approach considers the precision with which risk factor effect sizes are known when classifying risk; this prevents classifying people high-risk if their risk is imprecisely known . Thirdly , our models provide greater discrimination: the highest AUC for existing clinical-genetic models in discerning ACPA-positive RA from controls is 0 . 752; the highest AUC for our clinical-genetic model is 0 . 857 . SNPs provided only minor improvements in prediction , highlighting the limitations of genome-wide association study ( GWAS ) derived data in this field . Although GWAS-established SNPs have helped identify cellular pathways relevant to RA pathogenesis [51] their modest effect sizes limit their predictive utility . It has been proposed that the missing heritability of RA may reflect the involvement of rare variants of large effect sizes or structural variants [52] . Alternative genotyping technologies such as next-generation sequencing may identify these variants , although only loci with large effect sizes will substantially improve prediction modelling . Although individuals with clinically relevant increased lifetime risks ( such as 86% ) for RA were identified there was , overall , only a minority of individuals at a significantly elevated risk: 7% of ACPA-positive individuals had lifetime risks of 22% or more when evaluated using all available risk factors . Therefore despite high AUCs our modelling is unsuitable for population level screening . However , if its use was targeted to groups with a priori increased risks , such as first degree relatives of RA probands [53]–[55] , then a substantially greater proportion of very high-risk individuals might be identified . Individuals classified high-risk by our HLA model were more likely to develop RA at a younger age . This finding – mainly attributable to the *04:01 allele – is supported by existing literature . Hellier et al reported a higher frequency of *04 RA associated alleles in YORA ( present in 52% of 262 RA cases with onset age <60 ) compared with elderly onset RA ( present in 37% of 60 cases with onset age >60; P = 0 . 045 ) [18] . Similarly , Wu et al identified a significantly younger age of onset in Caucasian RA patients carrying shared epitope encoding *04 alleles ( P = 0 . 0003 ) [19] . Other studies report positive correlations between YORA and shared epitope alleles [25] , [56] . Our finding of ever-smoking associating with older onset RA is less established . It has only been examined in three relatively small studies , with contrasting outcomes: one study reported a significant relationship between smoking at disease onset and a younger onset age [57]; one reported a younger onset age in current vs . never-smokers ( although ex-smokers had older onset RA in comparison to both these groups ) [58]; the final study found no association [59] . Our findings – demonstrated in 2 , 323 individuals across two independent datasets – are biologically plausible . As risk genotypes are present from birth they can exert their effects on disease risk throughout an individual's lifetime; therefore possessing high-risk HLA-DRB1 alleles predisposes to RA at a younger age . In contrast the risk of RA increases as more cigarettes are smoked [60] , [61] and smokers are exposed to more cigarettes as they age; therefore smokers are more likely to develop RA as they get older because they have been exposed to more cigarettes and thus smoking associates with older onset RA . This logic also explains why ever-smoking associates with older onset RA in both men and women , with heavy smoking a risk factor for RA in both genders [5] . We were , however , unable to incorporate heavy smoking in our prediction modelling due to a paucity of data on smoking pack-years in WTCCC/UKRAGG . We incorporated many genetic risk factors in our modelling but included only one environmental risk factor , smoking . This reflects uncertainty regarding relevant environmental risks alongside limited environmental data within current genetic datasets . Although many environmental factors are linked to RA their associations are usually identified in case-control studies , which are subject to multiple biases , rather than cohort studies . Examples include alcohol consumption [36] , parity [62] , [63] and oral contraceptive pill use [64] . Better characterisation of environmental risks will enhance predictive modelling . Our modelling has several limitations . Firstly , WTCCC participants were included in the meta-analyses that we obtained our genetic risk loci data from; however WTCCC comprised only a proportion of the meta-analyses datasets ( 20% of the HLA meta-analysis; 29% of the SNP meta-analysis ) and our findings were independently replicated in UKRAGG . Secondly , missing data meant the number of individuals included in each model fell as more risk factors were included; this is particularly seen in models incorporating smoking . Thirdly , due to marked heterogeneity in published data on gene-gene/gene-environment interactions we assumed independence between these factors despite known interactions existing between the shared epitope alleles and PTPN22 and smoking [6] , [7] , [29] , [37] , [38] . Improving RA prediction requires better clarification of its genetic and environmental risk factors . Identifying risk factors with large effect sizes of known precision will most enhance prediction modelling . This could be facilitated through fine-mapping studies that better tag causal variants [65] alongside prospective cohort studies examining environmental risk factors in RA cases subdivided by ACPA status , with increasing evidence that risks differ between these serological subsets [36] , [66] . It is , however , unlikely that identifying such risk factors will substantially increase the proportion of individuals with clinically relevant high disease risks . We therefore consider that prediction modelling requires evaluation in a priori higher risk groups . In this context it may identify sufficient numbers of very high-risk individuals , facilitating a better understanding of pre-RA immunopathology and enabling the assessment of primary prevention strategies .
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Rheumatoid arthritis ( RA ) is a common , incurable disease with major individual and health service costs . Preventing its development is therefore an important goal . Being able to predict who will develop RA would allow researchers to look at ways to prevent it . Many factors have been found that increase someone's risk of RA . These are divided into genetic and environmental ( such as smoking ) factors . The risk of RA associated with each factor has previously been reported . Here , we demonstrate a method that combines these risk factors in a process called “prediction modelling” to estimate someone's lifetime risk of RA . We show that firstly , our prediction models can identify people with very high-risks of RA and secondly , they can be used to identify people at risk of developing RA at a younger age . Although these findings are an important first step towards preventing RA , as only a minority of people tested had substantially increased disease risks our models could not be used to screen the general population . Instead they need testing in people already at risk of RA such as relatives of affected patients . In this context they could identify enough numbers of high-risk people to allow preventive methods to be evaluated .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2013
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Predicting the Risk of Rheumatoid Arthritis and Its Age of Onset through Modelling Genetic Risk Variants with Smoking
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Mass drug administration ( MDA ) treatment of active trachoma with antibiotic is recommended to be initiated in any district where the prevalence of trachoma inflammation , follicular ( TF ) is ≥10% in children aged 1–9 years , and then to continue for at least three annual rounds before resurvey . In The Gambia the PRET study found that discontinuing MDA based on testing a sample of children for ocular Chlamydia trachomatis ( Ct ) infection after one MDA round had similar effects to continuing MDA for three rounds . Moreover , one round of MDA reduced disease below the 5% TF threshold . We compared the costs of examining a sample of children for TF , and of testing them for Ct , with those of MDA rounds . The implementation unit in PRET The Gambia was a census enumeration area ( EA ) of 600–800 people . Personnel , fuel , equipment , consumables , data entry and supervision costs were collected for census and treatment of a sample of EAs and for the examination , sampling and testing for Ct infection of 100 individuals within them . Programme costs and resource savings from testing and treatment strategies were inferred for the 102 EAs in the study area , and compared . Census costs were $103 . 24 per EA plus initial costs of $108 . 79 . MDA with donated azithromycin cost $227 . 23 per EA . The mean cost of examining and testing 100 children was $796 . 90 per EA , with Ct testing kits costing $4 . 80 per result . A strategy of testing each EA for infection is more expensive than two annual rounds of MDA unless the kit cost is less than $1 . 38 per result . However stopping or deciding not to initiate treatment in the study area based on testing a sample of EAs for Ct infection ( or examining children in a sample of EAs ) creates savings relative to further unnecessary treatments . Resources may be saved by using tests for chlamydial infection or clinical examination to determine that initial or subsequent rounds of MDA for trachoma are unnecessary .
Trachoma , caused by ocular infection with Chlamydia trachomatis ( Ct ) , is the leading infectious cause of blindness worldwide and is estimated to cause 3 . 6% of the world’s blindness [1] . The presence of follicles and inflammation in the upper tarsal conjunctiva , known as active trachoma , is characteristic of childhood infection . Following years of repeated infection , the upper tarsal conjunctiva may become so severely scarred that the eyelashes turn inwards , rub on the eyeball and cause corneal opacity and blindness . The World Health Organization ( WHO ) estimates that worldwide , 40 . 6 million people have active trachoma , 8 . 2 million people have in turned eyelashes ( trichiasis ) , and 1 . 3 million are blind as a result of trachoma [1 , 2] . It was estimated in 1995 that $2 . 9 billion is lost in annual revenue as a result of the loss of vision arising from trachoma[3] . Trachoma is most prevalent in poor , rural communities with low standards of hygiene and sanitation . It is thought to be endemic in 57 countries [2] . The WHO recommendations for the control and elimination of trachoma are based on a strategy with the acronym “SAFE”: Surgery for in turned eyelashes , Antibiotics to treat ocular Ct infection , Facial cleanliness and Environmental improvement to reduce transmission of the infection . The WHO recommends that mass treatment with an antibiotic such as azithromycin should be given annually to districts or communities where the prevalence of follicular trachoma ( TF ) is ≥10% in children aged 1–9 years , continuing for at least three rounds before the need to re-survey . In some settings however , including the Jareng village cluster in The Gambia [4] and Rombo district in Tanzania [5] , a single round of high coverage mass azithromycin reduced Ct infection to very low and unsustainable levels , although the prevalence of TF would still have indicated that intervention was needed . Testing for Ct demonstrated that further treatments were unnecessary . In 2007 , The Gambia implemented a national plan for trachoma control [6] with a donation of azithromycin through the International Trachoma Initiative . In this plan , based on a 2006 survey [7] , extrapolation and local knowledge , 11 districts demonstrated or believed by the programme to have a prevalence of TF greater than 10% in 1–9 year olds were assigned to mass drug administration ( MDA ) with azithromycin . The Partnership for the Rapid Elimination of Trachoma ( PRET ) study [8 , 9] aimed , inter alia , to test whether one round of MDA would be sufficient to control active trachoma across a wide geographical area , comprising four of the eleven districts assigned to MDA and containing 67 , 156 people . The study compared communities randomised to receive yearly MDA for three years or to a stopping rule ( SR ) in which mass treatment would cease if the estimated prevalence of either TF or Ct infection at six months were sufficiently low . A further stopping rule was applied at the district level , according to which treatment in non-study communities would also cease if the district prevalence of infection or of TF were sufficiently low , . In the study , the TF prevalence in 0–5 year-olds in the study area was reduced below 3% after one round of treatment and Ct infection , which was at a low level initially , was not detectable in any child at 12 and 18 months of follow up . The study found no difference in outcome ( TF or Ct infection at 36 months ) between the stopping rule communities and those in which treatment continued [8] , illustrating that tests for Ct infection ( or clinical examination for TF ) could be used to demonstrate that initial or subsequent rounds of MDA were redundant . Data were gathered during PRET The Gambia with the aim of comparing the programme costs of implementing a stopping rule based on tests for infection with those of further rounds of treatment and to explore the situations in which testing for infection would have a cost advantage over two further treatments . We report on these cost data and on their application to this and other possible testing and treating strategies .
The study was conducted from 2008 to 2011 in the Foni Bintang and Foni Kansala districts in Western Region , and in Central Baddibu and Lower Baddibu in North Bank Region . These are shown on the map ( Fig 1 ) . For census purposes , The Gambia is divided into geographically defined census Enumeration Areas ( EAs ) , of similar population size , notionally containing 600–800 people . An EA is a useful unit for representative sampling , as randomly choosing EAs is equivalent to sampling settlements with probability proportional to their size . EA geography varies in ways which might influence costs; an EA is either a segment of a large settlement ( segment ) a single medium-sized settlement ( single ) , or made up of multiple adjacent small settlements ( multiple ) . The randomisation scheme in PRET has been described previously [8 , 9] . Briefly , all 102 EAs were randomly assigned to one of four arms: 1 ) standard treatment coverage , SR; 2 ) standard treatment coverage , 3 annual MDAs; 3 ) enhanced treatment coverage , SR; 4 ) enhanced treatment coverage , 3 annual MDAs; under the restriction that each settlement was treated in the same way ( all EAs representing segments of the same settlement were in the same arm ) . The outcomes were assessed in a random selection of 48 EAs for sampling , which was made such that 12 EAs per arm and per district were selected ( 3 EAs per arm per district ) and such that each large settlement was represented by only one of its segment EAs . This ‘sample’ of 12 EAs per district then served as the basis for implementing district-level stopping rules in those EAs in the district not included in the sampling . Details of PRET in The Gambia have been described elsewhere [8–10] . Briefly , all members of every household in the 48 EAs selected for sampling were listed in a census and a random sample of 100 children aged 0–5 years per EA had both eyes examined for the clinical signs of trachoma using the WHO simplified grading system [11] . An ocular swab was then taken for detection of ocular Ct infection by Amplicor Polymerase Chain Reaction ( PCR ) ( Roche Molecular Systems , Branchburg , NJ , USA ) , as previously described [12] . A new random sample of 100 children aged 0–5 was examined in each EA at 6 , 12 , 18 , 24 , 30 and 36 month follow-up time points . Samples were processed by two laboratory technicians at Medical Research Council ( MRC ) Laboratories , The Gambia , by Amplicor PCR . The manufacturer’s instructions were followed , except for sample preparation where a previously published method was used [12] , and the extracts of five swabs were pooled , with individual testing of any positive pools . At baseline , all 102 EAs in the four districts were mass treated with azithromycin by the Gambian National Eye Health Programme ( NEHP ) . EAs assigned to standard treatment coverage were visited on a single day , whereas those assigned to enhanced coverage were visited a second day to treat those not treated on the first visit . The treatment teams were kept unaware , on their first visit to an EA , of the coverage assignments . Children were dosed using height sticks , with cut-offs optimally derived from local height/weight data to minimise the risk of over- and under-dosing [6 , 13] . EAs were usually treated by a team of six people , working in three pairs , plus a driver . In segment EAs , all six team members worked together whereas in multiple EAs pairs would work on their own in the different settlements . In each pair , one measured the height and distributed the treatment , while the other recorded the treatment information against the census in the treatment book . Under the stopping rule , the decision to treat at 12 months post-baseline was based on the clinical examination and ocular Ct infection data at 6 months ( Table 1 ) . MDA was discontinued in study EAs in the SR arms if there were either no cases of Ct infection or no cases of TF in the 100 sampled individuals ( equivalent to 95% confidence that the true prevalence was less than 5% ) . Furthermore , MDA was discontinued throughout a district ( excluding those EAs randomised to three annual treatments ) if , based on the EAs that were sampled , there was 95% confidence that the prevalence of infection , ( or of TF ) in the district was below 5% . Cost data , which included personnel , fuel , equipment , consumables , data entry and supervision , were collected for census , sampling and examination . Treatment cost data were collected 12 months post baseline from the 12 EAs assigned to the enhanced coverage treatment arm , with the costs for one day of treatment ( standard coverage ) inferred by removing the costs of the second day from the total EA treatment cost . Examination cost data were collected from these same EAs at 18 months post baseline . Efforts were made to identify and exclude the costs of concurrent research activity , such as personnel and consumables involved in taking eyelid photographs , and completing consent forms . As is recommended for cost studies [14] , worksheets detailing all costs involved for the day’s activities ( examination or treatment ) were completed each day . Unit costs were obtained from local sources when available , and when not , the original source price was taken . The laboratory cost of processing the samples was calculated using information provided by the MRC Laboratories , The Gambia . Costs were obtained in US Dollars ( $ ) , British Pounds ( GBP ) , and Gambian Dalasi ( GMD ) . GMD and GBP costs were converted to $ using a historic currency conversion of an average of 366 days from the 1st January 2009 to the 1st January 2010 ( http://www . oanda . com/currency/historical-rates/ ) . For this time period , 1GMD = $0 . 0377 , and 1GBP = $1 . 5665 . Cost data were entered and analysed in Microsoft Excel 2007 . Salaries , including overheads , were converted to a daily rate . For the census , it was assumed that NEHP staff would first attend a training workshop , and that subsequently one census taker , using a motorcycle , could census one EA/day . For the treatment team , per diems to cover food and accommodation in the field were given as a single payment based on the expected number of days needed to complete the treatment . The team’s total per diem was divided by the number of days worked to obtain a daily rate . The PRET field team contained research workers and NEHP Community Ophthalmic nurses ( CONs ) . For costing we assumed that three NEHP CONs ( for form filling , grading and the field lab ) and a NEHP driver would undertake the work . Examination team received per diems for each day worked . Volunteers from the communities who facilitated field work were also compensated for their time and effort . For the laboratory , personnel costs included the salaries of two lab technicians , employed locally by the MRC Laboratories , who spent 85% of their time processing the samples . Based on an average of 920 samples being processed a week , a lab personnel cost per sample processed was calculated . The cost of fuel was calculated based on the distances in kilometres ( km ) travelled by the teams as read from the vehicle dashboards and the refuelling costs . Equipment for the treatment team included the vehicle , height sticks and weighing scales . Equipment for the examination team included the vehicle , table , chairs and loupes . Laboratory costs were obtained for all equipment necessary to run Amplicor PCR . The equipment cost/day was calculated by dividing the capital cost , by the equipment’s life expectancy in years multiplied by 345 ( number of assumed working days per year ) . For the census , consumable costs of clipboards , pens , phone credit and stationery were included . For treatment , consumables included the cost of medication . Azithromycin for trachoma control is donated free to the NEHP and to other trachoma control programmes by the International Trachoma Initiative , but storage and transport costs are met by the programmes and were included . The costing gold standard [14] of taking into account the opportunity cost ( i . e . when goods or services are donated , a ‘replacement’ cost is imputed ) was applied ( drugs were assumed purchased rather than donated ) . In this case we assumed the online market rates of $20 for 30x250mg azithromycin tablets , and $9 for a bottle of 30 ml paediatric oral suspension . Costs of tetracycline eye ointment , which is purchased and offered to children aged under 6 months and pregnant women in MDA campaigns were included . For examination , field consumables included swabs , tubes , labels , paper , gloves , waste bags , ointment , torches and batteries , phone credit and stationery . For the laboratory , costs of all consumables necessary for processing samples by Amplicor were included , together with the cost of kits . We define the Amplicor test ‘kit cost’ as the amount spent on Amplicor kits to generate one test result using a strategy of testing in pools of five and retesting all positive or equivocal pools . We assumed the NEHP would enter data rather than using the PRET research data entry system . We assumed that one data entry clerk would be employed , entering two EAs per day for treatment data , and four EAs per day for examination data . We assumed that the NEHP manager ( a senior civil servant ) would spend one day per week supervising the census , and two days per week in the field to supervise the treatment and examination teams ( one day per team ) . Supervision costs included the manager’s salary , vehicle ( car depreciation ) and fuel , calculated as outlined above . For the laboratory , we assumed that locally employed technicians were supervised by a Scientific Officer ( a scientist with a Master’s degree employed by the MRC on a sub-regional salary scale ) at 5% of their time . The treatment and exam cost data were recorded from twelve EAs ( five multiple , two single and five segments ) at the 12 and 18 month time points respectively . Census costs were estimated from records of training workshops and field records at baseline . Total EA level costs were calculated by summing the personnel , fuel , equipment , consumables , data entry and supervision costs . For both examination and treatment , when more than one EA was visited in a day , the number of individuals treated , or number of children examined , in the EA was used to provide a weighted cost per EA for items that were not “per individual/child” ( personnel , fuel , and equipment costs ) . Summary estimates were made for each type of EA and then extrapolated for the study sample ( 48 EAs ) , and for the whole study area ( 102 EAs ) , according to their decomposition by EA type . Results are expressed as total costs in US dollars ( $ ) over the study area , costs per EA and costs per head of the population of the study area . The study was conducted in accordance with the declaration of Helsinki . Written informed consent was given by adult subjects or by the parent or guardian of child participants . Ethical approval was obtained from the London School of Hygiene & Tropical Medicine ( LSHTM ) , UK , Ethics Committee and The Gambia Government/Medical Research Council ( MRC ) joint Ethics Committee , The Gambia .
Fig 2 illustrates the target populations , randomisations , study arms , MDA treatment rounds and effects of the stopping rules ( based on examining and testing 100 children per sampled EA 6 months after the first MDA round in the PRET study . The mean and median baseline EA populations in the 48 sampled EAs were 701 and 667 respectively . The mean and median baseline EA populations in the study area of 102 EAs were 658 and 622 respectively . The baseline TF prevalence was 6 . 5% of 0–5 year olds . Six months after the baseline mass treatment , TF prevalence was 2 . 4% ( 95% CI 1 . 6–3 . 1 ) and no Ct infection was found in any of the 24 EAs randomised to the stopping rule . Implementing the stopping rule led to mass treatments being discontinued in these 24 EAs . Furthermore , implementing the district stopping rule led to treatment being discontinued in all 54 non-study EAs across all four districts . MDA only continued in the 24 EAs randomised to three annual treatments , where MDA at 12 months and 24 months was implemented regardless of the prevalence of TF or of infection . At baseline , the prevalence of Ct infection was 0 . 8% ( 95% CI 0 . 3–1 . 2 ) . On average , enhancing coverage by making an extra visit improved MDA coverage of 0–9 year-olds by 3% ( from 90 . 2% to 93 . 2% ) . [8] At 36 months there were no differences in the study outcomes between the study arms ( Table 1 ) . Specifically , neither enhanced coverage nor the two additional mass treatments in the 3x annual treatment arm had any effect on TF or infection prevalence at 36 months . There were also no differences at intermediate time-points . Thus , a single round of MDA reduced TF to low levels , and there were no apparent benefits to two further rounds of mass treatment , relative to discontinuing MDA post-baseline based on tests for infection[8] . In PRET 48 EAs were sampled and 100 children 0–5 per EA examined and tested for infection at each time point . This was because the PRET study explored a stopping rule in each EA , necessitating almost all the eligible children aged 0–5 being examined in order to have 95% confidence ( with zero observed cases ) that the underlying prevalence of TF or of Ct infection in the EA was less than 5% . For a stopping rule applied to the whole study area a smaller number of sampled EAs would have sufficed for 95% confidence that the underlying area-wide prevalence of TF or Ct infection were less than 5% . We used recommendations for sampling surveys in the WHO manual for trachoma programme managers [15] to estimate the number of EAs that would have been needed to be sampled , examined and/or tested in order to determine a stopping rule of 95% confidence TF or Ct infection prevalence less than 5% across the study area of 102 EAs , given the prevalences observed in the PRET sample . These calculations were based on the design effects [16] estimated in EA summarised data from PRET at baseline and 6 months [8] . Costs are presented per EA and then applied as estimates to the study area of 102 EAs and 67 , 156 people according to decomposition by EA type as outlined above and in Tables 2 , 3 , and 4 . The breakdown of census costs is shown in Table 2 . The cost of the census was $108 . 79 for training plus $103 . 24 per EA . The estimated cost of census in the study area was $10 , 639 . 27 . Per diem costs were the greatest component of training , and supervision costs the greatest component of the census . Over the study area the estimated average cost of one MDA round in an EA was $227 . 73 for standard treatment and $296 . 05 for enhanced treatment ( Table 3 ) . The estimated cost of a standard round of MDA in the study area ( 102 EAs ) was $23 , 228 . 46 , or $0 . 35 per head of target population , increasing to $0 . 39 per head for the extra visit in the enhanced treatment . Personnel represented the greatest treatment cost , followed by supervision ( Fig 3 ) . Treatment costs varied slightly depending on the geography of the EA , with single EAs being cheapest to treat and segments the most expensive ( data in Table 2 ) . Over the study area the average EA cost of examining and testing 100 children was $796 . 90 . As anticipated costs varied by EA geography: and were $773 . 90 , $869 . 90 and $795 . 40 in multiple , single and segment EAs respectively ( Table 4 ) . The major laboratory cost was the Amplicor kit cost , at $480 . 15 for 100 samples per EA ( Fig 3 ) . The major field costs were personnel and supervision ( Fig 3 ) , whose relative importance varied depending on the geography of the EA ( data in Table 4 ) . Examining and testing all EAs in the study area , adjusting for EA type , has an estimated programme cost of $81 , 283 . 80 or $ 1 . 21 per head . Examination alone , without ocular infection swab sampling or testing has an estimated cost of $24 , 869 . 64 over the study area , $243 . 82 per EA or $0 . 37 per head ( data from Table 4 ) . Based on the results of the PRET study , and cost estimates as above we calculated cost estimates for four alternatives to the base strategy of three rounds of MDA throughout the 102 EAs in the study area , which are illustrated in Fig 4 . We considered two alternatives directly examined in the PRET study , namely 1 ) a decision to discontinue MDA in individual EAs based on testing a sample of 100 children within them after the first MDA round , and 2 ) a decision to discontinue MDA over the whole study area based on testing a subsample of EAs for Ct infection . Further , we examined the costs of decisions 3 ) to discontinue MDA based on demonstrating a reduction in TF below 5% after the first MDA round without laboratory testing and 4 ) to apply tests for Ct infection before embarking on MDA at all . Finally , we considered the effect of the cost of azithromycin in alternatives 1 ) – 4 ) if the programme had purchased the drug rather than receiving it from the donation programme . The PRET baseline EA summarised prevalence of Ct infection was 0 . 8% with a design effect of 4 . 0 , The 6 month post-baseline EA summarised Ct prevalence was 0 . 1% with a design effect of 1 . 9 . For an underlying Ct infection prevalence of 0 . 5–1 . 0% with these design effects a sample of at most six EAs would provide 95% confidence that the infection prevalence in the study area was less than 5% . Conservatively we assume below that 8 EAs would be an adequate sample . The 6 month post-baseline EA summarised prevalence of TF across all sampled EAs was 2 . 4% with a design effect of 4 . 3 . For an assumed underlying TF prevalence of 2–3% , approximately 18 EAs would be required to be sampled to demonstrate that TF was less than 5% with 95% confidence . From the data in Table 2 the estimated cost of census in the study area is $10 , 639 . 27 or $0 . 16 per head . From the data in Table 3 three rounds of standard MDA cost $69 , 685 . 38 in the 102 EAs in the study area or $1 . 05 per head . The total estimated cost of the base strategy applied in the study area is $80 , 324 . 75 or $1 . 20 per head . From the data in Tables 2 , 3 , and 4 the costs of census plus one standard MDA round plus examining and testing 100 children in each EA once is $115 , 151 . 63 or $1 . 71 per head . Thus it costs $34 , 826 . 88 , $359 . 50 per EA or $0 . 51 per head more to test 100 children per EA for Ct infection than to treat them annually twice . Fig 5 demonstrates how this depends on the kit cost—in order for testing using an Amplicor-like test to produce savings relative to two unnecessary MDA rounds , a kit cost of $1 . 38 or less per result would be required In the PRET study , examination and testing was conducted in 48 EAs at an estimated programme cost of $38 , 163 . 36 ( Table 4 ) . Combining this with census and treatment costs across the study area from Tables 2 and 3 leads to a cost estimate of $72 , 004 . 09 or $1 . 07 per head . Implementation of the district level stopping rules across the study area based on these 48 EAs would have resulted in a saving of $8 , 320 . 66 , or $0 . 13 a head . However , following the above sampling calculations , if a sample of 100 children in 8 EAs were examined and tested for Ct infection to show that two further MDA rounds were unnecessary in the study area , this would cost $40 , 242 . 93 or $0 . 60 per head and save $40 , 081 . 82 or $0 . 60 a head across the study area . Following the above sampling calculations , we assume that 100 children in each of 18 EAs could be examined for TF ( without testing for Ct infection ) to show that two further MDA rounds were unnecessary in the study area . From the data in Tables 2 , 3 , and 4 this costs $38 , 256 . 49 or $0 . 57 a head , and would save $42 , 068 . 26 or $0 . 63 a head across the study area . Based on the above sampling calculations we assume that we could demonstrate that three rounds of MDA were unnecessary through census , sampling and testing in eight of the 102 EAs . From data in Tables 2 and 4 this would cost $7 , 309 . 91 or $0 . 11 per head . Thus , if MDA were not to start at all when baseline infection was less than 5% with 95% confidence , there would be a saving of $73 , 014 . 84 , or $1 . 09 per head . A head to head comparison of alternatives 1 to 4 is given in S1 Table . If azithromycin had been bought rather than donated , census followed by three rounds of MDA in the study area would have cost $461 , 723 . 05 or $ 6 . 91 per head ( data in Tables 2 and 3 ) . Census followed by one standard round of MDA would cost $161 , 031 . 13 or $2 . 40 per head . This is roughly twice the $1 . 21 cost per head of testing 100 children for infection in every EA .
We have shown , in the context of the findings of the PRET study , that tests for infection can be applied in trachoma control to prevent further redundant MDA rounds and that in circumstances where an initial MDA round reduces infection below the decision threshold , this will save resources . Using programme costs estimated for The Gambia , discontinuing MDA based on testing 100 children for Ct infection in each of a sample of communities , ( drawn from a total population of 67 , 156 ) after one round of MDA , offers cost savings of $0 . 57 a head relative to continuing for three MDA rounds , even when the Amplicor test is used . Since PRET found no difference in effectiveness between these study arms , this is also a cost-effective strategy . The 3rd WHO Global Scientific Meeting on trachoma [17] made recommendations for trachoma surveillance post-MDA based on the sub-district , where a sub-district is a natural or convenient segmentation of a district of 250 , 000 people . Here we present calculations extrapolated to the whole PRET study area of 102 census EAs and 67 , 156 people , which despite being made up of four Gambian districts , we believe best corresponds to the ‘sub-district’ envisaged in the recommendations . We found the average EA treatment cost in The Gambia , based on a single treatment visit to each community not including census was $227 . 73 , which equates to $0 . 35 per head of population . Enhancing coverage via an extra treatment visit to each community cost a further $0 . 04 per head , improved coverage in 0–9 year-olds by about 3% , but had no effect on outcome and , in this setting , was not worthwhile . The treatment costs are less than the $ 1 . 53 per head reported in South Sudan [18] , but similar to the $ 0 . 25 estimate from Mali [19] . A cost of ≤$0 . 50 per person for trachoma treatment has been quoted in the literature when assessing programme sustainability or cost savings through integrated treatment campaigns targeting several neglected tropical diseases [18 , 20 , 21] . Our results are in line with this estimate . The estimated total annual cost of mass treatment with standard coverage and donated azithromycin for the study area ( 102 EAs and 67 . 156 people ) was $23 , 224 per round . The main drivers were personnel costs followed by supervision . Others have also found personnel to be the major cost from all cost categories in population-based prevalence surveys [22] and trachoma mass antibiotic treatment distributions [18] . Interestingly , both these studies found transportation to be the next most expensive cost category after personnel , whereas it was not a major cost in our study . This is likely a reflection of the high population density , small distances travelled and relatively good terrain in The Gambia . Costs will likely be higher in countries where the population is sparse and the terrain unforgiving , reflecting increased personnel and transportation costs . This is apparent in the study from the Ayod county of Southern Sudan where a plane had to be chartered to transport personnel [22] . There are different , but not markedly different , costs associated with the variations in EA geography , presumably reflecting different field team organisation , and more hierarchical social structures in smaller settlements , offset by higher transport costs in visiting multiple settlements . The average cost of testing 100 children per EA for Ct infection was $796 . 90 . The major contribution was the kit cost of Amplicor testing at $480 . 15 per EA . This is less than the cost of 100 Amplicor tests , but more than the cost of 20 , because of the strategy of testing in pools of five ( requiring 20 tests per 100 ) and then retesting each individual sample within a positive pool [23] , which here reduced costs by over 60% relative to testing each sample individually . We show that , at the EA level , testing using an ‘Amplicor-like’ test would not cost less than two further rounds of MDA unless the kit cost for testing the EA was below $138 per EA or $1 . 38 per result . However because the number of EAs that would need to be tested to establish district level prevalence with sufficient precision to guide MDA treatment decisions is , in this case , relatively small a district level decision process similar to our ‘stopping rule’ has the potential to save money even with the current costs of testing . The Amplicor test used in this study is no longer commercially available , but alternatives such as the COBAS Amplicor CT/NG Test , Aptima Combo 2 Assay ( Gen-Probe Inc . CA , USA ) , and the Real-time CT/NG Assay ( Abbott Laboratories , Illinois , USA ) platform are no cheaper and some [24] but not all have had pooling strategies validated . Nevertheless , this study suggests that even the application of relatively costly Nucleic Acid Amplification Tests ( NAATs ) may save resources if a sample of representative communities was tested for infection before MDA was implemented The ‘stopping rule’ used in PRET was an arbitrary one based on the observation that if no infections were found in a sample of 100 children then there was 95% confidence that the true infection prevalence was less than 5% . Whether an infection prevalence of 5% in 0–5 year-olds is the best cut-off for discontinuing MDA is unknown . It has been pointed out by others that studies to ascertain a threshold or thresholds below which trachoma infections do not persist and will then disappear on their own ( i . e . the existence of an ‘Allee effect’ ) will be very difficult to conduct against the background of worldwide downward secular trends in trachoma prevalence [25] . However , one interpretation of the PRET data would be that The Gambia reached such a point before the PRET study started , when the application of tests for infection would have shown a baseline infection prevalence of 0 . 8% , The potential we outline for a testing strategy to save money is likely to vary with both TF prevalence and Ct infection rates and with the number of children tested in a population unit . Here TF prevalences in the 5–10% range were compatible with extremely low rates of Ct infection , but the extent to which this is generalisable needs to be established in other studies in which district or sub-district level TF and Ct infection rates are both estimated . In PRET , 100 children per EA were tested as if there were zero cases of infection there was 95% confidence that the true rate in the EA was less than 5% . For a district rather than EA based MDA decision fewer children per EA could be tested and our data could be extrapolated to estimate costs in those circumstances . We suggest that tests for Ct infection will be needed to confirm whether , or , more likely , when a country , region or district is ready to discontinue MDA . We show that the application of such tests with appropriate sampling schemes and decision rules will save money relative to initiating or continuing MDA , even with the current costs of NAATs . Our study had a number of limitations which may affect the results . We did not include the opportunity cost of people coming to be treated and/or examined in relation to what they would otherwise have been doing , what this represented in monetary terms , and how long each adult spent with the team . The NEHP workers administering treatment were taken away from their usual tasks , also incurring societal costs . Our total cost estimates are therefore likely to under-estimate the total societal cost . By not including the opportunity cost , we may have masked cost differences between the strategies , depending on the amount of time an adult spent when attending and accompanying children for treatment distribution , compared with accompanying children for examination . Studies comparing different trachoma treatment strategies have noted that patient ( opportunity ) cost is a major cost in mass azithromycin treatment with donated drug [19 , 26] . We probably under-estimated the cost of the extra treatment day in the enhanced treatment strategy as , per person , the teams would have spent longer per person treated trying to find the few remaining individuals needing treatment than treating people when arriving in an EA for the first day . As the extra day had no impact on outcome , this concern is minor . Finally , however , our data are taken from a research project . Although we have made substantial efforts to separate out and remove research costs , the census , treatment and examination activities may have been organised and conducted differently if entirely planned and run by the programme . Programmes may elect for example to combine census and treatment activities . Thus , absolute costs would probably vary with a different project organisation ( and be different in another low prevalence country ) , but we suggest that the relative cost differences we highlight between three annual MDAs and a stopping rule based on testing for infection would be less affected . If azithromycin were purchased rather than donated , the cost of treatment was 6 . 7 times greater with azithromycin constituting over 80% of the total cost . This is much more than the costs of testing and , without the donation programme it would be cheaper to test first if there were even a minority of communities with low levels of infection as purchasing azithromycin is prohibitively expensive [19 , 26–28] This underlines the importance of the donation to national eye health programmes in their quest to eliminate trachoma as a public health problem .
We have shown that the strategy of three annual rounds of mass azithromycin treatment of a sub-district are more expensive than examining and testing ocular swabs from a sample of EAs , if , as in the PRET study treatment can then be discontinued based on the results after one round . Therefore , in low prevalence settings , it could be both cost-saving and cost-effective to implement a stopping rule strategy .
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Trachoma , caused by infection with a bacterium ( chlamydia ) is controlled by mass drug administration ( MDA ) , which is recommended yearly for districts in which a trachoma problem has been found to exist . The decision , after several rounds , that MDA is no longer needed is currently based on clinical signs of trachoma , but these are an unreliable indicator of infection . The PRET study , in the Gambia , found that tests for infection could be used to show that subsequent rounds of MDA were redundant . This paper shows , by estimating the costs , that testing children in a sample of census districts for infection can save resources compared to ( unnecessary ) rounds of MDA .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion",
"Conclusion"
] |
[] |
2015
|
Costs of Testing for Ocular Chlamydia trachomatis Infection Compared to Mass Drug Administration for Trachoma in The Gambia: Application of Results from the PRET Study
|
Complex trait genome-wide association studies ( GWAS ) provide an efficient strategy for evaluating large numbers of common variants in large numbers of individuals and for identifying trait-associated variants . Nevertheless , GWAS often leave much of the trait heritability unexplained . We hypothesized that some of this unexplained heritability might be due to common and rare variants that reside in GWAS identified loci but lack appropriate proxies in modern genotyping arrays . To assess this hypothesis , we re-examined 7 genes ( APOE , APOC1 , APOC2 , SORT1 , LDLR , APOB , and PCSK9 ) in 5 loci associated with low-density lipoprotein cholesterol ( LDL-C ) in multiple GWAS . For each gene , we first catalogued genetic variation by re-sequencing 256 Sardinian individuals with extreme LDL-C values . Next , we genotyped variants identified by us and by the 1000 Genomes Project ( totaling 3 , 277 SNPs ) in 5 , 524 volunteers . We found that in one locus ( PCSK9 ) the GWAS signal could be explained by a previously described low-frequency variant and that in three loci ( PCSK9 , APOE , and LDLR ) there were additional variants independently associated with LDL-C , including a novel and rare LDLR variant that seems specific to Sardinians . Overall , this more detailed assessment of SNP variation in these loci increased estimates of the heritability of LDL-C accounted for by these genes from 3 . 1% to 6 . 5% . All association signals and the heritability estimates were successfully confirmed in a sample of ∼10 , 000 Finnish and Norwegian individuals . Our results thus suggest that focusing on variants accessible via GWAS can lead to clear underestimates of the trait heritability explained by a set of loci . Further , our results suggest that , as prelude to large-scale sequencing efforts , targeted re-sequencing efforts paired with large-scale genotyping will increase estimates of complex trait heritability explained by known loci .
In the past few years , genome-wide association studies ( GWAS ) have identified hundreds of genetic variants associated with quantitative traits and diseases , providing valuable information about their underlying mechanisms ( for a recent example , see [1] ) . More than 2 , 000 common variants appear associated with over 200 conditions ( as reported by the NHGRI GWAS catalog on 12/2010 ) and for a few , like age-related macular degeneration [2] and type 1 diabetes [3] , these common variants already account for a large fraction of trait heritability . In contrast , for most complex traits and diseases , common variants identified by GWAS confer relatively small increments in risk and explain only a small proportion of trait heritability [4] . For example , for low-density lipoprotein cholesterol ( LDL-C ) , GWAS based on up to ∼100 , 000 individuals examined at ∼2 . 5 million common variants [1] , [5]–[6] , have identified 35 loci associated with trait variation , with some also involved in modulating the risk of cardiovascular diseases . Common variants at these loci are estimated to account for 12 . 2% of the variability in LDL-C levels , about one-fourth of its genetic variance [1] . Several hypotheses have been formulated about the nature of the remaining heritability of lipid levels and other complex traits [4] , [7] , ranging from the potential role of copy number variants to contributions from a large number of common SNPs each with very small effects . In our view , common and rare variants that are poorly represented in common genotyping arrays might account for an important fraction of trait heritability . Ignoring these variants might not only preclude identification of important trait associated loci but also compromise estimates of heritability . Thus , fine mapping appears the logical next step after GWAS . Here , we have focused on seven genes located in five of the loci associated with LDL-C in our original GWAS for blood lipid levels ( APOE , APOC1 , APOC2 , SORT1 , LDLR , APOB and PCSK9 ) [5] . A sixth locus ( corresponding to SNP rs16996148 ) that included a large number of genes and no obvious functional candidates was not further examined here . Together , the 5 SNPs identified in the original GWAS analyses of these 5 loci in >8 , 000 individuals ( with follow-up genotyping of >10 , 000 individuals ) explained only 3 . 1% of LDL-C variability . We set out to re-assess the contribution of these loci to trait heritability by evaluating a broader spectrum of variants . To catalog genetic variation in these regions , we first sequenced the exons and flanking regions of the seven genes in 256 unrelated Sardinians [8] , each with extremely low or high LDL-C , and in an additional 120 HapMap samples ( parents from the 30 CEU and 30 YRI trios ) . To assess the effect of identified polymorphisms , we genotyped detected variants and additional variants selected based on an early release of the 1000 Genomes Project in a cohort of 5 , 524 volunteers from the SardiNIA project [8] . Our results show that at these five loci , a combination of rare and common variants , some novel and some previously identified , are associated with LDL-C , and that , taken together they double the variance explained by the common variants detected in GWAS .
To refine the contribution of five loci implicated by GWAS in the variability of LDL-C , we sequenced the exons and flanking regions of seven genes in 256 unrelated Sardinians [8] with LDL-C levels that were either extremely low ( 116 individuals , mean LDL-C = 70 . 4±16 . 0 mg/dl ) or high ( 140 individuals , mean LDL-C = 205 . 9±19 . 6 mg/dl ) ( Materials and Methods ) , as well as an additional 120 HapMap samples ( parents from the 30 CEU and 30 YRI trios ) . Observed heterozygosity per base pair per individual was 1 . 28×10−3 in the selected Sardinian individuals , 1 . 31×10−3 in the CEU and 1 . 99×10−3 in the YRI . Sequencing identified 782 variants , all submitted to dbSNP and now included in dbSNP releases 130 and later ( for a complete list see Table S1 ) . As expected , more variants were found in the HapMap YRI samples than in the HapMap CEU or in Sardinian individuals with extreme lipid levels ( Table S2 ) . Overall , we observed a 2∶1 trend for enrichment of rare variants ( MAF<1% ) in the high LDL-C group compared to the low LDL-C group , similar to the observation by Johansen and colleagues [9] ( Table S3 ) , but this enrichment was only statistically significant for APOB ( P = 0 . 03 using an exact test ) . To test for LDL-C association , we used logistic regression to compare individuals in the two categories , yielding 10 variants ( in APOE , APOC1 , SORT1 , APOB , and PCKS9 ) with P<0 . 1 ( Table S4 ) . Because of the modest number of sequenced individuals and because no signal reached significance after Bonferroni adjustment , we judged these initial association analyses – which focused only on sequenced samples and only at coding regions – inconclusive . In addition to the loci discussed so far , our re-sequencing and genotyping effort also included B3GALT4 and B4GALT4 , two loci that approached genome-wide significance in our initial GWAS analysis ( each with 5×10−8<p<5×10−6 ) [5] . SNPs in these loci did not reach genome-wide significance in two subsequent meta-analyses [1] , [6] and were not significantly associated with LDL-C in the data generated here ( Table 1 and Figure S1 ) . Because we have no evidence that these two genes are associated with LDL-C , they are not discussed further . Variants identified in the two genes were also deposited in dbSNP . To increase the power to detect association , we genotyped 5 , 524 individuals in the SardiNIA cohort [8] using the Metabochip ( see Materials and Methods ) . The chip included 285 variants newly discovered by sequencing , together with an additional 2 , 992 derived from an early analysis of 1000 Genome Project Pilot haplotypes ( considering variants ±250 Kb from each gene ) . Of the 3 , 277 SNPs that were genotyped , 1 , 868 passed quality control filters ( see Materials and Methods and Table S5 ) . To further supplement the number of variants at each locus , we carried out two rounds of genotype imputation . First , we used haplotypes for 256 sequenced SardiNIA samples to impute genotypes for SNPs that failed assay design or genotyping on the Metabochip . Second , using the haplotypes of 60 CEU samples from the 1000 Genomes Pilot , we successfully imputed an additional 5 , 066 variants [10] ( Materials and Methods and Table S5 ) . After imputation , 7 , 488 SNPs were available for analysis , with an average minor allele frequency of 18% and an average imputation r2 of 0 . 84 for 5 , 620 imputed SNPs ( 554 and 5 , 066 from Sanger and 1000 Genomes imputations , respectively; see Table S5 for gene specific counts ) . At three loci , SORT1 , APOB and LDLR , GWAS-identified variants were very strong proxies for the best available association signal , with similar allele frequencies and r2>0 . 88 ( Table 1 , Figure 1A and Figure S2 ) . In those three genes , the variant showing strongest association was non-coding and not in strong linkage disequilibrium ( r2>0 . 4 ) with any tested coding variant . The most strongly associated marker at the SORT1 locus , rs583104 ( p-value = 1 . 2×10−9 ) was in high LD ( r2 = 0 . 77 ) with rs12740374 ( p-value = 2 . 2×10−8 ) , an intronic SNP in the CELSR2 gene that alters the hepatic expression of the SORT1 gene by creating a C/EBP ( CCAAT/enhancer binding protein ) transcription factor binding site [11] . Both markers were genotyped , so that under the hypothesis that rs12740374 is the causal variant underlying this association signal , the modest difference in p-values may be attributable to statistical fluctuation . At the remaining two loci , APOE and PCSK9 , evidence for association peaked at low frequency ( 1–5% ) variants not in strong linkage disequilibrium with the original GWAS signals . In both cases our analyses pointed to variants that were well studied in other contexts , but which are not included in typical GWAS panels or in the HapMap panel of European haplotypes commonly used to impute missing genotypes . Thus these variants were missed in previous GWAS analyses . In PCSK9 , variant rs11591147 , which leads to a non-synonymous R46L change in exon 1 , was more strongly associated ( P = 2 . 9×10−15 , frequency ( T ) = 0 . 037 , effect = −12 . 9 mg/dl; Table 1 ) than GWAS variant rs11206510 , a SNP ∼10 Kb upstream of the transcription start site of the gene ( P = 5 . 7×10−7 , frequency ( C ) = 0 . 24 , effect = −3 . 7 mg/dl ) ( Figure 1C ) . Furthermore , rs11591147 explained the GWAS association signal ( association at GWAS variant rs11206510 became non-significant ( P = 0 . 013 ) when non-synonymous variant R46L/rs11591147 was included as a covariate , Figure 1D ) . This coding variant has been previously implicated in the regulation of blood lipid levels , including LDL-C , and in the susceptibility to coronary and ischemic heart disease [12]–[13] . At the APOE gene cluster , the strongest evidence of association was observed at the missense variant ( R176C , also known as R158C [14] ) rs7412 ( P = 1 . 8×10−31 , frequency ( T ) = 0 . 037 , effect = −18 . 8 mg/dl ) ( Figure 1E ) . This variant did not account for the previously reported GWAS signal; marker rs4420638 indeed remained significantly associated ( P = 6 . 4×10−10 ) after adjusting for rs7412 . The missense variants at APOE and PCSK9 were not typed in the HapMap II data set , and were only recently added to genotyping arrays ( Illumina 1MDuo ) . Thus they have not been assessed by any GWAS reported to date . We next conditioned on the top association signal at each of the 5 loci and sought to identify additional independently associated variants . To declare statistical significance at secondary signals , we used a p-value threshold of 1×10−4; corresponding to an adjustment for 500 independent tests across the five regions examined . At LDLR , we found an independently associated rare missense variant ( rs72658864/V578A , P = 2 . 5×10−6 in the basic model , P = 3 . 9×10−6 in the conditional model , frequency ( C ) = 0 . 005; effect = 23 . 7 mg/dl ) ( Table 1 and Figure 1B ) . This variant appears to be specific to Sardinia ( where we identified it in our SardiNIA cohort [8] by Sanger sequencing in 3 out of 256 individuals with extreme LDL-C; by Illumina genotyping in 51 out of 5 , 800 randomly ascertained individuals; and by Solexa sequencing in 1 out of 505 individuals , unpublished data ) . It is absent in the HapMap data set , not detected in 280 Northern European individuals sequenced within the 1000 Genomes Project , and monomorphic in >10 , 000 Finnish [15]–[16] and Norwegian [17]–[19] individuals genotyped with the MetaboChip ( Materials and Methods , Table S6 and Table S7 ) . Reassuringly , the variant was also observed , albeit with a lower frequency ( 0 . 00035 ) , in TaqMan genotyping an independent sample of 5 , 661 Sardinians from different villages in Sardinia [20] ( Materials and Methods ) . The change in lipid levels associated with this rare variant ( 23 . 7 mg/dl ) is 4 times greater than that observed for the strongest associated common variant at the locus ( 5 . 7 mg/dl for rs73015013 ) . At the APOE locus , we found a strong independent signal at non-synonymous variant rs429358 ( C130R , also known as C112R [14] ) ( Table 1 and Figure 1F ) ( P = 1 . 2×10−12 in the basic model , P = 5 . 8×10−11 in the conditional analysis , frequency ( C ) = 0 . 071 , effect = 9 . 3 mg/dl ) , which , together with rs7412 , defines the three major isoforms of APOE ( ε2 , ε3 and ε4 ) [14] , [21] . This variant strongly correlates ( r2 = 0 . 96 ) with the originally reported GWAS signal , rs4420638 ( P = 4 . 6×10−12 , frequency ( G ) = 0 . 097 , effect = 7 . 8 mg/dl ) . So , at this locus , the initial GWAS analysis picked up one independent signal ( a proxy of rs429358/C130R ) but missed the strongest associated variant in the region ( rs7412/R176C ) . There was no clear evidence for residual association after accounting for the two missense variants ( Figure S3 ) . Interestingly , the frequency of the derived allele C at rs429358 was remarkably lower in Sardinia ( freq = 0 . 07 , see Table 1 ) than that observed in the Finnish and Norwegian individuals ( see Table S7 ) and several other European ancestry samples ( freq∼0 . 20 ) [22]–[24] , resulting in a strikingly lower frequency of the ε4 haplotype ( 2 . 5% vs . 15% ) [22] . Finally , at PCSK9 , we observed a possible independent association at SNP rs2479415 , in the non-coding region flanking the transcript ( P = 1 . 1×10−7 in the basic model , P = 8×10−5 in the conditional model , frequency ( T ) = 0 . 59 , effect = −3 . 6 mg/dl ) ( Table 1 and Figure 1D ) . This variant showed an independent trend also in ∼10 , 000 Finnish and Norwegian individuals ( one-sided P = 0 . 055 after conditioning for rs11591147 ) . When the 5 GWAS SNPs were replaced by the 8 variants described here ( 1 each for SORT1 and APOB , 2 for APOE , PCSK9 and LDLR ) the variance accounted for by those loci increased from 3 . 1% to 6 . 5% . Similar estimates were also obtained with ∼10 , 000 Finnish and Norwegian individuals , where , on average , analysis of these 8 variants increased variance explained from 3 . 5% to 7 . 1% ( Table 2 and Materials and Methods ) .
We conducted fine mapping of five loci associated with LDL-C at an unprecedented level of resolution . In particular , we sequenced individuals with extreme phenotype levels , and subsequently genotyped variants identified by us and by the 1000 Genomes Project in a larger sample . In a final step we also imputed additional variants in the region to account for limitations of genotyping assay design . At all but one of the loci , APOB , the most strongly associated variant was directly genotyped or sequenced , suggesting that our initial selection included the crucial variants . In three loci , we found strongly associated rare or low frequency variants – which ( except for a variant in LDLR , which appears to be specific to Sardinia ) had been extensively characterized in previous non-GWAS studies . In these cases , although the associated variants had been previously described , they had not been thoroughly examined in together with GWAS associated variants at the same loci – so that the relative contributions of GWAS identified SNPs and previously described variants remained unclear . In summary , we observed that: The strongest signals identified at APOE ( both variants ) and PCSK9 ( the top hit ) are likely to be the causal variants underlying the association signals . For SORT1 , the variant exhibiting strongest association appears to be in strong linkage disequilibrium with a recently proposed functional polymorphism . In contrast , biological interpretation remains unclear for the other identified polymorphisms and requires further studies . Our results lead to several important major conclusions . First , it is striking that prior LDL-C GWAS have often missed signals due to low frequency variants ( in two of the loci examined here , we identified strongly associated variants with frequency 1–5% that were missed in the original GWAS , because they were untyped or missing on imputation panels and poorly tagged by nearby SNPs ) . Sequencing in individuals with extreme trait values , along with large-scale imputation and genotyping , provided a better evaluation of the contribution of these loci to variation in LDL-C levels . A similar design was recently used to fine-map loci associated with fetal hemoglobin levels , a trait for which three loci can now account for about half of total variance [25] . Second , we show that in one of the five loci we fine-mapped , a previously missed low frequency variant can account for the GWAS signal – consistent with the hypothesis that at least some GWAS signals will be due to disequilibrium with nearby low frequency or rare variants [26] . There is considerable debate on how frequently this scenario will occur [27] . Our observations are compatible with some of the arguments made on both sides of this debate [26]–[27] . For example , in the case of PCSK9 , a single low frequency variant explains the observed common variant association signal but did not appear to reduce the ability of the genome-wide association study to localize the functional element of interest . Furthermore , the effect of this variant was too small to be detectable in most linkage studies ( including our own linkage analysis of >35 , 000 relative pairs in Sardinia ) . Further , a single low frequency variant ( and not a cluster of variants ) was sufficient to explain this association signal . Finally , our results show that if estimates are based only on the common variation assessed through GWAS , heritability at identified loci is likely to be underestimated . A more complete dissection , including common , low frequency and rare variants ( some of which will be population specific ) , dramatically increased the proportion of heritability associated with the 5 loci examined here , from 3 . 1% to 6 . 5% . Notably , the variance explained by each locus increased when a rare variant was found as a primary or secondary hit ( LDLR , APOE and PCSK9 ) , even when the top GWAS SNP highly correlates with a strong association signal ( LDLR and APOE ) . By contrast , only slight improvements were observed at loci where the most associated marker highly correlated with the GWAS SNPs and there was no evidence for additional independent signals , even when the GWAS variant is unlikely to be functional ( SORT1 and APOB ) . Genome-wide association studies have proven to be an extremely productive strategy for identifying regions of the genome associated with complex traits , often leading to unexpected insights into complex trait biology . A major efficiency of these studies is that , by focusing on a subset of variants that can be genotyped using array based platforms , they can conveniently and economically survey many common variants in large numbers of individuals . Our results emphasize the utility of these genome-wide studies in identifying trait association regions , but also emphasize that caution is needed when genome-wide study results are used to quantify the overall contribution of a locus to trait heritability . In our opinion , and consistent with our results , accurate estimates of heritability will require more extensive examination of each identified locus . Broadly , this observation is consistent with recent simulation studies [28] which explore , in the context of a dichotomous trait , the relationship between effect sizes observed at GWAS SNPs and at true causal variants for the same locus . These simulation studies suggest that , most of the time , effect sizes estimated from GWAS would be similar to true effect sizes but that , some of the time , effect sizes estimated from GWAS might substantially underestimate the true effect size – especially in a scenario where rare variants are more likely to be causal . In cases where the effect size was underestimated by GWAS variants , a noticeable increase in heritability ensues . It is also interesting to note that the effect sizes estimated here for rare and low frequency variants ( all >10 mg/dl ) are larger than the effect sizes of any of the common variants identified in GWAS studies . Effect sizes of more rare alleles associated with familial hypercholesterolemia are even larger ( see [29] for examples of PCSK9 variants with effects >100 mg/dl ) . This is consistent with the intuition that alleles with a large impact on LDL-C levels will be under strong natural selection and will , thus , be prevented from reaching high frequency in the population . Although rare and low frequency alleles with more modest impacts on LDL-C values are also likely to exist , we cannot detect them using available sample sizes and their detection must await studies of much larger sample sizes . In conclusion , these results underline that the subsequent sequencing of the coding regions around GWAS associations in individuals with extreme values followed by large scale imputation and genotyping is an important step in assessing the contribution of associated genomic regions to trait heritability . If similar trends to those described here are observed at the remaining LDL-C associated loci , extending our approach described to all known LDL-C susceptibility loci could lead to an increase in the proportion of variance they explain from ∼12% to ∼24% , exceeding half of the genetic variance for this trait . Due to economic considerations , our sequencing efforts focused on the coding regions of each gene and only on genes that appeared very likely to be involved in lipid metabolism . In each locus , we augmented the set of discovered variants with variants discovered by the 1000 Genomes Project , but that will likely miss very rare as well as population specific variants . We expect that more extensive fine-mapping efforts that more comprehensively examine non-coding regions could identify additional trait associated variants . Ultimately , unbiased whole genome sequencing based association analyses might be required to fully explain the heritability of a trait like LDL-C , facilitating the comprehensive assessment of rare , population specific , and non-SNP variation . In the meantime , directed sequencing and large scale genotyping appears to be a promising approach .
All individuals studied and all analyses on their samples were done according to the Declaration of Helsinki and were approved by the local medical ethics and institutional review committees . The SardiNIA project is a population based study of aging-related traits that includes 6 , 148 related individuals from the Ogliastra region of Sardinia , Italy [8] , [30] . During physical examination , a blood sample was collected from each individual and divided into two aliquots , one for DNA extraction and the other to characterize several blood phenotypes , including lipids levels . Specifically , LDL-C values were derived using the Friedwald formula that combines HDL and total cholesterol levels . The Finnish and Norwegian individuals are Type 2 Diabetes patients and unaffected individuals collected from several studies . Specifically , Finnish studies are: Dehko 2D 2007 ( D2D 2007 ) , Dose Responses to Exercise Training ( DrsEXTRA ) , Diabetes Prevention Study ( DPS ) , FUSION stage 2 [15] samples ( from ACTION LADA , D2D 2004 , FINRISK 1987 , FINRISK 2002 , Health 2000 , Savitaipale ) and Metabolic Syndrome in Men ( METSIM ) [16]; Norwegian studies are: The Nord- Trøndelag Health Study ( HUNT 2 ) [17]–[18] and The Tromsø Study ( TROMSØ ) [19] . Baseline clinic characteristics of the SardiNIA , Finnish and Norwegian studies are reported in Table S7 . The independent Sardinian sample used for assessing the frequency of the rare variant at LDLR consists of 5 , 661 individuals belonging to 884 families enrolled in the SharDNA study [20] , which recruited volunteers from a cluster of villages located in the Ogliastra region: Talana , Urzulei , Baunei , Triei , Seui , Seulo , Ussassai , Perdasdefogu , Escalaplano and Loceri . Observed heterozygotes were unrelated to those observed in the SardiNIA study based on demographic records to track origin of individuals up to 10 generations . Sequencing of the 256 Sardinians and the 120 HapMap samples ( parents from the 30 CEU and 30 YRI trios ) was carried out at the University of Washington Genome Sequencing Center through the NHLBI Resequencing & Genotyping Service ( Debbie Nickerson , PI ) . To select the 256 individuals to be sequenced , we adjusted LDL levels by age and sex and then identified individuals in the top and bottom 5% of the distribution ( individuals under lipid-lowering therapy were not considered ) . Among those , we selected all unrelated individuals who had at least one sibling in the study and were genotyped with 500 K or 10 K arrays [30] , to facilitate downstream follow-up and imputation analyses . Among the 782 variants detected by sequencing , two loss-of-function variants were observed . However , these were identified only on HapMap samples ( see Table S8 ) . A common in-frame insertion in APOB was observed in Sardinia and in HapMap CEU samples but was not associated with LDL-C after multiple testing adjustment ( rs17240441 , P = 3 . 0×10−4; see Figure S1C and S1D , Table S8 ) . The observed heterozygosity per bp/per individual was 0 . 00128 , 0 . 00131 and 0 . 00199 in Sardinia , CEU and YRI samples , respectively . Concordance rate of HapMap II and III phases genotypes with those obtained from Sanger sequencing was 99 . 63% , while a lower rate ( 98 . 1% ) was observed with genotypes obtained from the low-pass sequencing 1000 Genomes Project ( 43 CEU and 42 YRI samples were common between the two datasets ) , indicating the slightly lower accuracy of next-generation sequencing technologies and in particular of low-pass sequencing approaches [31] . Genotyping was carried out with Metabochip arrays ( Illumina ) , which were designed in collaboration with several international consortia [5] , [32]–[33] with the aim to fine map association loci detected through GWAS for a variety of traits . Part of the design included a set of wild-card SNPs chosen by individual research groups , and the SardiNIA study promoted the inclusion of all variants detected by sequencing individuals with extreme LDL-C values . In particular , assays were successfully designed for 285 of the 782 variants discovered by sequencing and 178 passed quality controls filters ( some of those were polymorphic only in HapMap individuals , but we included all detected variants on the chip to assess heterozygosity on a large sample ) . Briefly , 3 , 277 variants were included on MetaboChip , and 1 , 868 passed quality checks . For a detailed description of markers discarded by each filter see Table S9 . Concordance rate of Sanger and Metabochip genotypes was 99 . 47% at QCed markers , evaluated comparing genotypes of the 256 sequenced samples . Metabochip genotyping was performed using Illumina Infinium HD Assay protocol with Multisample Beadchip format , and GenomeStudio was used for genotype calling . All samples had a call rate>98% , and there was no evidence for mis-specified family relationships ( evaluated using Relpair software [34] ) . We discarded markers if any of the following was true: a ) call rate <95% , b ) MAF = 0 , c ) Hardy-Weinberg Equilibrium P<10−6 or d ) excess of Mendelian Errors ( Table S9 ) . A total of 5 , 524 Sardinian individuals were genotyped , of which 5 , 382 had lipid measurements available and were not under lipid lowering therapy . In the Finnish and Norwegian studies , a total of 10 , 823 samples were genotyped , of which 10 , 027 had LDL-C measurement available and were not under lipid lowering therapy . Genotyping of the rare LDLR variant rs72658864 on the SharDNA samples was carried out using TaqMan single SNP genotyping assays ( Applied Biosystems ) . Given the rarity of the variant , DNA of a known heterozygote from the SardiNIA project was included in each well plate to allow detection of intensities of both alleles . The genotype of this sample was called as heterozygote in all plates . To better represent genomic variation , we merged genotypes from the 256 sequenced Sardinian samples with genotypes available from Affymetrix 500 K [30] and/or Metabochip for all variants +/−2 Mb spanning the gene's transcript . We then phased the haplotypes using MACH [10] and used this reference set of haplotypes to impute sequence variants in the rest of the cohort [35] . We then focused on variants within +/250 Kb of the gene transcript . To further fine map the region , we used 120 haplotypes from the 60 CEU samples sequenced within the 1000 Genomes Project ( June 2010 release of haplotypes based on March 2010 genotypes release ) to impute variants outside the coding regions and flanking sequences targeted in our sequencing study . MACH software was used for imputation , with the same sized window used for the Sardinian-based imputation ( +/−2 Mb ) . The results obtained with these two rounds of imputation are identified in the text , as well in table and figure legends , as “Affy+Sanger” and “1000G” , respectively . For association , LDL-C levels were adjusted for age , age squared and sex , and the distribution of residuals was normalized using a quantile transformation . The association test was performed using Merlin ( –fastassoc option ) , which uses a variance component framework to account for genetic correlation across family members [35]–[36] . Comparison of imputed genotypes with experimental genotypes , carried out on a set of 1 , 097 individuals that were genotyped with the 6 . 0 Affymetrix Arrays ( unpublished data ) , showed that the average per genotype error rate between imputed and experimental genotypes was 3 . 7% and 4 . 1% for imputations based on 1000 Genomes and Sanger haplotypes , respectively . In the Finnish and Norwegian studies we applied a similar strategy to analyze variants ( rs547235 and rs562338 on APOB , rs2479415 on PCSK9 and rs429358 on APOE ) that were not included on Metabochip . We defined a set of reference haplotypes of the 60 HapMap CEU founders by merging genotypes from the 1000 Genomes project and those from our Sanger sequencing , using SNPs located +/−2 Mb of APOB , PCSK9 and APOE . We then used this reference panel to carry out imputation and successively used imputed dosages for testing association with LDL-C . Association analysis was performed using the same trait transformation and covariates as in the SardiNIA study . Imputation and association tests were performed separately for Finnish diabetics ( N = 1 , 742 ) , Finnish non-diabetics ( N = 5 , 678 ) , Norwegian diabetics ( N = 1 , 171 ) and Norwegian non-diabetics ( N = 1 , 436 ) . Results were then meta-analyzed using an inverse-variance method , which combines p-values from each study using weights proportional to the variance of the beta coefficient ( effect ) ( Table S7 ) . A combined estimate of allele frequencies was obtained using the same weights . We evaluated the variance explained by a set of markers by including all of them into the linear model in addition to the clinical covariates ( age , age squared , gender ) , and by subtracting the variance explained by this model versus the basic model ( only clinical covariates ) . Analyses were performed using the lmekin function in R kinship package which uses a variance component framework to account for genetic correlation across family members . In particular , since variance is not purely additive across loci , heritability in Table 2 has been calculated using all 8 SNPs ( or 5 SNPs ) in the model rather than adding values observed at specific loci ( Table 1 ) . For the Finnish and Norwegian samples , the LDL-C variance explained was calculated in each study group separately , and a combined estimate was calculated by weighting each study according to its sample size ( Table 2 ) . We conducted conditional analyses to test for residual associations after accounting for a key SNP . The procedure consists of adding a SNP into the regression model as covariate and testing the effect of another SNP . Specifically , we performed this analysis by adding the strongest associated variant ( key SNP ) as covariate in order to test 1 ) whether that variant could explain the GWAS association signal; and 2 ) if additional independent signals were present . For the latter analysis , a threshold of P<1×10−4 was used to declare significance , corresponding to a Bonferroni threshold for 500 independent tests . A graphical representation of association results from the conditional analysis is shown in Figure 1B , 1D , 1F and in Figure S2B and Figure S2D . MACH software: http://www . sph . umich . edu/csg/abecasis/mach/; HapMap project: http://www . hapmap . org/; 1000 Genomes Project: http://www . 1000genomes . org/; 1000 Genomes Haplotypes for imputation: http://www . sph . umich . edu/csg/abecasis/MACH/download/1000G-2010-06 . html; Locus Zoom: http://csg . sph . umich . edu/locuszoom/ R kinship package http://cran . r-project . org/web/packages/kinship/index . html
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Despite the striking success of genome-wide association studies in identifying genetic loci associated with common complex traits and diseases , much of the heritable risk for these traits and diseases remains unexplained . A higher resolution investigation of the genome through sequencing studies is expected to clarify the sources of this missing heritability . As a preview of what we might learn in these more detailed assessments of genetic variation , we used sequencing to identify potentially interesting variants in seven genes associated with low-density lipoprotein cholesterol ( LDL-C ) in 256 Sardinian individuals with extreme LDL-C levels , followed by large scale genotyping in 5 , 524 individuals , to examine newly discovered and previously described variants . We found that a combination of common and rare variants in these loci contributes to variation in LDL-C levels , and also that the initial estimate of the heritability explained by these loci doubled . Importantly , our results include a Sardinian-specific rare variant , highlighting the need for sequencing studies in isolated populations . Our results provide insights about what extensive whole-genome sequencing efforts are likely to reveal for the understanding of the genetic architecture of complex traits .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome-wide",
"association",
"studies",
"genome",
"sequencing",
"genomics",
"heredity",
"genetics",
"biology",
"quantitative",
"traits",
"genetics",
"and",
"genomics",
"complex",
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] |
2011
|
Fine Mapping of Five Loci Associated with Low-Density Lipoprotein Cholesterol Detects Variants That Double the Explained Heritability
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The integration of experimental data into genome-scale metabolic models can greatly improve flux predictions . This is achieved by restricting predictions to a more realistic context-specific domain , like a particular cell or tissue type . Several computational approaches to integrate data have been proposed—generally obtaining context-specific ( sub ) models or flux distributions . However , these approaches may lead to a multitude of equally valid but potentially different models or flux distributions , due to possible alternative optima in the underlying optimization problems . Although this issue introduces ambiguity in context-specific predictions , it has not been generally recognized , especially in the case of model reconstructions . In this study , we analyze the impact of alternative optima in four state-of-the-art context-specific data integration approaches , providing both flux distributions and/or metabolic models . To this end , we present three computational methods and apply them to two particular case studies: leaf-specific predictions from the integration of gene expression data in a metabolic model of Arabidopsis thaliana , and liver-specific reconstructions derived from a human model with various experimental data sources . The application of these methods allows us to obtain the following results: ( i ) we sample the space of alternative flux distributions in the leaf- and the liver-specific case and quantify the ambiguity of the predictions . In addition , we show how the inclusion of ℓ1-regularization during data integration reduces the ambiguity in both cases . ( ii ) We generate sets of alternative leaf- and liver-specific models that are optimal to each one of the evaluated model reconstruction approaches . We demonstrate that alternative models of the same context contain a marked fraction of disparate reactions . Further , we show that a careful balance between model sparsity and metabolic functionality helps in reducing the discrepancies between alternative models . Finally , our findings indicate that alternative optima must be taken into account for rendering the context-specific metabolic model predictions less ambiguous .
Genome-scale metabolic models ( GEMs ) have proven instrumental in characterizing the activity of metabolic pathways in different biological scenarios . The activity of all metabolic reactions is specified by the flux distribution , which can be readily inferred from GEMs through the usage of constraint-based approaches [1 , 2] . Such approaches often infer fluxes as solutions to a convex optimization problem in which an objective function is optimized under specified constraints . Two types of constraints can generally be considered: The first is due to the stoichiometry , thermodynamic viability ( i . e . , if a reaction is irreversible or reversible under normal physiological conditions ) and mass-balance conditions . These constraints are included in every constraint-based approach . The second type comprises constraints specific to each approach , and usually reflects the context-specific knowledge or data to be integrated . Flux distributions which satisfy the set of constraints are called feasible . A convex optimization problem is guaranteed to render a unique optimal value [3] . However , it is not always guaranteed that there is a unique flux distribution realizing the optimal objective value , leading to alternative optimal flux distributions . Indeed , such a space of alternative optima arises even in the case of flux balance analysis ( FBA ) , as a classical representative of constraint-based approaches [4–9] . Experimental systems biology studies have generated comprehensive atlases of transcript , protein , and metabolite levels from different context , such as: cell types , developmental stages , and environments , across different species from all kingdoms of life [10–15] . Analyses of these data sets have already pointed that context-specific differences in the levels of molecular components often affect the activity of metabolic pathways . Additionally GEMs allow constraint-based approaches to integrate such data sets through the so-called gene-protein-reaction rules , which relate metabolic reactions with the enzymes involved and their coding genes [16–19] . These approaches address two aims: ( i ) obtaining context-specific flux distribution and ( ii ) determining context-specific GEMs; we refer to the respective approaches as flux- and network-centered , respectively . Alternative optima may also result from the integration of context-specific data . In both settings , the existence of alternative optima leads to ambiguity in context-specific flux distributions and/or network reconstructions , since alternative solutions may substantially differ . This is particularly important in the case of context-specific network reconstructions , where further investigations conducted on a single optimal network could lead to erroneous conclusions . To our knowledge , only three studies considered the space of alternative optimal solutions arising from flux-centered approaches: The approach termed iMAT [20] proposed a procedure to classify the flux state of reactions into active , inactive or uncertain across the alternative optima space . Another approach , abbreviated as EXAMO [21] , later used the set of active reactions obtained from the iMAT alternative optima space as input to the approach referred to as MBA [22] , a network-centered method , to reconstruct a context-specific network . Additionally , the Flux Variability Sampling [23] was used to sample the alternative space of flux values that are equidistant to the data integrated . Finally , we note that alternative optimal context-specific models have not been recognized in the case of network-centered approaches , and currently , there is no available method for their analysis . In the present study , we propose a method to quantify the variability of alternative optimal flux values of a flux-centered approach . Additionally , we quantify the effect in the alternative optima of including an additional constraint in the flux values , minimize the total sum of absolute flux values , which has been proposed to obtain unique solutions in a flux-centered method [24] . Furthermore , we investigate , for the first time , the space of alternative optimal context-specific models that arise from several network-centered approaches , and analyze the potential impact on further metabolic predictions and biological conclusions drawn . The study is organized in two parts . The first part is dedicated to explaining the mathematical and computational logic of both ( i ) the context-specific data integration approaches herein evaluated , and ( ii ) the methods that we propose to analyze the respective alternative optima . The second part presents the findings obtained from applying the previously described methods to two particular case studies: a leaf-specific reconstruction from the model plant Arabidopsis thaliana , and a human liver reconstruction . This second part serves as an illustration of the impact that alternative optima have in context-specific metabolic reconstructions , and may be followed independently from the first part—which is primary addressed to the specialized reader .
In this section , we present the mathematical formulation of the computational methods that we developed to investigate the alternative optima of three selected data integration approaches . In all three cases , we first provide an overview of the approach , which is followed by a description of the method to explore its alternative optima space . We start by a representative of a flux-centered approach—a modified version of RegrEx [25]—and the method that we propose to explore its alternative optima , termed RegrEx Alternative Optima Sampling ( RegrExAOS ) . We then focus on Core Expansion ( CorEx ) , also developed in this study , which we take as representative of a network-centered approach . In addition , we show how the optimization program behind CorEx can be adapted to evaluate not only its alternative optima space , but that of FastCORE [26] and CORDA [27] , two state-of-the-art network-centered approaches . Given a GEM and ( context-specific ) gene or protein expression data , the Regularized metabolic model Extraction ( RegrEx ) method reconstructs a context-specific metabolic model , along with the corresponding flux distribution . To this end , RegrEx finds a feasible flux distribution that is closest to a given experimental data set , and is , therefore , considered a flux-centered approach . The original RegrEx approach relied on a regularized least squares optimization in which the Euclidean distance between the given gene expression data vector , d , and a feasible flux distribution , v , i . e . , the squared ℓ2 norm of the difference vector ϵ = d – v , was minimized [25] . The regularization was implemented by also considering the ( weighted ) ℓ1 norm of v in the minimization problem , as a means to select the reactions in the GEM that are most important for a given metabolic context . However , here we used a slightly modified version of RegrEx: Instead of minimizing the sum of square errors , we minimize the sum of absolute errors , i . e . , the ℓ1 norm of ϵ . Except for this substitution , the modified RegrEx version , called RegrExLAD ( for Least Absolute Deviations ) , follows the same formulation as the original RegrEx ( see S1 Appendix for detailed comparison ) . The minimization problem behind RegrExLAD considers a set of constraints required to handle reversible reactions: In this case , absolute flux values must be taken into account when minimizing the distance to the ( non-negative ) associated gene expression ( i . e . , for a reversible reaction i , ϵi = |vi|–di ) . This is accomplished by splitting reversible reactions into the forward and backward directions , each constrained to have non-negative flux value , and introducing a vector of binary variables , x , to select only one of them during the optimization . Altogether , these particularities are captured in the mixed integer linear program ( MILP ) , vopt=argminϵ+=[ϵirr+;ϵfor+;ϵback+] , ϵ−=[ϵirr−;ϵfor−;ϵback−] , v=[virr;vfor;vback]∈ℝ0+ , x∈{0 , 1}nwT ( ϵ++ϵ− ) +λ||v||1s . t . 1 . Sextv=02 . virr i+ ( ϵirr+−ϵirr− ) =dirr3 . vfor i+ ( ϵfor+−ϵfor− ) +xdrevRxns=drevRxn4 . vrev i+ ( ϵback+−ϵback− ) −xdrevRxn=0} , i∈RD 5 . virrmin≤virr≤virrmax6 . vfor+xvformin≥vformin7 . vback−xvrevmin≥08 . vfor+xvformax≤vformax9 . vback−xvrevmax≤0 ( OP1 ) In OP1 , the flux distribution , v , is partitioned into the sets of irreversible ( virr ) , and reversible reactions proceeding into the forward ( vfor ) and backward directions ( vback ) , and the ( reaction ) columns of the stoichiometric matrix , Sext , are ordered to match the partition of v . In addition , the components of the error vector , ϵi = ϵ+i−ϵ–i , ϵ+i , ϵ–i ≥ 0 , are split into two non-negative variables , ϵ+i , ϵ–i , as a way to computationally treat the otherwise required absolute values of the components of ϵ . Thus , the ℓ1 norm ||ϵ||1 = Σi |ϵi| is replaced by ϵ+i + ϵ–i in the objective function . ( ϵ is defined only over the set of reactions with associated data , RD in OP1 ) . Finally , the λ parameter corresponds to the weight of the ℓ1 norm in the objective function , and is chosen during the optimization as to maximize the Pearson correlation between data and flux values [25] . The convexity of OP1 guarantees finding the minimum distance between experimental data and a feasible flux distribution that is allowed by the constraints . However , it does not guarantee that the resulting flux distribution is the only feasible one that is optimal with respect to a particular context-specific data . This variability in optimal flux distributions may be attributed to two factors . On the one hand , as mentioned above , not all reactions in a GEM are typically associated to data . In contrast to data-bounded reactions , there is a set of data-orphan reactions comprising non-enzymatically catalyzed reactions , reactions without gene-protein annotation or without associated data for a particular context . Data-orphan reactions do not contribute to the error norm in the RegrExLAD objective function , described in OP1 , and their flux value can vary as long as v satisfies the imposed constraints and its ℓ1 norm is preserved . This situation is depicted in Fig 1 , where the search for a flux distribution v that is closest to the data vector , d , is carried out in the projection of the flux cone , F = {v: Sv = 0 , vmin ≤ v ≤ vmax} , where d resides . On the other hand , the geometry of F may preclude certain reactions to obtain an exact match with the data value , when d remains outside the projection of F . In this case , a set of flux distributions may be equidistant to d , thus generating variability also in the optimal flux value of data-bounded reactions . The general approach followed by RegrExAOS , depicted in Fig 2 , is similar to the Flux Variability Sampling [23] ( here adapted to RegrExLAD , see S1 Appendix ) . RegrExAOS first creates a random flux vector , vrand , which is bounded by the maximum and minimum flux values previously calculated by Flux Variability Analysis ( using only upper and lower bounds as constraints , see Methods ) . It then searches for the closest flux vector , v , to vrand that belongs to the alternative optima space , i . e . , it is at the same distance to the data vector , d , and has the same ℓ1 norm as the previously calculated RegrExLAD optimum . This is performed by solving the MILP given in OP2: minϵ+=[ϵirr+;ϵfor+;ϵback+] , ϵ−=[ϵirr−;ϵfor−;ϵback−] , δ+=[δirr+;δfor+] , δ−=[δirr−;δfor−] , v=[virr;vfor;vback]∈ℝ0+ , x∈{0 , 1}n||δ++δ−+δback||1s . t . 1−9 ( OP1 ) 10 . ϵ++ϵ−=ϵopt++ϵopt−11 . ||v||1=||vopt||112 . virr− ( δirr+−δirr− ) =vrand ( irr ) 13 . vfor− ( δfor+−δfor− ) −xvrand ( revRxn ) =014 . −vback+δback+xvrand ( revRxn ) =vrand ( revRxn ) ( OP2 ) Finally , RegrExAOS iterates this routine n times to obtain a sufficiently large sample; here we used n = 2000 , which is sufficient sample size for the subsequent statistical analyses . OP2 inherits constraints 1–9 from OP1 and incorporates two sets of new constraints . Constraints 10 and 11 are added to guarantee that v renders the same similarity to data and the same ℓ1 norm of the previously found RegrExLAD optimum , vopt , respectively . In addition , constraints 12–14 introduce the auxiliary variables δirr , δfor and δback quantifying the distance of an optimal flux distribution to the randomly generated vrand . More specifically , δirr ( i ) = δ+irr ( i ) −δ–irr ( i ) = vrand ( i ) −virr ( i ) , i ∈ IR , acts over the set of irreversible reactions ( IR ) and δfor ( i ) = δ+for ( i ) −δ–for ( i ) = vrand ( i ) −vfor ( i ) , δback ( i ) = vrand ( i ) −vback ( i ) , i ∈ RR , over the set of reversible reactions ( RR ) . Note that both δirr , δfor , are defined as the difference of two non-negative components , which enables us to formulate a linear objective function that renders OP2 computationally tractable . In contrast , δback does not require this treatment since it always takes non-negative values ( see Fig 2 ) . This is because in OP2 , the stoichiometric matrix , S , corresponding to the GEM is first modified in the following way: we change the sign of the columns , as well as the entry in vrand , corresponding to reversible reactions that were randomly assigned a negative flux value in vrand . In this manner , all reversible reactions in vrand operate in forward direction ( i . e . , are non-negative ) which facilitates the optimization process . In addition , δfor and δback are constrained to be mutually exclusive by the same binary variable , x , introduced to select only one of the directions in reversible reactions ( i . e . either forward or backward ) . In this manner , OP2 will select the direction of reversible reactions that minimizes the overall distance to vrand . Finally , reversible reactions whose sign was originally changed in vrand are altered back to their original directions and their sampled flux values are modified accordingly . In this section , we analyze the alternative optimal solutions of CorEx , a method that we designed in this study to represent the network-centered approaches . In a general sense , network-centered approaches first partition the set R = C∪P of reactions in the original GEM into a core set , C , that must be present in the final context-specific model , and a non-core set , P , which does not necessarily have to be in the final model . These approaches find then a subset PA⊆ P of non-core reactions that renders C consistent , i . e . , all reactions in the core are able to carry a non-zero flux in at least one steady-state solution . The final context-specific subnetwork is then defined as RA = C∪PA . Some approaches , like MBA [22] , mCADRE [28] and FastCORE [26] , aim at minimizing the size of PA , as to obtain a parsimonious final model . In contrast , CORDA [27] relaxes the parsimony condition as a way to prevent eliminating important reactions for a given context . In this respect , CorEx aims at obtaining a parsimonious model , although , as shown in the following , it can be easily adapted to allow increasing the size of PA if desired . CorEx follows the MILP displayed in OP3 , which minimizes the number of reactions with non-zero flux in P while constraining all reactions in the core to carry at least a small positive flux ( ϵ in constraints 2–3 ) . This is achieved by minimizing the norm ( Z in OP3 ) of the vector , x , of binary variables ( constraints 4–7 ) which selects the set PA that renders the MILP feasible . Note that the selected non-core reactions are forced to carry a small positive flux ( constraints 5 , 7 ) to guarantee that they are active in the final context-specific model . Finally , like in RegrEx , reversible reactions are split into the forward and backward directions , to operate only with non-negative flux values . In addition , another vector of binary variables , y in constraints 8–9 of OP3 , is introduced to select the direction of reversible reactions ( i . e . , imposing vfor > XOR vback > 0 , when the reaction is selected to be active ) . To identify alternative optimal CorEx extracted networks , we developed the MILP displayed in OP4 . The general idea behind OP4 is to find the most dissimilar context-specific network , RA* = C∪PA* , to a previously found optimal RA , that maintains the set C consistent . Namely , it maximizes the number of differences between the reactions contained in PA and PA* . Note that OP4 inherits constraints 1–9 from OP3 , and incorporates three new constraints . Constraint 10 guarantees that the cardinality of PA* equals that of the previous optimal PA in OP3 . Constraint 11 introduces two additional binary variables , δ+ , δ– , which measure the mismatches between the vectors x , selecting the reactions in PA* , and the optimal vector xopt , selecting the reactions in PA and previously found by OP3 . Finally , constraint 12 is added to impose a δ+ XOR δ− relationship to avoid the trivial optimal solution in which δ+ = δ– , maxv=[virr;vfor;vback]∈ℝ0r+ , x=[xirr;xrev] , δ+ , δ−∈{0 , 1}Py∈{0 , 1}rev ||δ++δ−||1s . t . 1−9 . ( OP3 ) 10 . || x||1=Z11 . x+δ+−δ−=xopt12 . δ++δ−≤1 ( OP4 ) However , besides CorEx , OP4 can be used to generate alternative optimal networks to other network-centered approaches . We just need to set xopt , in constraint 11 , to be the optimal x vector of the particular approach under study; in addition , we need to update Z , in constraint 10 , to the corresponding number of non-core reactions added by this approach ( i . e . , the size of PA ) . Note that xopt can be easily constructed from the set PA , which is derived from a particular context-specific model . In addition , a similar constraint to the constraint 10 of OP4 , namely ||x||1 ≥ Zlb , may be included in OP3 , as a lower bound to its objective function , where Z* ≤ Zlb ≤ R , and Z* is the unconstrained optimum of OP3 . It is in this manner that CorEx allows relaxing the parsimony condition , as commented before , although in this study we did not constrain the CorEx optimum . Noteworthy , the main advantage of using OP4 to obtain alternative optimal networks lies in its MILP formulation . This is because , with the exception of CorEx , which also relies on a single MILP , all existing network-centered approaches require iteratively solving a convex optimization problem . For instance , the linear programs behind the consistency testing step of FastCORE [26] , or the ones behind the flux balance analysis , iterated over each reaction of the GEM , in CORDA [27] . Alternative optima may arise in each one of these iterations , thus exploring the alternative optima space in each case would require an extensive computational effort . In contrast , we circumvent this problem with OP4 by analyzing the alternative solutions of a single MILP . However , OP4 only generates a single , maximally different , alternative optimal network . To generate a sample of alternative networks , here we applied OP4 in an iterative way . We first used OP4 to obtain a maximally different network to a given optimal context-specific network , and then repeated this process of feeding OP4 with the successively generated alternative networks until no additional one was found . At that point , we randomly perturbed the last network by changing the state ( active or inactive ) of 1% of the reactions , and repeated this process until no additional network was found ( an implementation of the procedure is provided in S1 File ) . We note that with this iterative process , which we term the AltNet procedure , we do not guarantee an exhaustive enumeration of all maximally different alternative networks . However , as shown in the next section , it sufficed to illustrate the variety found across optimal context-specific extracted networks in this study . Finally , we use the AltNet procedure to analyze the alternative optima space of CorEx , FastCORE and CORDA . In the latter case , however , OP4 had to be slightly modified . The reason for the modification is that CORDA divides the reactions in the GEM into four categories , in contrast to CorEx and FastCORE , where only the core , C , and the non-core set , P , are considered . Concretely , reactions are separated into three groups based on experimental evidence: reactions with high ( HC ) , medium , ( MC ) and negative ( NC ) confidence , and an additional group collecting the remaining reactions ( OT ) in the GEM , for which experimental evidence is not available . In this case , the group HC corresponds to the core set of reactions ( i . e . , all reactions in HC must be included in the final model ) , and the other three groups constitute the non-core set P , although reactions in MC are preferentially added over NC and OT reactions . To account for the different reaction groups , we partitioned the vector x in OP4 into the sets of MC , NC and OT reactions , and evaluated constraint 10 for each of the three sets . In this manner , we guaranteed that an alternative optimal network contained , besides all HC reactions , the same number of MC , NC and OT reactions than the original CORDA optimum . Here , we illustrate the ambiguity found during the extraction of context-specific flux distributions and metabolic networks due to the alternative optima . To this end , we apply the methods described in the previous section to two case studies: a leaf-specific scenario , the model plant Arabidopsis thaliana , and a human , liver-specific reconstruction . In the first case , we used the AraCORE model , which includes the primary metabolism of Arabidopsis thaliana [29] , and a leaf-specific gene expression data set , obtained from [30] ( Methods ) . In the second case , we employed Recon1 , a well-established human metabolic model [31] . Moreover , we considered two different core sets of reactions that were identified as liver-specific by experimental evidence ( taken from [19] and [20] ) , and upon which the liver reconstructions were built . In addition , we reduced the original metabolic models by taking only the consistent part of them . The resulting models are termed here Recon1red and AraCOREred , and contain a total number of 2469 and 455 reactions , respectively ( see Methods for details ) . We first analyzed the alternative optima space of RegrExLAD—as a representative of a flux-centered approach—and evaluated the ability of the ℓ1-regularization of RegrExLAD to reduce this space . To this end , we focused on the leaf-specific scenario; however , we also applied these methods to the liver-specific scenario , to verify if our main conclusions held in the case of a larger genome-scale model . We then applied CorEx , a network-centered representative , to extract and analyze the alternative optima for the leaf- and the liver-specific reconstructions , and compare its performance with that of FastCORE [19] , a well-established approach . In addition , we evaluated the alternative optimal liver-specific networks generated by CORDA , a recently published approach [20] . Finally , we also investigated the alternative optima of iMAT to the leaf- and liver-specific scenario with both , the original approach proposed in [16] and our own complementary method . After applying RegrExLAD with λ = 0 , we obtained an optimal , leaf-specific flux distribution . We then applied RegrExAOS to evaluate the alternative optima space of the previously obtained optimum . The results from this evaluation confirmed the existence of an alternative optima space for RegrExLAD . However , the variability of the fluxes at the optimal objective value was not uniform across different reactions . As expected , data-orphan reactions exhibited more broadly distributed flux values at the alternative optima than data-bounded reactions . We quantified this property by the Shannon entropy ( Methods ) , as a measure of uncertainty of flux value prediction associated to a data integration problem . In this sense , data-orphan reactions showed a larger mean entropy value of 1 . 64 in comparison to the value of 0 . 95 found for the data-bounded reactions ( one-sided ranksum test , p-value = 1 . 95x10-5 ) . However , we found reactions with particularly low or high entropy values in both sets , data-bounded and data-orphan ( S1 Table ) . This last observation suggests that reactions with low entropy values may be of special importance under the leaf-specific metabolic state . On the other side , high entropy values suggest that the corresponding reactions could operate more freely in the leaf context . For instance , we found that the majority of transport reactions showed large entropy values , in accord with the fact that most transport reactions are data-orphan . Nevertheless , there were some transport reactions with particularly low entropy values , such as: the TP/Pi translocator ( reaction index 327 in AraCOREred , H = 0 . 07 ) interchanging glyceraldehyde 3-phosphate and orthophosphate between the chloroplast and cytoplasm , the P5C exporter ( index 363 , H = 0 . 01 ) exporting 1-Pyrroine-5-carboxylate from mitochondria to cytoplasm and the ADP/ATP carrier ( index 320 , H = 0 . 01 ) , interchanging ATP and ADP also between mitochondria and cytoplasm ( for a comparison , the highest entropy value in the rank is H = 2 . 92 , corresponding to the Proline uniporter , see the complete list in S1 Table ) . Therefore , the leaf data integration constrains these transport reactions to take a small range of different flux values due to the network context in which they operate , since they are not directly bounded by experimental data . This observation is contrasted by the high entropy values that these same three reactions when no experimental data are integrated , i . e . , when a similar sampling procedure is performed in which only mass balance and thermodynamic constraints are imposed ( Methods ) . In this case , all three entropy values are markedly larger ( H > 2 , S1 Table ) . We next focused on the entropy values of reversible reactions in the AraCOREred model . Reversible reactions in a GEM usually correspond to reactions for which no thermodynamic information is available ( leaving aside the set which is known to operate close to equilibrium ) . Therefore , it would be informative to evaluate whether integrating context-specific experimental data in a GEM could be used to fix the direction of such reactions . Interestingly , we found that a large proportion ( 75 . 81% ) of the reversible reactions carrying a non-zero flux ( including data-orphan ) had a fixed direction , either forward or backward , in the alternative optima ( Table 1 ) . This finding indicates that , even though there is variation in the flux value of reversible reactions , integration of expression data can determine their direction in a given context . Therefore , the proposed approach and findings provide valuable information on how metabolism could be operating under the particular condition . For the analyzed sequence of increasing λ-values , the table includes: The sum of entropy values for the subset of data-bounded , HData , and data-orphan , HOrphan , reactions , as well as for all reactions , HTotal , the mean entropy value across all reactions , H¯Total , and the proportion of reversible reactions with fixed direction in the alternative optima sample , FixedRev . We next evaluated the RegrExLAD alternative optima space for a sequence of increasing λ-values . This was motivated to test whether the inclusion of ℓ1-regularization , besides imposing sparsity in optimal flux distributions , could also reduce the variability found in individual reaction flux values across the alternative optima space . This property could serve as a way to decrease the uncertainty , as measured by the Shannon entropy , associated to a context-specific data integration problem . To this end , we first applied RegrExLAD on AraCOREred and the same leaf data set , but using three increasing λ-values ( λ1 = 0 . 1 , λ2 = 0 . 3 and λ3 = 0 . 5 ) . We then applied RegrExAOS to sample the alternative optima space of each of the three RegrExLAD data integrations . We found that the entropy tended to decrease with increasing λ-values , although the effect was more pronounced for the data-orphan reactions ( Table 1 , Fig 3 ) . For instance , the sum of entropy values among data-orphan reactions decreased from a value of HOrphan = 86 . 82 for λ = 0 , to HOrphan = 36 . 50 with λ = 0 . 5 . In contrast , for the data-bounded reactions , it only decreased from a value of 73 . 17 with λ = 0 to 65 . 46 with λ = 0 . 5 , and even led to a transient increase at λ = 0 . 3 ( Table 1 , Fig 3 ) . These findings suggest that the inclusion of regularization can reduce the uncertainty associated to a context-specific data integration problem . Naturally , there is a trade-off between decreasing uncertainty and increasing sparsity of the obtained models , since greater λ-values also produce smaller models that may exclude reactions that are relevant to a particular context ( S1 Fig ) . However , a mild regularization ( λ = 0 . 1 ) already had a substantial effect in reducing the uncertainty of the RegrExLAD data integration in this analysis . Specifically , it decreased the total model entropy , defined as the sum of entropy values over all reactions , by 16 . 54% ( from a value of HTotal = 159 . 99 for λ = 0 , to HTotal = 133 . 52 with λ = 0 . 1 , Table 1 ) . Finally , we focused on the effect that regularization had on reversible reactions . We found that the number of reversible reactions with fixed direction increased monotonically with increasing λ-values ( Table 1 ) . Hence , this finding suggests that a mild regularization can further constrain the direction in which a reversible reaction can proceed under a particular metabolic context . We next analyzed the alternative optima space of RegrExLAD in the liver scenario . Specifically , we focused on evaluating whether the qualitative results obtained in the leaf context remained unchanged when using Recon1red , a larger genome-scale model . To this end , we used a liver-specific and publicly available gene expression data set [32] , and mapped it to the reactions in Recon1red following the same procedure as in the leaf-scenario ( Methods ) . Obtaining samples in a larger model is more challenging , due to the increased computational time required to solve the MILP of OP2 . Therefore , we restricted our sample to 100 random points for each of the four λ-values evaluated here , as to avoid an excessively large computational time ( the total sample time remained under 41 hours , see Methods for details ) . In this case , we observed a general qualitative agreement between the leaf and the liver scenarios throughout the increasing λ sequence ( Fig 3E–3H ) . More specifically , data-orphan reactions showed a monotonic decrease in their median entropy values; however , this effect was less apparent in the case of data-bounded reactions . Specifically , although the total entropy values of data-bounded reactions tended to decreased with increasing λ , with the exception of λ = 0 . 5 ( Table 1 ) , these differences were not significant ( one-sided ranksum test , α = 0 . 05 ) . However , we observed marked differences when looking at the proportion of fixed reversible reactions . In general , this fraction was smaller in the liver scenario , 61 . 78% versus 75 . 81% with λ = 0 ( Table 1 ) , and , in contrast to the leaf case , it did not show an increasing trend with increasing λ-values . We conclude that , while the sample size was smaller than that in the leaf case , these results again suggest that a mild ℓ1-regularization of RegrExLAD can be of help in reducing the ambiguity of context-specific flux values . We first applied CorEx and FastCORE to reconstruct two leaf-specific networks , LeafCorEx and LeafFastCORE . To this end , we used the AraCOREred model and a core set of 91 reactions , which was previously obtained by considering reactions for which the associated gene expression data had a value greater than the 70th percentile ( Methods ) . Both LeafCorEx and LeafFastCORE , contained the core set and were consistent , i . e . , all reactions were unblocked . However , we noticed that LeafCorEx was more compact than LeafFastCORE , containing 236 versus 254 non-core reactions , respectively ( Table 2 ) . We next reconstructed the two liver-specific networks in a similar way . To this end , we used the Recon1red model , and the core set of 1069 reactions defined in the original FastCORE publication [26] . In this case , CorEx added 593 non-core reactions to the core set , obtaining the liver-specific reconstruction LiverCorEx . FastCORE , on the other hand , added 677 non-core reactions to generate LiverFastCORE . Hence , CorEx was able to extract a more compact liver-specific network , resembling the behavior found in the leaf-specific case . After obtaining these context-specific metabolic reconstructions , we searched for alternative optimal networks to all of them , using the AlterNet procedure describe in the previous section . To quantify the uncertainty of the leaf- and liver-specific reconstructions , we looked at the number of reaction mismatches between all pairs of alternative networks in each case ( computed as the Hamming distance , see Methods ) . This metric was normalized by the total number of reactions in each metabolic model to allow fair comparison between the two case studies . This table summarizes the results of the evaluation of the CorEx alternative optima space . It includes the number of added non-core reactions , P , the maximum , MRmax ( within brackets the percentage of reaction in P ) , and the mean number , MR¯ ( CV stands for coefficient of variation ) , of reaction mismatches ( i . e . , Hamming distance ) across the alternative networks for the leaf- and the liver-specific scenarios evaluated by two methods , CorEx and FastCORE . The last column displays the p-value resulted from a one-sided ranksum test comparing the distributions of Hamming distances between any pair of the alternative networks of CorEx and FastCORE ( the null hypothesis states that the distribution generated by CorEx is bigger than that of FastCORE ) . We found marked differences between alternative optimal networks in both approaches and metabolic scenarios . In the case of LeafCorEx , alternative networks differed on average in 29 non-core reactions , with a maximum value of 52 reactions ( 22% of the added non-core reactions ) . In LeafFastCORE , networks differed on average in 66 . 78 reactions , and had a maximum number of 118 discrepant reactions ( 46 . 5% , Table 2 ) . This situation was even worsened in the liver-specific reconstructions . Between alternative networks to LiverCorEx , we found a maximum of 156 discrepant reactions among the 593 in the added non-core ( 26 . 3% ) , with an average of 108 . 3 . In the case of LiverFastCORE , the maximum number of discrepant reactions was as high as 398 out of the 677 ( 58 . 8% ) added non-core reactions , with an average of 246 . 93 between alternative optimal networks ( Table 2 ) . As a complementary analysis , we also determined the frequency of occurrence of every non-core reaction across the alternative optimal networks . In this manner , we could identify: ( i ) a set of non-core reactions that were always included , termed the active non-core set , ( ii ) a set of non-core reactions that were excluded from all alternative networks , termed the inactive non-core set , and ( iii ) a set of non-core reactions that were included in some of the networks , referred to as the variable non-core set . In this case , we took the size of the variable non-core set as a measurement of the uncertainty of a context-specific network extraction; 28% and a 47% of the total non-core reactions were in the variable set in the cases of LeafCorEx and LeafFastCORE . On the other hand , a 12% and a 58% were found in LiverCorEx and LiverFastCORE , respectively ( Fig 4A–4D ) . The previous results quantify the structural differences among the generated alternative optimal networks . However , these structural differences do not consider which kind of reactions ( i . e . , in which pathways in the GEM ) are more or less frequent ( i . e . , ambiguous ) , in the alternative optima space . To address this issue , we assigned a score ( between 0 and 1 ) to each metabolic pathway based on its representation in the active , variable or inactive non-core set . Specifically , the score represents the fraction of reactions of a given pathway that are assigned to a non-core subset with respect to the total size of the non-core set ( Methods ) . Pathways with high score values in the active and inactive non-core are consistently over- and under-represented , respectively , among the alternative optimal networks . Therefore , these pathways should be more important ( the opposite in the inactive non-core case ) to maintain the core active and hence the assumed context-specific metabolic function . In contrasts , pathways with high-score values in the variable non-core tend to be represented only in certain alternative optimal networks , thus being more ambiguous in the context-specific reconstruction . For instance , in the leaf scenario , we found among the pathways with highest score in the active non-core: the Calvin-Benson cycle , light reactions and photorespiration . All of these pathways showed a maximum score value of 1 in both cases LeafCorEx and LeafFastCORE , which agrees with key roles of these pathways in a photosynthetic tissue . Additionally , alongside these photosynthetic pathways , we also found housekeeping pathways for the synthesis of AMP , CTP , GMP , UMP , Acetyl-coA or Fatty acid , among others , with the maximum score value in both cases . More interestingly , among the pathways with the highest scores in the variable set we also found primary pathways like the Tricarboxylic acid cycle , Alanine synthesis , the Pentose Phosphate Pathway and Pyruvate metabolism . However , we also found pathways that are usually linked to active photosynthetic tissues like Starch and sucrose degradation and sucrose synthesis ( see S9 Table for a complete list containing the ranked pathways ) . Moreover , in the liver scenario , we also found typical liver-specific pathways like Cholesterol Metabolism and Fatty acid oxidation [33] with the maximum score value in the active non-core in the case of LiverCORDA . However , we also found a variety of other pathways with high scores in the variable non-core like CoA catabolism , ROS detoxification or Vitamin A metabolism , which indicates that the variable non-core set contains a diverse set of metabolic functions that may be important to the canonical liver physiology ( see S9 Table for a complete list of the ranked metabolic pathways ) . Finally , we analyzed the alternative optima space of CORDA , a recently published network-centered approach [27] . As explained in the previous section ( Computational methods ) CORDA differs to CorEx and FastCORE in two ways . On one hand , CORDA does not aim at obtaining compact or parsimonious models , but rather emphasizes the metabolic functionality of the final context-specific reconstructions . On the other hand , CORDA considers four groups of reactions based on experimental evidence , out of which only one , the high confidence core set ( HC ) , has to be fully included in the final model ( thus being equivalent to the core set of CorEx and FastCORE ) . In this case , a suitable alternative optimal network must contain not only the entirety of the HC set , but exactly the same number of reactions added by CORDA in each one of the three remaining groups: the medium ( MC ) and the negative confidence ( NC ) groups , and the reactions without experimental data ( OT ) . Therefore , it is reasonable to expect that this additional constraint may reduce the uncertainty of the CORDA reconstructions . To test this idea , we searched for alternative networks to the CORDA liver reconstruction ( here LiverCORDA ) provided in [27] . LiverCORDA was obtained from Recon1 and experimental evidence from the Human Protein Atlas [13] , and contains 279 HC , 369 MC , 11 NC and 1147 OT reactions . We used again our AltNet procedure , Recon1red ( since blocked reactions , by definition , can never be included in a final network ) , and the classification of the reactions in the four groups also provided in [27] . We were indeed able to find alternative networks to the original LiverCORDA with marked differences among them . Concretely , a maximum number of 992 discrepant reactions between two alternative networks , out of the total 1527 distributed among the MC , NC and OT groups ( 65% , Table 2 ) , with a mean number of 545 . 22 . Similarly , 51% of the non-core reactions ( MC , NC and OT ) in Recon1red were assigned to the variable non-core set ( Fig 4E ) . The examples presented here show that the context-specific reconstructions are more ambiguous than specific , especially in the human liver scenario . This latter case is of special concern , given the implications of obtaining accurate context-specific reconstructions in biomedical research . In fact , most , if not all , of the network-centered approaches have focused on human metabolism [22 , 26–28] . There are ways , however , to cope with this ambiguity or uncertainty of context-specific reconstructions . For instance , as commented before , CORDA aims at obtaining functional reconstructions . In fact , the authors in [27] tested the capability of the LiverCORDA model to conduct a basic set of liver metabolic functions , including aminoacid , sugar and nucleotide metabolism . We evaluated the alternative LiverCORDA models with the same metabolic test ( Methods ) , and extracted the subset that passed it . Among these networks , we found that the number of discrepancies and the size of the variable non-core were significantly reduced , as compared to the total set of alternative networks ( Table 2 , Fig 4E and 4F ) . This is not surprising , since requiring the alternative networks to fulfill certain metabolic functions indirectly imposes an additional constraint to the optimal solution . On the other hand , this additional constraint can also be realized by augmenting the core set , as to guarantee that certain key reactions are present in the final context-specific network . This relates to an additional way to reduce the ambiguity of the reconstruction . In the case studies evaluated here , we found that the CorEx alternative networks tended to be more similar among each other than that of FastCORE or CORDA , as quantified by the ( normalized by non-core size ) number of discrepancies ( Table 2 ) . These differences may be explained by the number of non-core reactions included in the optimum: CorEx obtained more compact models than FastCORE in the Leaf- and the Liver-specific case . This imposes a more stringent constraint when searching for alternative optimal networks . However , there is a tradeoff between model parsimony and functionality . In fact , the LiverCorEx model was not able to pass the metabolic function test , while LiverFastCORE was able to pass it . In this particular case , LiverCorEx did not contain the 9 basal exchange reactions ( Methods ) required to perform the metabolic functions in the test . However , including these 9 reactions in the liver core set sufficed to generate a LiverCorEx model that passed the test . The analysis of the alternative optima space can be employed to cope with the ambiguity of a context-specific network reconstruction . Notably , the authors of EXAMO ( EXploration of Alternative Metabolic Optima ) [21] proposed a first step in this direction . In this case , EXAMO first generates a sample of alternative optimal flux distributions of iMAT [20] . It then focuses on the activity state of each reaction across the sample , for which it binarizes the flux values through the usage of an arbitrary threshold value . A reaction is included in the High Frequency Reaction ( HFR ) set if it is active throughout the alternative optima sample . Finally , EXAMO uses the HFR set as a core set to MBA [22] , a network-centered method , which reconstructs the minimal network that renders the HFR set consistent . EXAMO directly addresses the problem of alternative optima . However , the final context-specific model is again subject to the effects of alternative optima , since a set of alternative networks , all containing the HFR set as a core , could be found for the MBA method . A possible way to circumvent this problem in the case of iMAT could be the following: i ) similar to EXAMO , obtain samples of alternative optimal flux distributions , binarize flux values and rank the reactions according to the number of times that they appear as active in the sample , ii ) include the reactions that are always active ( the HFR set ) in a core set and the rest in a non-core set , and iii ) , add non-core reactions in decreasing order of frequency until consistency of the core is reached . In this manner , this ranking provides a way to select which non-core reactions are included in the final model . This idea parallels that of mCADRE [28] , although in the latter , reactions are ranked following an heuristic approach that considers experimental evidence from several databases , which may be difficult to obtain for certain metabolic contexts . Finally , to generate the sample of alternative optima flux distributions of iMAT , we propose a sampling method similar to RegrExAOS that allows drawing arbitrarily large samples , as opposed to the one used in EXAMO which generates samples of restricted size . Details about this method , here called iMATAOS , can be found in S2 Appendix . In the case of the network-centered approaches here evaluated , establishing a ranking of non-core reactions could also be a way to deal with the ambiguity during network reconstructions . Non-core reactions that occur with high frequency in the alternative optima space should be preferentially included in the final network , while reactions with a low frequency should be discarded . To guarantee that the final network is consistent ( i . e . the core set is active ) , non-core reactions could be again added in decreasing order of frequency to the core set until consistency is reached . Naturally , this requires the development of competent methods to sample the alternative space of network-centered approaches . In this sense , we consider our proposed AltNet procedure a first step towards this goal . We analyzed the space of alternative optima resulting from the integration of context-specific data into GEMs . To this end , we evaluated a representative set from the flux- and network-centered approaches . We selected RegrEx [25] as a representative of flux-centered approaches and CorEx , as a network-centered approach , proposed in this study . In addition , we adapted CorEx to obtain alternative optimal networks for FastCORE [26] and CORDA [27] , two state-of-the-art network-centered approaches . We compared the developed approaches and implemented tools on two illustrative case studies: ( i ) a medium size GEM of the primary metabolism of Arabidopsis thaliana [29] and a leaf-specific gene expression data set [30] , and ( ii ) a larger GEM collecting a reconstruction of a human metabolic network [31] , two liver-specific core sets of reactions [26 , 27] and a liver-specific gene expression data set [32] . Our findings demonstrated the existence of a space of alternative optima for all evaluated approaches integrating context-specific data . Consequently , this space of alternative optima induces ambiguous context-specific reconstructions . In the case of flux-centered approaches , RegrExLAD in this study , we proposed the usage of a mild regularization to mediate the uncertainty of the resulting context-specific fluxes . In network-centered approaches , our results showed the existence of markedly disparate alternative context-specific networks in CorEx , FastCORE and CORDA . A delicate balance between model parsimony and metabolic functionality seems key to reducing the ambiguity of the context-specific reconstructions . Additionally , an evaluation of the alternative optima space followed by a ranking of the reactions according to their frequency may serve as a way to determine their context-specificity . On this line , we proposed the AltNet procedure to generate alternative optimal context-specific networks . As a concluding remark , we acknowledge the utility of the existent experimental data integration methods , since they allow a fast and automated generation of context-specific flux distributions and metabolic networks . However , our findings indicated that the interpretation and further usage of their results warrant caution . Specially , since the existence of alternative optima is likely linked to the nature of the context-specific data integration problem , and thus is independent of the approach used . The latter claim is supported by our evaluation across qualitatively different approaches . We advocate the view that an analysis of alternative optimal solutions should be performed , whenever possible , if context-specific data are integrated in metabolic models . In the case of context-specific networks reconstructions , more reliable results could be obtained from subsequent careful knowledge-based curation .
All optimization programs used in this study , ( i . e . , OP1-6 ) were implemented in MATLAB and solved using Gurobi ( version 7 . 1 ) [34] on a desktop machine with an Intel Core i7-4790 @3 . 6 GHz processor and 16GB of RAM . We used default Gurobi parameter values except for: i ) reduced feasibility tolerance to 10−9 when solving OP3-4 , ii ) increased MIPGap parameter to 1% when solving the MILP of OP2 . All generated code with the implementations is available as Supplementary Information . A reduced version of the original AraCORE model [29] was used in this study: AraCORE contains 549 reactions and 407 metabolites assigned to four subcellular compartments , whereas the herein used version ( AraCOREred ) contains 455 reactions and 374 metabolites . The reactions that were removed correspond to exchange reactions that directly connect organelles to the environment ( circumventing the cytoplasm ) , and were eliminated to avoid bias in the obtained flux distributions . AraCOREred can be found in the Supplementary Material . Leaf-specific gene expression values were taken from [30] , stored in the GEO database under the accession numbers GSM852923 , GSM852924 and GSM852925 corresponding to Arabidopsis thaliana Col-0 lines with no treatment . The corresponding CEL files were normalized using the RMA ( Robust Multi-Array Average ) method implemented in the affy R package [35] . In addition , probe names were mapped to gene names following the workflow described in [36] , where probes mapping to more than one gene name are eliminated . Gene expression values were then scaled to the maximum value and mapped to reactions in the AraCOREred model following the included Gene-Protein-Reaction rules and a self-developed MATLAB function , mapgene2rxn , which is available in S1 File . This process was repeated for the three samples in the dataset and mean values were taken as representative values to obtain the final leaf-specific data used in this study . Liver-specific gene expression values were obtained from [32] , which is accessible under: http://medicalgenomics . org/rna_seq_atlas/download . In this case , we used the RPKM values corresponding to the liver ( normal tissues ) . Since the RPKM values are already normalized we used them directly as input of the mapgene2rxn procedure , already described . We removed blocked reactions from the original Recon1 model to get the Recon1red model used in this study . To this end , we performed a Flux Variability Analysis ( see next section ) and removed reactions with a maximum absolute flux , |vi| < 10−6 . The Flux Variability Analysis was implemented in the MATLAB function reduceGEM which also extracted the reduced model , Recon1red , in a COBRA compatible MATLAB structure . The function is available in S1 File . The minimum and maximum allowed values of each reaction in AraCOREred were determined through Flux Variability Analysis [4] . Although only the mass balance and the thermodynamic constraints were imposed ( i . e . , no reaction was forced to take a fraction of a previously calculated optimal value ) . This was accomplished through the following linear program , min/max vvi , ∀i∈vs . t . Sv=0vmin≤v≤vmax , which was implemented in MATLAB and solved with the Gurobi solver ( version 6 . 04 ) . The own-developed MATLAB function can be found in Supplementary Material under the name of FVA . To evaluate to what extent the Leaf data integration affected the entropy values of the reactions in the AraCOREred model , we also sampled the space of feasible flux distributions ( i . e . , the flux cone ) when no experimental data was been integrated . To this end , and to allow direct comparability of the results , the flux cone was sampled following a similar approach as in RegrExAOS: first , we generated a random vector of flux values , vrand , within the minimum and maximum values obtained by regular Flux Variability Analysis . The closest flux vector v to vrand within the flux cone was then obtained by minimizing the Euclidean distance between the two vectors . The following quadratic program was used to this end: minv12‖v−vrand‖22s . t . Sv=0vmin≤v≤vmax . This procedure was iterated to obtained a sample of size n = 2000 . After the sample was generated , we obtained the Shannon entropy values of the samples in the same way as when evaluating the alternative optima space of RegrExLAD ( described in the next section ) . The MATLAB function implementing this sampling procedure can be found in S1 File under the name coneSampling . The Shannon entropy of the sampled alternative optima distribution , Hi , was used to quantify the extent to which the flux values of a reaction , i , varied across the alternative optima space . It was calculated as follows: Hi=−∑k=1nfi , klog ( fi , k ) . Where fi , k represents the frequency ( i . e . , number of counts relative to sample size ) of the k interval in the distribution , for n = 20 equally spaced flux value intervals within the flux range of i . In addition , the total entropy of an alternative optima space , HT , was defined as the sum of the entropies corresponding to the r reactions in AraCOREred , i . e . , HT=∑i=1rHv ( i ) , and was taken as a measure of the total flux variability found in a particular alternative optima space . In the case of CorEx , we generated the set of alternative optimal metabolic networks from the set of sampled alternative optimal flux distributions . To this end , we first generated the binary vector representations of the flux distributions . The binary vector representations were generated by assigning a value of 1 to the entries corresponding to reactions with a flux value v ≥ 10−6 , and 0 otherwise . This process was repeated for each sampled alternative optimal flux distribution . In addition , repeated vector representations were removed from the generated set . After the binary representations were obtained , we calculated the number of mismatches between any pair , a , b , of binary vectors , with a ≠ b , i . e . , the Hamming distance , MR ( a , b ) =∑k=1n|a ( i ) −b ( i ) | . In this way , we obtained a distribution of MR values whose characteristics were reported and compared . We computed a score , ranging between 0 and 1 , to quantify the ambiguity found in individual metabolic pathways ( subsystems in the GEM ) across the space of alternative optimal networks . Concretely , the score of a pathway , M , represents the fraction of the reactions in the ( total ) non-core set , P , belonging to the pathway that are assigned to the active , variable or inactive non-core ( thus producing a score value for each case ) . That is , in general , SX ( M ) =XMP , where XM ∈ {AM , VM , IM} represents the number of reactions assigned to M that are included in the active , variable or inactive non-core , respectively . We performed the same metabolic test proposed in [27] and applied to the original Liver-specific CORDA reconstruction . This test consists of a list of metabolic tasks that a metabolic model has to perform , including parts of the aminoacid , sugar and nucleotide metabolism . Concretely , there a total of 48 metabolic tasks , divided into the production of different aminoacids from minimal metabolic sources and the excretion on urea ( 19 tasks ) , the ability to synthetize glucose from 21 different sources ( including some aminoacids ) , and the production of all 5 nucleotides and nucleotide precursors ( 8 tasks ) . The details about these tasks can be found in the original CORDA publication [27] , while the MATLAB code of our implementation of this test is provided in S1 File . In this study , we used the fraction of performed tasks as measure of the ability of a given liver-specific model to pass this test . For instance , the liver-specific model provided in [27] ( under the name of liverCORDAnew ) , was able to pass 89 . 58% of the tasks ( 43 out of 48 ) . In this study , however , we required to pass all tasks in the test to consider an alternative liver-specific network as functional . We realized that , in the liverCORDAnew model , some reactions were slightly different to the analogous reactions in the Recon1red model that we used throughout this study ( likely due to different versions of the Recon1 model , which is periodically updated [37] ) . When we reconstructed our LiverCORDA model , using the same reaction identifiers in liverCORDAnew but extracting the reactions from our Recon1red version , we found that the generated model passed all metabolic 48 tasks in the test . Hence , for consistency of the results , we considered that all proper alternative optimal networks to LiverCORDA had to pass all 48 tasks as well .
|
Recent methodological developments have facilitated the integration of high-throughput data into genome-scale models to obtain context-specific metabolic reconstructions . A unique solution to this data integration problem often may not be guaranteed , leading to a multitude of context-specific predictions equally concordant with the integrated data . Yet , little attention has been paid to the alternative optima resulting from the integration of context-specific data . Here we present computational approaches to analyze alternative optima for different context-specific data integration instances . By using these approaches on metabolic reconstructions for the leaf of Arabidopsis thaliana and the human liver , we show that the analysis of alternative optima is key to adequately evaluating the specificity of the predictions in particular cellular contexts . While we provide several ways to reduce the ambiguity in the context-specific predictions , our findings indicate that the existence of alternative optimal solutions warrant caution in detailed context-specific analyses of metabolism .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"discussion",
"Methods"
] |
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"carbohydrate",
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2017
|
On the effects of alternative optima in context-specific metabolic model predictions
|
Trachoma is a fibrotic disease of the conjunctiva initiated by Chlamydia trachomatis infection . This blinding disease affects over 40 million people worldwide yet the mechanisms underlying its pathogenesis remain poorly understood . We have investigated host microRNA ( miR ) expression in health ( N ) and disease ( conjunctival scarring with ( TSI ) and without ( TS ) inflammation ) to determine if these epigenetic differences are associated with pathology . We collected two independent samples of human conjunctival swab specimens from individuals living in The Gambia ( n = 63 & 194 ) . miR was extracted , and we investigated the expression of 754 miR in the first sample of 63 specimens ( 23 N , 17 TS , 23 TSI ) using Taqman qPCR array human miRNA genecards . Network and pathway analysis was performed on this dataset . Seven miR that were significantly differentially expressed between different phenotypic groups were then selected for validation by qPCR in the second sample of 194 specimens ( 93 N , 74 TS , 22 TSI ) . Array screening revealed differential expression of 82 miR between N , TS and TSI phenotypes ( fold change >3 , p<0 . 05 ) . Predicted mRNA targets of these miR were enriched in pathways involved in fibrosis and epithelial cell differentiation . Two miR were confirmed as being differentially expressed upon validation by qPCR . miR-147b is significantly up-regulated in TSI versus N ( fold change = 2 . 3 , p = 0 . 03 ) and miR-1285 is up-regulated in TSI versus TS ( fold change = 4 . 6 , p = 0 . 005 ) , which was consistent with the results of the qPCR array . miR-147b and miR-1285 are up-regulated in inflammatory trachomatous scarring . Further investigation of the function of these miR will aid our understanding of the pathogenesis of trachoma .
Chlamydia trachomatis ( Ct ) is the causative agent of trachoma , the leading cause of blindness that results from infection . Forty million people have active trachoma and eight million people suffer with unoperated trichiasis [1] . Repeated infection of the conjunctiva by this intracellular bacterium during childhood causes a chronic inflammatory response , leading to progressive fibrosis and scarring in adult life . Scarring distorts the conjunctiva and the eyelashes are pulled inward to the extent that they scratch the cornea ( trichiasis ) , causing pain and eventually blindness . Chronic trachomatous inflammation is known to continue in the absence of current Ct infection and is believed to drive the continued scarring process , however the mechanisms by which this occurs are not completely understood [2] . Messenger RNA ( mRNA ) expression profiling of each clinical stage of trachoma has revealed many thousands of mRNAs that are differentially expressed [2] , [3] . Key pathways that are differentially regulated in the conjunctiva are innate inflammatory pathways and extracellular matrix modifiers . MicroRNAs ( miR ) are known to have significant roles in the regulation of inflammation , fibrosis and cell differentiation [4]–[7] and can be dysregulated upon bacterial infection [8] , [9] . miR are post-transcriptional regulators of gene expression . They are single-stranded RNA molecules typically 18–22 nucleotides in length . miR bind to complementary mRNA sequences in association with the RNA-induced silencing complex ( RISC ) , causing transcriptional degradation of the transcript or repression of its translation [10] . The seed sequence ( 5′ nucleotides 2–7 ) of the miR guides target selection [11] . Complementary target sequence sites are usually , though not exclusively , found in the 3′ untranslated region ( UTR ) of mRNA transcripts . For a given miR complementary sequence sites may be present on a few or several hundred different mRNA targets , indicating the potential for a few miRs to regulate complete biological processes . Indeed , a relatively small total number of miR are thought to regulate over a third of all protein-coding genes [12] . miR have profound roles in the regulation of many biological processes and interest in their various functions in health and disease is growing . The number of known mature miR ( http://www . mirbase . org/ ) is increasing rapidly as research in this area quickly unravels miR biology . The ability of miR to regulate entire pathways offers investigators an opportunity to reduce the complexity of the trachoma transcriptome [2] . We suggest that the differential regulation of just a few miR in trachomatous disease may underlie the substantial differences in mRNA expression that characterize each phenotypic trachoma group . This reduction in complexity will enable more targeted research into the mechanisms of disease and may identify new potential therapeutic approaches .
The study was conducted in accordance with the tenets of the Declaration of Helsinki . The Ethics Committee of the Gambian Government/Medical Research Council Unit , and the ethics committee of the London School of Hygiene and Tropical Medicine approved the study . Samples were drawn from an archive built up under the MRC study numbers SCC729 , SCC1177 and SCC1274 with specific approval for miR gene expression studies under SCC L2011 . 03 . The samples described in this paper were collected from individuals recruited in trachoma-endemic communities across the whole geographic range of The Gambia , West Africa . Written informed consent was taken from individuals at the time of sample collection . For those participants aged <16 years that wished to take part in the study consent was obtained from a parent/guardian . All samples were anonymized . Cases of trachomatous scarring ( TS ) were identified from screening records , community ophthalmic nurse referral and opportunistic rapid screening . Control individuals with normal conjunctivae were selected by matching for age , sex , ethnicity and location . Clinical phenotypes were assessed in the field by experienced field supervisors trained and regularly assessed in trachoma grading . FPC scores [13] were assigned and grades were agreed by two experienced trachoma physicians using high-resolution photographic records taken in the field using a Nikon D3000 SLR camera with a VR AF-S micro Nikkor 105 mm 1:2 . 8G ED lens . Photographs were taken at the time of sample collection . Individuals were grouped into the following clinical phenotypes for analysis: individuals with trachomatous scarring ( TS ) had a C score between 1–3 ( mild to severe scarring ) and a P score of 0 or 1 ( none or mild inflammation ) , individuals with trachomatous scarring in the presence of clinically significant inflammation ( TSI ) had a C score of 1–3 and a P score of 2 or 3 ( moderate to severe inflammation ) , and control samples from individuals with normal healthy conjunctivae ( N ) had no conjunctival scarring ( C0 ) , papillary inflammation ( P0 ) or follicles ( F0 ) . Swabs were taken from the upper tarsal conjunctiva using Dacron polyester-tipped swabs ( Hardwood Products Company ) and stored in 250 µl RNAlater ( Ambion , Life Technologies ) on ice blocks in the field and then archived at −20°C until processed . A total of 63 specimens from the archive were selected for miR expression array profiling . Specimens were selected as representative examples of each phenotype group using the FPC scores . As control samples for these experiments , individuals with normal conjunctivae matched on age , sex , ethnicity and location were selected . MiR was extracted from swabs using the Qiagen Allprep DNA/RNA/protein kits with a modification to collect small non-coding RNAs . DNase1 digestion ( Qiagen ) was included . Total RNA purity was assessed by spectrophotometry using a nanodrop ND-1000 ( Thermo Fisher Scientific ) . Reverse transcription and pre-amplification were performed using Megaplex human primer pools Av2 . 1 and Bv3 . 0 following the manufacturer's instructions ( Taqman , Life Technologies ) . Quantitative PCR was performed using 72 µl of pre-amplified cDNA as template in the PCR master mix for the TaqMan Array Human MicroRNA genecards ( Av2 . 0 and Bv3 . 0 ) . Thermal cycling was performed on a 7900HT thermal cycler ( Life Technologies ) . Plates were held at 50°C for 2 minutes , 94 . 5°C for 10 minutes , then underwent 40 cycles of 97°C for 30 seconds and one minute at 59 . 7°C . A total of 754 of the most well characterised unique human miR from Sanger miRBase V . 14 ( www . mirbase . org/ ) were screened . Sanger miRBase V . 14 was the latest version of the miR database at the time of screening . qPCR cycle threshold ( CT ) values were processed in SDS RQ manager ( Life technologies ) ; the threshold was set to 0 . 05 and baselines were detected automatically . Data from each array were uploaded and analysed using the High Throughput qPCR Package ( HTqPCR ) in Bioconductor R ( www . bioconductor . org , www . r-project . org ) [14] . Sample profiles were excluded from the analysis when the median CT value for the array was 40 since the majority of the CT values were either close to threshold or undetermined . Individual miR were retained in the analysis only when expressed ( CT value<40 ) by at least five specimens . A and B genecard data were analysed separately due to differences in specimen performance on each card . Data were normalized to reduce technical bias in the analysis by a number of different standard methods ( supplementary figure S1A & B ) . The coefficient of variation , standard deviation and correlation of raw against normalized data were used to evaluate the suitability of each method of normalisation , as described in Deo et al . ( 2011 ) [15] . The ‘Norm rank invariant’ method was chosen as the most effective normalisation strategy for both A and B cards ( supplementary figure S1A & B ) . The distribution of the raw and Norm rank invariant normalized CT values are shown in supplementary figure S2 A–D . Differential expression was then assessed by empirical Bayes/moderated t-tests using HTqPCR in Bioconductor R . These data are deposited within the NCBI GEO public database ( GSE37717 ) and , in line with MIQE guidelines [16] , details are included as supplementary table S1 . Relative abundance of miR in the conjunctiva was calculated from array CT data . An average was taken of the normalized CT values for all specimens ( including all phenotypes ) for each miR on A and B cards . The general equation for estimating relative differences in PCR was then applied to these values: 2− ( CT_target−CT_calibrator ) where the calibrator was the most abundant miR ( miR-1274B ) . For each miR this value was then divided by the sum of all these values to create a relative abundance . A network graph based on the specimen-to-specimen Pearson correlation was generated Biolayout express 3D v2 . 2 ( www . biolayout . org/ ) [17] . The overall miR expression correlation matrix and graph were constructed from the rank invariant normalized raw CT data . The graph was arranged according to patient clinical classification ( N , TS , TSI ) . Pearson correlation coefficients ( r ) >0 . 7 were retained and used as cut-offs in network construction . Nodes in the graph are individual miR linked by an edge if r>0 . 7 . The graph was then clustered using a Markov Clustering algorithm using default inflation values . The partitioned clusters of expression contain sets of miR that exhibit a very strong degree of co-expression across the sample . Cluster content is independent of differential expression level . The co-expression clusters were then investigated in silico at the individual and pathway levels . miR that were differentially expressed in the arrays at a significance level of p<0 . 05 and with a fold change ( FC ) over three ( up or down-regulated ) were entered into pathway analysis . DIANA-microT v4 . 0 ( Beta version ) target prediction was used in the DIANA mirPath software [18] . Multiple miRNA analysis was used for the significant miR within each comparison group . Total RNA including miR was extracted from swabs using a Qiagen miRNeasy kit , incorporating a DNase1 digestion step . Total RNA purity was assessed by spectrophotometry using a nanodrop ND-1000 ( Thermo Fisher Scientific ) . miR was reverse transcribed using miScript II RT kit with the hiFlex buffer as per the manufacturer's instructions ( Qiagen ) . qPCR was carried out using miScript Primer Assays and the miScript SYBR Green PCR kit ( Qiagen ) on a 7900HT thermal cycler ( Life Technologies ) . Ten microlitres of RT product was diluted in 100 µl H20 and 0 . 5 µl was used as template in each qPCR assay , with 0 . 5 µl specific miR forward primer assay , 1 µl water , 0 . 5 µl universal reverse primer and 2 . 5 µl SYBR green master mix , in a total reaction volume of 5 µl . Each assay was performed in quadruplicate , including no template controls for each miR and for each specimen . Cycling conditions were as follows: 15 minutes at 95°C , followed by 70 cycles of 15 seconds at 94°C , 30 seconds at 55°C , and 30 seconds at 70°C . Data were collected at 94°C and 70°C . qPCR was run for 70 cycles to minimize the number of undetermined values . Fifteen percent of all miR assays had a CT value over 40 . CT values were derived in SDS RQ manager ( ABI , Life technologies ) , with a threshold of 0 . 05 and an automatic baseline . Four replicate tests were used to calculate the geometric mean after outliers were removed . Analysis was done in R . Specimens were removed from the analysis if the endogenous control snoU6 ( U6 ) CT values were ≥2× the standard deviation ( s . d . ) of the mean of all U6 CTs in the sample . For analysis purposes , any assay that did not amplify by 70 cycles was assigned a CT value of 80 ( 19 assays out of a total 1552 ) . Target CT values for each specimen were normalized to U6 using Δ CT = CT target−CT U6 . For each miR within each phenotype group , the Shapiro-Wilk method was used to test for normality of distribution in the raw-data . For each comparison ( N v TS , N v TSI , TS v TSI ) we calculated the fold change in miR expression between the phenotypic groups , using 2−ΔCT ( median phenotype 1 ) −ΔCT ( median phenotype 2 ) . We used the Wilcoxon rank sum test ( with continuity correction ) to test for differences in the expression of each miR between phenotypic groups as the majority of the data were not normally distributed . Details of the qPCR and analysis in line with MIQE are included in supplementary table S1 .
Sixty-three specimens were tested by miRNA array cards . Twenty-three specimens were excluded from A genecard profiles and 34 specimens from B genecard profiles . Basic demographic and clinical phenotype data are shown in table 1 both before and after filtering and these show that there was no systematic loss of any specific sample type based on clinical phenotype , age or sex as a result of the filtering process . Following the filtering procedures described , 506/754 miRs were included in the final analysis . Relative abundance of all of 506 expressed miR in the conjunctiva was calculated from the CT data . Of the miR that were tested , just six constitute 90% of total miR present in the conjunctival samples ( figure 1 ) . miR-1274B has the highest overall expression and miR-623 the lowest . Networks of co-expression , independent of differential expression , based on rank invariant CT values were explored in the entire data set of 506 miR using Biolayout express 3D . The undirected graph contained 126 miR connected by 215 edges . Markov clustering partitioned the network into 11 clusters of co-expressed miR . These clusters ranged in size from 23 to 4 co-expressed miR and accounted for 80 miR in the original network . Each of the 11 clusters is laid out in figure 2 and the specific miR content of each cluster is available in supplementary table S2 . The major biology revealed by these co-expression clusters indicates that these miR target mRNA in four major pathways . These are repeatedly identified and are shown in figure 2 . The MAPK signaling pathway and focal adhesion pathway contain the largest number of miR target genes and have the highest levels of enrichment ( over-representation ) . A total of 82 miR are differentially expressed across the comparison groups ( FC>3 , p<0 . 05 ) ( supplementary table S3 ) . The number of up- and down-regulated miR in each comparison is shown in table 2 . A miR that is up-regulated in the N v TS comparison has a lower CT value in TS relative to N . The same applies to N v TSI and TS v TSI , where the latter is up- or down-regulated relative to the former in each comparison . A larger number of miR are differentially expressed in comparisons with the TSI phenotype . Fewer miR are down-regulated than up-regulated , particularly in the N v TSI group . Twenty miR are differentially expressed in both N v TSI and TS v TSI comparisons ( figure 3 ) indicating they might be features of inflammation . In contrast , very few miR are shared with the N v TS miR gene set and there are none that overlap between all three groups . This indicates that TSI and TS phenotypes are distinct and have characteristic miR signatures . Of the 103 miR found in the networks by Markov clustering , 15 had some evidence of differential regulation based on p-value alone . miR-492 and miR-548d were both up-regulated ( 4 . 9 and 3 . 2 times respectively ) in TSI individuals compared to controls whilst 3 miR ( miR-508 , miR-509 and miR-664 ) were >3 fold down-regulated . The miR precursor let-7b showed modest evidence of differential up-regulation in TS individuals versus normal controls ( p = 0 . 037 ) . Differentially expressed miR in each comparison group ( FC>3 p<0 . 05 ) were entered into DIANA mirPath pathway analysis . The top ten most enriched pathways for each comparison are listed in table 3 , where a greater −ln ( p-value ) reflects increasing enrichment of miR targets within a pathway . Many of the same pathways are enriched in each comparison group , despite little overlap in miR between the N v TS , N v TSI and TS v TSI groups ( figure 3 ) . Axon guidance , focal adhesion , and the TGF-β signaling pathway are all present in all three groups . A large number of genes in the TGF-β pathway are predicted targets of differentially expressed miR in the N v TSI comparison ( figure 4 ) . Within the TGF-β pathway , 53% of transcripts are differentially regulated based on differences found in a mRNA transcriptome array using Ethiopian conjunctival samples ( GSE23705 ) from similar phenotypic groups [2] . Analysis was also carried out on each phenotypic comparison group split into up- and down-regulated gene sets ( supplementary table S5 ) . Interestingly , TGF-β is enriched in the down-regulated gene set for each comparison group . Given that this pathway is enriched for miR targets that would be silenced and these miR are down-regulated , this would suggest an up-regulation or release of the TGF-β signaling pathway . Analysis was also performed on the 20 miR differentially expressed in both N v TSI and TS v TSI comparisons , with enrichment again in MAPK , TGF-β and Wnt pathways ( supplementary table S6 ) , supporting the hypothesis that these miR are characteristic of the major pathways under miR control in the conjunctiva and are perturbed by inflammation . In the validation set it was not feasible to assay the expression of all the potentially differentially expressed miR and we selected for follow-up a small number of miR that exhibited a high FC , low p-value and homogeneity in the raw data . Seven miR were selected for follow-up ( supplementary table S7 ) including three miR that were differentially regulated in the N v TS comparison ( miR-30c , miR-32 , miR-203 ) and four from the N v TSI comparison ( miR-10a , miR-147b , miR-1285 , miR-1305 ) . Each candidate miR was tested for differential expression in a second sample of 194 independent archival Gambian clinical specimens , selected as representative examples of each phenotype group . In these experiments , small nucleolar ( sno ) U6 RNA was used as the calibrator snoU6 CT values were not different between the phenotypic groups , which implied that it was a stably expressed reference gene ( supplementary information figure S3 ) . Five specimens were excluded because they had outlying snoU6 values ( average CT>2 s . d . of the sample mean ) , leaving a total of 189 specimens to be tested for statistical differences in expression levels . Summary statistics for these 189 specimens are shown in table 4 . Data were tested for differential expression between the three phenotypic groups as is presented for the analysis of the array data ( N , TS and TSI ) . Only miR-1285 and miR-147b showed a significant difference between the different phenotypic groups in this validation set ( table 5 ) . MiR-147b was up-regulated 2 . 3 fold in individuals with TSI relative to N ( p = 0 . 0332 ) . This is consistent with the array results in which miR-147b was up-regulated 9 . 6 fold in individuals with TSI versus N . MiR-1285 was up-regulated 4 . 6 fold in TSI relative to TS ( p = 0 . 005 ) . This is also consistent with the array results in which miR-1285 was up-regulated 16 fold in TSI versus TS .
Array analysis revealed that a large number of miR are potentially differentially regulated between different disease states and healthy controls . Trachomatous scarring with inflammation ( TSI ) has a distinct miR signature compared to scarring trachoma ( TS ) . TS may be a less active disease process than TSI as fewer miR were differentially regulated . On validation , two miR remained significantly differentially regulated . MiR-147b was up-regulated in individuals with TSI compared to N and miR-1285 was up-regulated in people with TSI compared to those with TS alone . In a transcriptome analysis of similar phenotypic comparison groups in Ethiopians with scarring trachoma [2] , 25% of predicted targets of miR-1285 and 52% of predicted targets of miR-147b predicted targets ( TargetScan v6 . 2 ) were differentially regulated ( adjusted p<0 . 05 ) . MiR-1285 directly targets the 3′UTR of p53 mRNA in HEK 293T cells [19] . The loss of p53 is associated with many cancers via disruption of the normal function of p53 in the initiation of apoptosis and growth arrest . In contrast , Hidaka and colleagues [20] find miR-1285 to be a tumor suppressor . Expression levels of miR-1285 were reduced in clinical samples of renal cell carcinoma ( RCC ) compared to healthy mucosa and miR-1285 transfection in RCC cell lines in vitro led to inhibition of cell proliferation , migration and invasion [20] . The authors verified transglutaminase 2 ( TGM2 ) as a target of miR-1285 . Interestingly , TGM2 is linked to several cancers and the process of epithelial-mesenchymal transition ( EMT ) [21] . EMT can be initiated by chronic inflammation [22] and is implicated in the pathology of many fibrotic diseases [23] , so could have a role in trachomatous disease . The conflicting conclusions of these studies are likely due to the different cell types used . In vitro studies in primary conjunctival epithelia will be required to understand the function of miR-1285 in chlamydial infection and trachoma . There is limited literature on the clinical significance of changes in miR-147b expression , but it is known to be down-regulated in rectal cancer [24] . This is consistent with the ability of miR-147b to induce apoptosis four days post-transfection in A549 cells [25] . Mmu-miR-147 , a functional homologue of human miR-147b , is induced by multiple toll-like-receptor ( TLR ) signals and negatively regulates inflammation in murine macrophages [26] . It acts in a negative feedback loop to prevent excessive inflammation . Bertero and colleagues [25] showed that LPS ( bacterial lipopolysaccharide ) and TNFα strongly induced hsa-miR-147b expression in A549 cells in vitro; supporting the hypothesis that miR-147b has a homologous role in the regulation of inflammation in humans . The up-regulation of miR-147b seen in the TSI phenotypic group may reflect ongoing and uncontrolled inflammation . Understanding the role of miR-147b in TSI should also be aided by further in vitro functional studies . MiR-23b-5p is up-regulated in TS relative to both N ( FC = 3 . 6 p = 0 . 026 ) and TSI ( FC = 5 . 8 p = 0 . 008 ) ( figure 3 ) , indicating that it might be a feature of scarring in the absence of inflammation . MiR-23b is a member of the miR-23b cluster , which includes miR-27b and miR-24-1 . This cluster is known to target members of the TGF-β signaling pathway [27] . An up-regulation of miR-23b , as is seen in scarred individuals , would lead to a decrease in TGF-β signaling . Although this may seem counterintuitive , any dysregulation of the TGF-β signaling pathway could lead to aberrant wound healing [28] . MiR-23b is also anti-inflammatory through inhibition of NFkB activation [29] , preventing up-regulation of inflammatory cytokines such as IL-17 . In turn , IL-17 inhibits miR-23b , leading to inflammation . An up-regulation of IL-17A is associated with active trachomatous disease [30] . The relative down-regulation of miR-23b in TSI compared to TS and N conjunctival samples could reflect a down-regulation of this miR's expression by IL-17 in individuals with TSI . MiR-30c was up-regulated 15 fold in N v TS ( p = 0 . 01 ) and 11 fold in N v TSI ( p = 0 . 04 ) in the microarray experiments . This miR is thought to regulate fibrinolysis and collagen production through targeting serine protease inhibitor SERPINE1 and connective tissue growth factor ( CTGF ) respectively [31] . Over expression of this miR is known to inhibit the proliferative and migratory properties of endometrial cancer cells [32] , however qPCR validation found no association of this miR with disease . Pathway analysis revealed that many of the same pathways are enriched amongst predicted targets of differentially expressed miR in each comparison group . This is surprising due to the minimal overlap of differentially expressed miR in N v TS compared with the other groups . Many of these pathways are characteristic of epithelial cell and fibroblast communication , differentiation and fibrosis . In particular , the Wnt pathway has been implicated in the disruption of epithelial cell homeostasis and the pathology of C . trachomatis infection [33] . Importantly , epithelial cell differentiation , development and cytoskeleton remodeling pathways ( including TGF-β and Wnt ) are enriched in gene sets from a differential expression analysis of transcriptome data of similar phenotypic groups in Ethiopians [2] . Of all the members of the KEGG defined TGF-β pathway , 53% are differentially regulated in this Ethiopian transcriptome [2] . We theorise that the miR that are differentially expressed in this study are at least partly responsible for the observed changes in the normal function of the Wnt and TGF-β pathways in trachoma patients . Investigation of miR abundance in the conjunctiva shows that most miR have very low levels of expression , with just a few making up the vast majority of the population . MiR-1274B is the most highly abundant miR in the conjunctiva , but the nature of this miR has been called in to question . A previous study [34] found evidence that miR-1274B may not be a canonical miR , rather that it is a tRNA-derived small RNA ( tsRNA ) . tsRNAs are thought to be abundant in the genome [26] and may have a role similar to miR in regulating gene functions [35] . It may be the case that the high abundance of miR-1274B can be explained simply by its origin in tRNA and the generally high abundance of tRNA in cells . Regardless of this uncertainty over its designation as either a miR or a tsRNA , a role for miR-1274B in the regulation of gene expression cannot be excluded at this time . The discrepancy between the array results and qPCR validation could be due to a high number of false positives accepted in the initial analysis or as a result of a number of technical differences leading to differential miR isolation , extraction , amplification bias and normalisation [36] . Different methods of miRNA isolation and qPCR were employed in this study in the screening and validation stages which could introduce technical variation [37] . In addition , a non-proscriptive filtering process was used in the identification of potentially differentially expressed miR . Acceptance of a high number of potential false positive associations at this initial filtering stage was considered acceptable based on the arguments presented by Rothman [38] on principles of p-value adjustment and enabled us to explore the wider biology of miR conjunctival expression ( supplementary table S4 ) . As a result , many of the miR chosen for follow up in the validation clinical samples appear not to be differentially regulated . This highlights the need to verify array-profiling results even when the method of choice is the apparently robust gold standard technique to examine differential expression [39]–[41] . Furthermore the design of the B genecards , which cover less abundant miR , adds difficultly to analysis pipeline . Even with pre-amplification the amount of genetic material that can be obtained from a conjunctival swab is very small . Specimens were judged to perform less well on B genecards leading to the exclusion of a larger number of specimens , resulting in a reduction of the number of biological replicates in each phenotypic group and therefore some loss of statistical power . The raw and analysed data is publicly available in the NCBI GEO . This is the first description and initial identification of specific miR expression in an ocular fibrotic disease of humans . It is possible that these miR play a role in other ocular surface inflammatory diseases . We highlight the major pathways under the control of miR that are expressed in the conjunctival epithelium and suggest that it is plausible that dysregulation of expression in these miR leads to the release of the TGF-β signaling pathway in trachomatous disease . Two miR with significantly increased expression in trachomatous scarring and inflammation were identified ( miR-147b and miR-1285 ) . In order to understand the mechanisms by which these miR may contribute to health and disease of the conjunctiva the associations of miR-147b and miR-1285 with trachomatous disease now requires further study . A combination of in vitro experimentation in model systems and in vivo application in animal models will also facilitate our understanding of this association and whether these miR are reflective or causative effectors of disease . Research in this area of RNA biology is a rapidly evolving field that is only now beginning to realize its potential . We hope that its application to trachomatous disease may lead to the development of therapeutics or biomarkers for the diagnosis and treatment of trachoma and other fibrotic ocular pathologies .
|
Trachoma is a debilitating disease that affects 40 million people worldwide . It can cause progressive fibrosis of the upper eyelid and blindness , yet the mechanism is poorly understood . We have investigated the expression of short sequences of genetic material ( microRNA ) that regulate gene expression . We screened for the expression of 754 microRNA sequences ( miR ) in genetic material isolated from conjunctival swab samples from individuals in trachoma-endemic communities in The Gambia . This sample included healthy controls , individuals with trachomatous scarring and individuals with trachomatous scarring in the presence of clinically significant inflammation . We found 82 miR that were differentially expressed . Computer simulations predict that these miR regulate genes in epithelial cell differentiation , inflammation and fibrosis pathways , all of which are involved in the scarring process . We then validated the expression of seven of these differentially expressed miR in a second larger biological sample set from The Gambia . We confirmed that miR-147b and miR-1285 have increased expression in individuals with trachomatous scarring in the presence of clinically significant inflammation . Further investigation into the functions of these miR will aid our understanding of this disease and present opportunities to develop treatments for ocular fibrotic diseases .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"gene",
"networks",
"rna",
"interference",
"gene",
"regulation",
"immunology",
"gene",
"function",
"bacterial",
"diseases",
"neglected",
"tropical",
"diseases",
"epigenetics",
"molecular",
"genetics",
"signaling",
"pathways",
"infectious",
"diseases",
"inflammation",
"extracellular",
"matrix",
"gene",
"expression",
"biology",
"molecular",
"biology",
"systems",
"biology",
"signal",
"transduction",
"cell",
"biology",
"clinical",
"immunology",
"immunity",
"innate",
"immunity",
"genetics",
"trachoma",
"molecular",
"cell",
"biology",
"genetics",
"of",
"disease",
"genetics",
"and",
"genomics"
] |
2013
|
Conjunctival MicroRNA Expression in Inflammatory Trachomatous Scarring
|
Recombination between co-infecting poxviruses provides an important mechanism for generating the genetic diversity that underpins evolution . However , poxviruses replicate in membrane-bound cytoplasmic structures known as factories or virosomes . These are enclosed structures that could impede DNA mixing between co-infecting viruses , and mixing would seem to be essential for this process . We hypothesize that virosome fusion events would be a prerequisite for recombination between co-infecting poxviruses , and this requirement could delay or limit viral recombination . We have engineered vaccinia virus ( VACV ) to express overlapping portions of mCherry fluorescent protein fused to a cro DNA-binding element . In cells also expressing an EGFP-cro fusion protein , this permits live tracking of virus DNA and genetic recombination using confocal microscopy . Our studies show that different types of recombination events exhibit different timing patterns , depending upon the relative locations of the recombining elements . Recombination between partly duplicated sequences is detected soon after post-replicative genes are expressed , as long as the reporter gene sequences are located in cis within an infecting genome . The same kinetics are also observed when the recombining elements are divided between VACV and transfected DNA . In contrast , recombination is delayed when the recombining sequences are located on different co-infecting viruses , and mature recombinants aren’t detected until well after late gene expression is well established . The delay supports the hypothesis that factories impede inter-viral recombination , but even after factories merge there remain further constraints limiting virus DNA mixing and recombinant gene assembly . This delay could be related to the continued presence of ER-derived membranes within the fused virosomes , membranes that may once have wrapped individual factories .
Genetic recombination serves an essential role as a mechanism for repairing DNA damage , especially the double-stranded breaks that are produced when the replication machinery encounters single-stranded nicks in template DNA . In the field of virology , recombination was first used to define and map bacteriophage genes [1 , 2] and is widely used as a tool for genetically engineering a great diversity of viruses . Recombination also affects poxviruses , as was shown by early work with cowpox , variola and vaccinia viruses ( VACV ) [3 , 4] . It was subsequently used to map VACV genes using both classical and marker rescue methods [5–9] and methods developed in the 1980s [10 , 11] are also still widely used to produce genetically modified poxviruses . We , and others , have been studying the mechanism of poxvirus genetic recombination and have observed a process that is capable of generating viruses bearing evidence of multiple genetic exchanges over the course of even a single round of infection [12] . Mechanistically , poxvirus recombination is intrinsically linked to virus DNA replication [13 , 14] , and VACV recombination is catalyzed , both in vivo and in vitro , by the viral DNA polymerase ( E9 ) working in conjunction with the I3 single-strand DNA-binding protein [15–17] . These reactions use the polymerase-encoded proofreading 3’-5’ exonuclease activity to initiate an I3-catalyzed single-strand annealing reaction , and the process has been exploited for its commercial utility as an in vitro tool for cloning DNA [18] . Recombination has great biological relevance as it generates the genetic variation that is the substrate for viral evolution . For example , traditional smallpox vaccines comprise a genetically diverse quasispecies , wherein every virus exhibits evidence of having undergone inter- and intra-molecular recombination during its evolution [19] . Analysis of variola genome sequences suggests that recombination may also have shaped the evolution of this pathogen [20] . More recently it has been speculated that an “accordion-like” gene duplication and reduplication process [21 , 22] could also promote the evolution of potentially essential poxvirus genes . Although clearly illustrating a variant form of virus DNA recombination-repair , the mechanistic details remain poorly understood . One of the characteristic features of poxvirus biology is that as virions enter the cell , each infecting particle initiates the formation of separate replication sites or “factories” [23 , 24] . We have shown that these structures mix inefficiently [25] , which may explain the seemingly contradictory observation that although hybrid viruses are not produced in great abundance in cells co-infected with different viruses , the recombinants that are formed appear to have undergone a lot of recombination [12] . Presumably , each replication site has the potential to catalyze efficient recombination , but if the DNA in different virus factories doesn’t mix , then there is no opportunity to produce recombinant virus progeny . Poxvirus factories are thought to be bounded by membranes ( most likely ) derived from the endoplasmic reticulum ( ER ) [26] , and the DNA in different factories doesn’t seem to mix until relatively late in the infectious cycle [25] . That said , these statements incorporate some assumptions that have yet to be proven . One feature of these processes that has not been clearly established is how the timing of recombination relates to visible features of the virus life cycle . We presume that factory fusion reactions would have to precede recombinant virus production , but this has not been formally demonstrated beyond correlation analysis . We are also presuming that intracellular dynamics and mixing efficiency , rather than enzymology , is what constrains recombinant virus production . The necessary enzymes ( E9 and I3 ) would be present from the start of factory development , but we cannot exclude the possibility that the catalytic capacity to produce mature recombinants isn’t fully active until late in infection . If that were the case , the timing of recombinant production would not be dependent solely upon geometrical constraints . To explore these questions , we have employed a technology used previously to track factory development [25] . Cells were constructed that constitutively expressed the bacteriophage lambda cro protein fused to enhanced green fluorescent protein ( EGFP-cro ) . Upon infection with VACV , the EGFP-cro protein migrates from the nucleus and labels the virus DNA in the growing factories . This permits live cell imaging of virus development over the course of infection . In this study we have incorporated genes encoding cro fused to monomeric cherry fluorescent protein ( mCherry-cro ) into VACV . By apportioning overlapping fragments of the mCherry-cro gene into different viruses , and co-infecting EGFP-cro cells with these viruses , we can track both factories and recombinant production using green and red fluorescence , respectively . Our studies show that different types of poxvirus recombination events exhibit different timing patterns , depending upon the relative locations of the recombining elements . Recombination between partly duplicated sequences is detected soon after post-replicative genes are expressed , as long as the reporter gene sequences are located in cis within an infecting genome . The same kinetics are also observed when the recombining elements are divided between a virus and transfected DNA . In contrast , recombination is significantly delayed when the recombining sequences are located in trans , on different co-infecting viruses , and mature recombinants aren’t detected until well after late gene expression is well established . The delay is consistent with the hypothesis that virus factories create one impediment to inter-viral recombination , but even after factories merge there remain further constraints limiting recombinant production .
We have previously shown that a molecule composed of enhanced green fluorescent protein fused to the phage lambda cro DNA-binding domain ( EGFP-cro ) provides a useful tool for tracking DNA in vivo . When the EGFP-cro protein is expressed constitutively from a cellular promoter , it diffuses freely to sites of VACV DNA replication and permits tracking of viral “factories” [25] . In this study we have used a modification of this approach , to examine when and where recombinant poxviruses are formed during poxvirus infection , and thus permit optical tracking of virus recombination in real time . The principles behind these assays , and the viruses used in the different studies are shown in Fig 1 . These viruses encode all ( or parts ) of a gene comprising a poxvirus early-late promoter ( pE/L ) driving expression of mCherry fluorescent protein fused to a cro DNA-binding peptide ( mCherry-cro ) . The hybrid promoter combines conserved sequence elements that have been traditionally defined as driving either immediate early or late gene expression . It is not expected to be active at intervening time points . All of the gene constructs were incorporated into the non-essential VACV thymidine kinase locus . The mCherry protein was chosen for these studies because it is bright , and folds rapidly after being transcribed and translated [t1/2 = 15 min ( Clontech ) ] . We incorporated the cro DNA-binding domain to concentrate the signal and in the hope that the fusion protein might , at least transiently , selectively target the virus factory from where it had originated . Subsequent studies showed that the protein does concentrate on DNA , but also still diffuses freely throughout infected cells , as judged by red fluorescence in the infected cell nucleus . The virus designated as pE/L-mCherry-cro served as a control for reference purposes ( Fig 1A ) . When BSC-40 EGFP-cro cells were infected with this virus for 8 h , we detected a strong mCherry signal , co-located with DAPI and EGFP-cro at sites of virus replication and within the nucleus ( Fig 1B , top ) . At this time point VACV factories were typically starting to expand in volume and the initial punctate appearance was beginning to blur as the virus transitioned into the later stages of the infection cycle . Virus #2 ( mCherry-cro ) encodes an intact mCherry-cro fusion protein , but lacks the E/L promoter , while virus #3 ( pE/L-mCherry[t] ) encodes the E/L promoter driving a truncated and non-fluorescent mCherry protein . In contrast to cells infected with the control virus , no mCherry signal was detected in cells separately infected with viruses #2 and #3 even though they were clearly infected judging by the recruitment of EGFP-cro protein to DAPI-stained virus factories ( Fig 1B , middle rows; VACV-mCherry-cro [S1 Video; VACV pE/L-mCherry ( t ) [S2 Video] ) . To test whether this system could detect recombinant virus production , the BSC-40 EGFP-cro cells were co-infected with a 1:1 mixture of the pE/L-mCherry ( t ) and mCherry-cro viruses , at a total MOI = 5 ( i . e . MOI = 2 . 5 for each virus ) . Little red fluorescence was seen at the 8 h time point , but by 24 h red fluorescence was detected in many of the cells ( Fig 1B , bottom panel ) . These data showed that this method can be used to detect VACV recombinants , but the process is a slow one and recombinant gene products aren’t detected until late in the infection cycle . This matter is examined in greater detail in the sections to follow . We also examined how well these viruses would grow , using single-step growth curves . All of the viruses grew initially at nearly the same rate in BSC-40 cells , although the pE/L-mCherry-cro control yielded 10-to-60-fold less progeny than the other viruses ( S1 Fig ) . The mCherry-cro protein is produced in abundance by the pE/L-mCherry-cro virus , and appears to be packaged into virions . Because of this , it probably has a somewhat deleterious effect on virus growth ( or packaging ) over multiple rounds of VACV replication . We used live cell imaging to track the growth and movement of separate viral factories , in order to compare these events with the time ( s ) when recombinant mCherry can first be detected . In designing these experiments , we were cognizant of the fact that the timing of these events depends upon the sensitivity of the experiment ( i . e . the time when one can first detect fluorescence ) , and thus the strength of the mCherry signal . Therefore we set the gain in all of these experiments , at a level that would detect the weaker late mCherry signal observed in cells co-infected with pE/L-mCherry ( t ) and mCherry-cro viruses . In order to standardize the timing between different experiments , we defined the “factory time” tf = 0:00 as being the time ( in hours ) when small punctate cytoplasmic viral factories were first detected by EGFP-cro staining . We presume that these would be uncoated particles , since the DNA is accessible to cytoplasmic EGFP-cro protein , and they were detected 1–3 h post-infection . We defined Ti as the time ( in hours ) after the addition of virus . VACV pE/L-mCherry-cro was used as a control to establish when an intact mCherry reporter protein is first expressed during virus infection . This was complicated by the fact that many punctate mCherry+ signals were detected at the earliest time points , prior to entry and uncoating , and long before the first appearance of any EGFP-cro labeled factories ( Fig 2A , tf = -2:00 ) . This mCherry signal was only seen transiently and probably comprised mCherry-cro protein that had been incorporated into the virus particles used in the inoculum . It was mostly lost by degradation and/or dilution as the virus entered the cell and the DNA uncoated ( S3 Video ) . The first intracellular EGFP-cro-labeled virus particles were detected ~3 h post-infection ( Fig 2A , panel d ) and these acquired a secondary mCherry fluorescent signal only a few minutes after first detecting the viral factories ( Fig 2A , tf = 0:35 , panel h ) . As the infection progressed the intensity of the EGFP and mCherry signals increased , indicative of active replication and new mCherry synthesis . The factories also moved around and started to coalesce into larger assemblies by Ti = 7:15 ( Fig 2A , panels j and k ) . Because the gain was set to detect the faint signals produced by other combinations of virus , as noted above , the mCherry signal started to saturate the detectors in the later parts of the experiment ( Fig 2A , Ti = 10:00 , panels n and o ) . Quite different mCherry expression kinetics were seen in cells co-infected with the pE/L-mCherry ( t ) and mCherry-cro viruses . The cells were infected with the two viruses at a combined MOI = 5 , and imaged to again track the development of EGFP- and mCherry-tagged viral factories ( Fig 2B; S4 Video ) . No detectable mCherry signal was seen either in the inoculum or within a few minutes of first detecting the EGFP-labeled factories ( Fig 2B , panels b , e , and h ) . As in cells infected with the control virus , these factories gradually migrated towards the nuclear periphery and started to merge into a shared structure , around tf = 0:35 in the example shown here ( Fig 2B , compare panel d to panel g ) . To confirm that the viral factories had indeed fused , we quantified the fluorescence intensities of individual factories before and after fusion ( S2 Fig ) . This method [25] showed that factory fusion was associated with the conservation of the sum of the fluorescence intensities exhibited by the two separate factories prior to fusion , plus a correction for the replication and EGFP-cro accumulation over the 5-minute interval between frames . However , although one sees abundant evidence of factories fusing , a mCherry signal was still not detected until a larger aggregate had formed by the tf = 5:05 time point ( Fig 2B , panel k ) . Thereafter , this mCherry signal gradually gained intensity and was distributed across all EGFP-tagged cytoplasmic viral factories . These particular viruses , and this approach , illustrate two features of VACV recombination in vitro . First , recombinant genes aren’t detected until long after the different factories have started to fuse and mix their DNA . Secondly , even after factory fusion takes place , there is a further delay before a recombinant signal is detected . Several additional viruses were used to determine how the timing of recombinant virus detection relates to other stages in VACV development . I1L is representative of a class of VACV genes called post-replicative genes [27] . We assembled a VACV encoding I1 protein , under its native promoter , and fused to a mCherry reporter protein . This recombinant protein was first detected at tf = 2:00 ( Fig 3A , panel h; S5 Video ) , and as expected , that is later than the “early” fluorescent signal detected in cells infected with the pE/L-mCherry-cro virus ( Fig 2A , panel h; tf = 0:35 ) . We also measured the timing of expression of an A5L-YFP fusion protein . A5L is regulated by a late viral promoter and newly expressed YFP was not detected until tf = 3:50 ( Fig 3B , panel h; S6 Video ) . This time point still significantly precedes the timing of the appearance of a mCherry signal ( tf = 5:05 ) in cells co-infected with the pE/L-mCherry ( t ) and mCherry-cro viruses . To confirm the timing of early and late gene expression by independent methods we also used ordinary Western blotting . Cells were infected with the pE/L-mCherry-cro virus or co-infected with the pE/L-mCherry ( t ) plus mCherry-cro viruses . Samples were collected every hour and probed to detect another highly expressed early gene product ( I3 ) and a late one ( A34 ) . The timing of mCherry expression was determined by microscopy , because the low levels of mCherry that are easily detected optically ( for timing purposes ) aren’t as easily detected by Western blotting . Although the timing determined by these methods is a bit less accurate , I3 was first detected at approximately Ti = 1 h and A34 at about the Ti = 4–5 h mark . When we normalize the data to a common “start” point by marking the time where factories first form ( tf = 0:00 ) in each microscopy experiment and aligning it with the time of initiating infection ( Ti = 0:00 ) , it is again evident that mCherry is expressed early during pE/L-mCherry-cro virus infection ( as expected ) , whereas it is expressed late or very late in co-infected cells ( Fig 4 ) . The results outlined above are perhaps not surprising as the promoter in the pE/L-mCherry-cro virus permits mCherry expression prior to uncoating ( early ) . In contrast any recombinants that are assembled after that point can’t be detected until late gene expression is initiated . Does the very late appearance of a mCherry signal in co-infected cells simply reflect constraints imposed by transcriptional patterns , or is this truly due to recombinants being assembled and matured very late in infection ? We used two approaches to investigate this question . In the first approach we took the two overlapping fragments of the pE/L-mCherry-cro gene that are encoded separately on the pE/L-mCherry ( t ) and mCherry-cro viruses , and incorporated them into a single virus separated by a drug-selectable marker ( Fig 1A , pE/L-mCherry ( dup ) ; S7 Video ) . Although the tandem duplication is unstable , mixed stocks of parental and recombinant viruses can be obtained by continued selection for the drug-resistance marker . These two kinds of viruses can be differentiated , based on the fact that any pre-existing recombinants in the virus stocks should begin to synthesize mCherry-cro protein immediately after uncoating , while the presence of parental ( i . e . non-recombined ) VACV pE/L-mCherry ( dup ) in the virus stock can be demonstrated by PCR . The viruses that still retain the duplication are also expected to exhibit a delay in mCherry expression , but should still be capable of generating recombinant genes without necessitating factory fusion . What kind of timing characterizes this second class of mCherry expression kinetics ? BSC-40 EGFP-cro cells were infected with pE/L-mCherry ( dup ) virus at MOI = 0 . 5 , which according to a Poisson distribution maximized the chance ( ~90% ) that each cell was infected with just one of the two predicted kinds of viruses . As expected we observed two distinct populations of viruses expressing mCherry . The first cluster of virus-infected cells exhibited mCherry expression kinetics identical to those previously exhibited by the pE/L-mCherry-cro control virus; tfb = 0:40 ( Fig 5A , panel k ) versus tf = 0:35 ( Fig 2A , panel h ) . In contrast a second cluster of virus-infected cells exhibited mCherry expression kinetics significantly different from any seen previously . In these cells a mCherry signal was not detected until tfa = 3:20 ( Fig 5A , panel n ) and the level of expression was markedly lower . To compare the two distinct populations seen in cells infected with partially duplicated virus , with other viruses , we measured the timing of mCherry expression relative to the first appearance of the factories in these cells . Twelve data points were collected for each virus as well as 12 of each kinetic class in pE/L-mCherry ( dup ) infected cells . These results , along with the data from Fig 2 and Fig 3 , are summarized in Fig 6 . Although one cannot determine with certainty when the “late” class of recombinants are being produced in cells infected with the pE/L-mCherry ( dup ) virus , it is apparent that these recombinants are already assembled by the time the associated late promoter is activated . We also tested a second approach for measuring recombination timing and capacity . This method is based upon the observation that any DNA transfected into VACV-infected cells is replicated [28] within the virus factories [29] . This process is expected to create a large pool of substrate available for plasmid-by-virus recombination and in intimate contact with replicating virus genomes . In this experiment the BSC-40 EGFP-cro cells were first transfected with a plasmid encoding a promoterless copy of the mCherry-cro open reading frame ( Fig 1A ) , 4 h prior to infection with pE/L-mCherry ( t ) virus at a MOI = 5 . A complicating factor is that the transfected DNA is also stained with EGFP-cro ( Fig 5B , panel b; S8 Video ) , but while these structures look superficially like virus factories , they are seen at time zero . The timing for the appearance of true virus factories ( tf = 0:00 ) was most accurately determined by tracking growing factories backwards to their initiating point . Interestingly , recombinant mCherry was detected at tf = 3:25 ( Fig 5B , panel k ) a time essentially identical to that exhibited by the “late” class of recombinants formed in cells infected with the pE/L-mCherry ( dup ) virus ( Fig 6 ) . Collectively , these experiments show that as long as there are no other physical impediments to recombination , a newly assembled recombinant gene under regulation of a VACV late promoter can be detected as soon as the promoter is activated . However , when the recombining elements are located in trans , on different viruses , the formation of a recombinant gene is further significantly delayed and well beyond the time point when the late reporter gene promoter is shown to become active . The implication is that this class of recombinant viruses is not assembled or matured until very late in infection . One cannot determine a recombinant frequency using purely optical methods , nor do these methods provide direct evidence of recombinant gene formation . A combination of western blotting , Southern blotting , and plaque counts , were used to measure these parameters in cells co-infected with pE/L-mCherry ( t ) and mCherry-cro viruses ( Fig 7A ) . BSC-40 cells were separately infected , or co-infected , for 24 h with viruses encoding the truncated [pE/L-mCherry ( t ) ] and/or promoterless ( mCherry-cro ) fluorescent proteins . Whole-cell lysates were then fractionated and western blotted to detect mCherry antigens ( Fig 7B ) . An ~18 kDa N-terminal fragment was detected in cells infected with just the pE/L-mCherry ( t ) virus and lesser amounts of the same parental peptide were detected in cells co-infected with the two viruses ( Fig 7B , lanes 3 and 5 ) . Most critically , two recombinant peptides were detected in the co-infected cells and migrating at positions characteristic of mCherry ( ~26 kDa ) and mCherry-cro ( ~35 kDa ) proteins ( Fig 7A , lanes 5–7 ) . Judging by the control , both proteins are expressed by a recombinant pE/L-mCherry-cro virus ( Fig 7B , lane 7 ) . Proportionately more of the recombinant mCherry peptide , relative to the parental mCherry ( t ) peptide was also detected in cells co-infected at MOI = 5 versus MOI = 1 ( Fig 7B , lanes 5 and 6 ) . We also used plaque assays to measure the fraction of recombinant viruses formed during a single round of infection . BSC-40 cells were co-infected with the pE/L-mCherry ( t ) and mCherry-cro viruses , at a combined MOI = 5 , cultured overnight , and the progeny recovered by freeze-thaw 24 h post-infection . The viruses were then plated on BSC-40 cells and counted to determine the proportion of plaques exhibiting any degree of mCherry-positivity . The experiment was repeated three times and we detected 12 ± 1% red fluorescent recombinant plaques . Unfortunately this approach greatly overestimates the true recombinant frequency as was subsequently illustrated by the difficulties we had trying to detect recombinant genomes by Southern blotting . Two DNA probes were prepared that targeted either the pE/L poxvirus promoter , or sequences encoding the cro peptide ( Fig 7A ) . The pE/L probe should detect a 5 . 5 kbp DNA fragment encoded by a parental virus [pE/L-mCherry ( t ) ] and a 0 . 8 kbp fragment diagnostic for the recombinant virus , while the cro DNA probe is expected to detect 5 . 2 kbp DNA fragments encoded by the other parent ( mCherry-cro ) and by recombinant viruses ( Fig 7A ) . These hybridization patterns were confirmed when total cellular DNA was extracted and Southern blotted from cells infected with either of the two parental viruses ( Fig 8B , lanes 2 and 4 ) , or with a virus duplicating the anticipated recombinant ( pE/L-mCherry-cro; Fig 8B , lane 3 ) . However , when DNA was extracted at 24 h post-infection from cells co-infected with the pE/L-mCherry ( t ) and mCherry-cro viruses , at a combined MOI = 5 , we were unable to detect the 0 . 8 kbp fragment that is diagnostic for recombinant viruses ( Fig 8B , lane 5 ) . Further rounds of plaque purification showed that the mixture of viruses recovered from cells co-infected with the pE/L-mCherry ( t ) and mCherry-cro viruses do contain recombinant viruses , and these can be detected by Southern blotting . We picked four different red fluorescent plaques , performed one more round of plaque purification ( again selecting for MPA-resistant and fluorescent viruses ) , grew up small stocks under MPA selection , and Southern blotted the DNA from these viruses . Some of the partly purified viruses now exhibited the 0 . 8 kbp restriction fragment diagnostic for a recombinant ( Fig 7B , lanes 8 and 9 ) , although two rounds of plaque purification were clearly still not sufficient to generate pure stocks of recombinants . The challenge with these particular viruses is that it is hard to identify plaques formed by pure recombinants . We noticed that the plaques formed by viruses recovered from cells co-infected with the pE/L-mCherry ( t ) and mCherry-cro viruses exhibited a variable degree of red fluorescence . When we counted only plaques qualitatively exhibiting a high level of fluorescence , comparable to authentic pE/L-mCherry-cro recombinants , the recombinant frequency dropped to 1 . 9 ± 0 . 6% and suggested that many of the “recombinant” plaques might have been composed of a mix of parental viruses that produced recombinants subsequent to plating . The conclusion is that the propensity of VACV to form mixed plaques is a confounding factor , one that must be considered when attempting to measure recombinant frequencies using only plaque assays . Because of these concerns , we repeated the experiment using a different pair of viruses . One was the pE/L-mCherry ( t ) virus used in the preceding study , which also encodes the gpt marker that was used in virus construction [more properly it should be labeled pE/L-mCherry ( t ) -gpt] . The second encodes a LacZ marker replacing the gpt locus and expresses mCherry protein ( pE/L-mCherry-lacZ ) . These viruses exhibit phenotypes of being either mCherry- LacZ- or mCherry+ LacZ+ and share a comparable amount ( 0 . 5 kbp ) of homology with the preceding crosses , spanning the mCherry locus ( Fig 9A ) . This strategy also eliminated the cro peptide , which seemed to have deleterious effects on viral replication as observed in the viral growth curves ( S1 Fig ) . We infected BSC-40 cells with the two viruses either separately or together at MOI = 5 , for 24 h , harvested total cellular DNA , and performed a Southern blot analysis using a probe specific for the synthetic poxvirus E/L promoter ( Fig 9A ) . In parallel we plated the progeny virus on BSC-40 cells in the absence of selection and scored them using fluorescence microscopy followed by X-gal staining . Similar to the previous assay , we only scored mCherry+ plaques exhibiting a high level of red fluorescence . Using this approach we could accurately differentiate between viruses clearly exhibiting the parental ( mCherry- LacZ- or mCherry+ LacZ+ ) versus recombinant ( mCherry+ LacZ- or mCherry- LacZ+ ) phenotypes , since a mCherry+ LacZ- recombinant is not easily confused with a mCherry+ LacZ+ parent . The Southern blotting detected a small fraction of recombinant genomes exhibiting novel 2 . 2 kbp and 0 . 9 kbp restriction fragments . These comprised 1 . 1% and 1 . 2% of the total viral DNA ( Fig 9B , lane 4 ) . Although this represented a small proportion of recombinants , in this experiment the numbers were close to those determined by plaque assays . The virus stocks isolated at 24 h post-infection contained 2 . 5% ± 0 . 6% and 2 . 8% ± 0 . 6% of the mCherry+ LacZ- and mCherry- LacZ+ recombinants , respectively , between four independent experiments . Given the good agreement between Southern blotting and plaque assays in this second experiment , and considering that the extent of homology was essentially the same in the two different types of crosses , we concluded that the events detected optically at the cellular level are probably associated with production of about 1–3% recombinants . These results still leave unanswered questions relating to why recombinant gene products are not detected until very late in infection and why such low recombination frequencies are detected when the genes are located in trans . One clue was provided by a different kind of experiment , one suggested by the earlier work of Katsafanas and Moss [30] . In addition to the pE/L-mCherry-cro virus ( Fig 1 ) , we had also previously constructed a virus encoding EGFP instead of mCherry protein [pE/L-EGFP-cro ( Fig 10 ) ] . Interestingly , we observed that some of the factories seen in cells co-infected with the mCherry-cro and EGFP-cro viruses were uniformly stained with mCherry protein , while other factories in the same cell were tagged with the EGFP protein ( Fig 10; S9 Video ) . This showed that when the reporter protein is virus encoded it is not always freely diffusible . A possible explanation is that the bounding membranes that have been seen by electron microscopy [26] might be sufficiently contiguous as to limit protein movement between the factories originating as co-infecting viruses . These membranes are proposed to derive from the endoplasmic reticulum [26 , 31] , and if they were mostly intact then the DNA-binding proteins being synthesized on ER-associated ribosomes , might preferentially relocate to DNA binding sites located on the same side of the ER membrane . This then led us to wonder whether these ER boundaries might also continue to segregate the enclosed viroplasm , even after the factories have fused into larger assemblages . To examine this question we used fluorescence microscopy to image the distribution of ER membranes in VACV factories at different times in the infection cycle . An antibody targeting the ER marker calreticulin [32] was used to track the distribution of ER membranes . At an early time point ( 4 h ) , the calreticulin marker was distributed throughout the cytoplasm and also seen excluded from regions containing the virus DNA ( Fig 11 ) . It was not seen within the small factories at this stage in their development . However , later in the infection cycle ( 8 h ) , when many factory fusion events would have been expected to occur , the ER marker was seen forming a reticulated pattern within the now larger assemblages ( Fig 11 ) . The calreticulin stain could be traced through the optical sections , outlining a number of what look like subdomains within the larger structures . This can be traced through a series of separate image stacks spanning >1 μm ( Fig 11B ) . Elsewhere in this particular image one can see less intimately fused factories , clearly separated by opposed bounding membranes ( Fig 11A , 8 h ) . We interpret these images to mean that even though VACV factories are seen fusing during the course of infection , this process would not necessarily lead to DNA mixing , due to the continued presence of one or more of the original bounding ER membranes . These membranes are disassembled as virus assembly starts late in infection [26] , and perhaps only then can the DNAs of co-infecting viruses mix well enough to permit recombination in trans .
These studies provide insights into when recombinant genes can be formed during VACV replication and how that process is affected by the arrangement of the recombining fragments in cis ( i . e . on the same genome ) , or in trans ( on different genomes ) . The technology is somewhat constrained by the limits imposed by the kinetics of virus promoter activation , nevertheless some important general features of poxvirus recombination are illustrated by these studies . These experiments employed EGFP and mCherry fluorescent proteins fused to a phage lambda cro DNA binding domain . The cell-encoded EGFP-cro protein permitted tracking of replicating virus particles , while modified forms of virus-encoded mCherry-cro protein permit detection of gene rearrangements . Controls showed that a mCherry-cro signal is detected very shortly after new factories are first tagged with EGFP-cro in cells infected with pE/L-mCherry-cro virus . The ~35 min gap from the appearance of the first factories would likely be related to the time needed to fold the newly expressed mCherry protein ( ~15 min ) and to concentrate it enough to see as DNA is exposed in newly uncoated viruses . Like the EGFP-cro protein that we have previously studied , mCherry-cro associated with both factories and the nucleus , but exhibits a preference for VACV DNA ( S3 Video ) . A different pattern of mCherry-cro expression was seen in cells infected with the pE/L-mCherry ( dup ) viral construct . Viruses encoding partly duplicated DNA segments , such as this one , are unstable unless one maintains selection for the parental virus [33] . When cells were infected at low multiplicities of infection with these viruses , one can see two different patterns of mCherry expression with timing characteristic of either early or late VACV promoters ( Fig 6 ) . The first class of events , which express mCherry very shortly after the first factories are detected , presumably reflect pre-existing recombinants that were formed during preparation of the virus stock . The same mCherry expression kinetics was seen as in cells infected with the pE/L-mCherry-cro control virus . More interesting is the second class of recombination events seen in cells infected with the pE/L-mCherry ( dup ) virus , where mCherry is not detected until the activation of late promoters ( ~3:20 after factories are first detected ) . This presumably reflects the transcription and translation of recombinant genes formed during the preceding period of DNA replication . We have not tried to establish further the exact timing of such events , although one could probably narrow it down more using promoters based upon those regulating post-replicative genes . Most probably these reactions take place throughout the period of DNA replication when the enzymes needed to catalyze both viral replication and recombination are present . The same timing of appearance of a late mCherry signal ( tf = 3:25 ) is also seen in cells transfected with a plasmid encoding a promoterless copy of the mCherry-cro gene and infected with pE/L-mCherry ( t ) virus . A feature common to both situations is that all of the interacting genetic components would be mixed closely together within the factories and from an early stage in virus development . In the case of the pE/L-mCherry ( dup ) this is because of the physical linkage of the recombining elements , in the case of the transfected cells it is because non-specific DNA replication of transfected DNAs [28] takes place in viral factories [29] . Quite different expression kinetics were seen in cells co-infected with the pE/L-mCherry ( t ) and mCherry-cro viruses . In this case a recombinant can only be assembled through an exchange between gene fragments located in trans on different genomes and through a reaction requiring second order reaction kinetics . Given that each factory is understood to begin as a single infecting particle [25 , 30 , 34] , the fusion of different factories bearing different VACV genotypes would seem to be required in advance of any recombinant forming reactions . The time it takes to observe factory fusion is a function of the multiplicity of infection , although even at high multiplicities of infection a small portion of viral factories never fuse [25] . In these current studies , we saw varying times to fusion , but in the example shown in Fig 2B ( panel g ) , we detected the first mergers very shortly after factories first appeared ( tf = 0:35 ) and these were followed by further aggregation of the different virus factories into larger assemblies over the next few hours . Not all fusion events would necessarily aggregate viruses comprising the two different genotypes , but over the long course of infection at a combined MOI = 5 at least some co-mingling of different virus genotypes is bound to occur . Interestingly , even though fusion events were observed throughout the period of virus replication in co-infected cells , it wasn’t until an average tf = 5:20 that the first signs of recombinant mCherry-cro protein were detected ( Fig 6 , S4 Video ) . This is two hours after the late class of recombinants were detected in cells infected with the pE/L-mCherry ( dup ) virus and roughly coincident with the point when the factories started to exhibit a more diffuse appearance . As the mCherry signal appeared , it showed up simultaneously in all the factories , rendering it impossible to determine if it originated from a particular source . These observations suggest that while factory fusion would seem to be needed to mix the genotypes essential for recombination in trans , this alone is not sufficient to create the environment needed to produce recombinants . Otherwise one would expect to have seen at least some mCherry signals appearing as soon as the E/L promoter was activated in co-infected cells and around the same time as the late class of recombinants were detected in cells infected with the pE/L-mCherry ( dup ) virus . Also notable was the low intensity of the fluorescent signal . This could be related to reduced levels of transcription and translation by that time point . However , Southern blotting and plaque assays also detected about 1–3% recombinant genomes and virus plaques , reflective of the low level of recombinant protein expression . These observations suggest that recombination between co-infecting VACV is restricted by more factors than just factory fusion . One thing that we had noted previously , using fluorescence in situ hybridization ( FISH ) , was that even after the factories have merged , a large portion of the DNA encoding the two different genotypes remained segregated within the larger structures [25] . Moreover we ( Fig 10 ) , and others [30] , see some evidence that virus-encoded proteins are not always freely diffusible between different factories . In this regard J . Locker’s previous studies concerning the ultrastructure of VACV replication sites become highly relevant [26] . Her electron micrographs showed that rapidly growing viral factories are nearly completely ( 80–85% ) bounded by membranes from the endoplasmic reticulum . Moreover , these extensive bounding membranes are disassembled as immature virions begin to form . One might expect that were factories to fuse during the course of infection , only along the boundary between two different fused factories would there be any initial opportunity for DNA to mix . However , such mixing would be greatly limited if what once comprised that boundary was “fenced in” by stable membranes and the virus DNA perhaps further constrained by the DNA and membrane binding protein E8 [35] . This is precisely what is seen in larger late factories prior to their dissolution , the ER marker calreticulin enclosing the viroplasm within different subdomains ( Fig 11A ) . Although the VACV factories are fusing , they appear to retain what we presume are the original bounding membranes . It then starts to become clear why a recombinant mCherry signal doesn’t start to appear until after the well-demarked and larger late factories have started to break down into more diffuse structures ( Fig 11B ) . Presumably only then are virus DNAs finally able to mix freely . Of course by this time point , the capacity to process recombination intermediates into mature and intact DNA duplexes would also start to go into decline as VACV transitions from the replication phase into the assembly phase . The cumulative effect would be to limit the amount of recombinants formed in co-infected cells . These observations do explain one of the more confusing features of poxvirus biology , which is that transfected molecules exhibit extraordinarily high levels of recombination , while viruses do not . For example , one can detect high levels of recombinant formation among DNAs transfected into Shope fibroma virus and VACV infected cells , with linkage lost beyond 300–500 bp in some experiments using Shope fibroma virus infected cells [13 , 36] . In contrast , the current study detected only 1–3% recombinants formed in a single co-infection and this is in rough agreement with data from genome sequencing [~1 exchange per 10 kbp [12]] . The simplest explanation is that poxvirus recombination systems are very active , but transfected DNA also mixes well and is not subjected to the same constraints that viruses are . In conclusion , these studies show that where DNAs can mix intimately , recombinant viruses are detected as soon as the promoters and reporter genes permit their detection . However , two co-infecting VACV seem to face several impediments to recombination , in trans , that collectively delay and reduce the yield of recombinant viruses . Most importantly , even though the factories formed by different co-infecting viruses can fuse throughout the infection cycle , the bounding ER membranes would probably continue to limit complementation and partially isolate the different genotypes as they are being replicated . The DNA cannot mix , and recombinants cannot form , until these structures are disassembled . However , by that point the systems that might catalyze recombination are in competition with processes associated with virus assembly , greatly reducing the capacity to produce recombinants . This intriguing biology would tend to be a stabilizing factor in virus evolution , and disfavor accumulation of defective interfering particles in culture , and it raises interesting questions regarding how it might have affected the evolutionary trajectory of such apparently ancient [37] and successful viral pathogens .
African green monkey kidney epithelial cells ( BSC-40 ) were purchased from the American type culture collection ( ATCC ) and grown in modified Eagle’s medium supplemented with non-essential amino acids , L-glutamine , antibiotics/antimycotics , and 5% fetal bovine serum , which were all purchased from Thermo Fisher Scientific . BSC-40 cell lines constitutively expressing the bacteriophage lambda cro protein fused to either enhanced green fluorescent protein ( EGFP-cro ) or mCherry fluorescent protein ( mCherry-cro ) , were prepared as previously described [25] . All of the recombinant viruses used in this study were derived from VACV strain Western Reserve , our stock was originally obtained from the ATCC . A virus encoding VACV A5 protein fused to yellow fluorescent protein ( YFP ) was obtained from Dr . B . Moss [30] . Growth curves used BSC-40 cells infected with virus at a multiplicity of infection ( MOI ) of 3 . The virus were harvested at the indicated time point , released by freeze-thaw , diluted , and the yield determined by plaque assay on BSC-40 cells . The viruses were prepared by first cloning parts ( or all ) of a gene encoding mCherry fluorescent protein fused to the phage lambda cro DNA binding domain into plasmid pTM3 [38] and flanked by VACV thymidine kinase gene sequences . A detailed description of how each of the precursor plasmids was first assembled is provided as supplementary material ( S1 Methods ) . To generate each virus , BSC-40 cells were first infected with VACV at a MOI of 3 for 2 h followed by transfection of the linearized recombinant plasmid using Lipofectamine 2000 ( Invitrogen ) , and the recombinant viruses then isolated using modified Eagle’s medium supplemented with 25μg/mL mycophenolic acid , 15μg/mL hypoxanthine , and 250μg/mL xanthine ( Sigma ) . The viruses were plaque purified at least three times and purified by centrifugation through a sucrose cushion [25] . Fig 1A illustrates the different viruses assembled for this study . Note that the virus called “pE/L-mCherry ( dup ) ” is intrinsically unstable , no doubt due to recombination [33] , but the duplication can be maintained by continued passage in media containing mycophenolic acid . All of the live-cell imaging studies were performed using an Olympus IX-81 spinning-disc confocal microscope equipped with a heated cell chamber and providing a 5% CO2 atmosphere . Briefly , the cells were first cultured on optically clear 35 mm glass bottom dishes ( Fluorodish , World Precision Instruments ) and then infected with virus for 1 h at 4°C in serum-free MEM containing 10 mM HEPES pH 7 . 2–7 . 5 . The inoculum was then replaced with warmed FluoroBrite Dulbecco’s modified Eagle’s media ( Thermo Fisher Scientific ) supplemented with 10 mM HEPES pH 7 . 2–7 . 5 , nonessential amino acids , and 5% fetal bovine serum and incubated for another hour at 37°C . The dishes were sealed with Parafilm , and mounted on the 37°C microscope stage . For virus-by-plasmid recombination imaging , 4 h prior to initiating infection ( as described above ) 2μg of linearized plasmid DNA was transfected into EGFP-cro cells using Lipofectamine 2000 . Image data were collected using a 40×/1 . 3-numerical aperture ( NA ) oil PlanApoN objective at 5-minute intervals using Volocity software ( Perkin-Elmer ) . EGFP was detected using the fluorescein isothiocyanate ( FITC ) filter set and mCherry was detected using the red fluorescent protein ( RFP ) filter set . Ten separate fields of view were typically recorded in a given experiment . For fixed-cell imaging , the cells were first seeded on glass cover slips in 24-well plates and infected for 1 h at 4°C in serum-free MEM supplemented with 10 mM HEPES pH 7 . 2–7 . 5 . The inoculum was replaced with fresh warmed MEM supplemented with nonessential amino acids , L-glutamine , antibiotics/antimycotics , and 5% fetal bovine serum; and returned to the 37°C incubator until the desired time point was reached . The samples were fixed at 4°C overnight using 4% paraformaldehyde in phosphate-buffered saline ( PBS ) , and then quenched with 0 . 1 M glycine for 30 min . The cells were permeabilized with PBS containing 0 . 1% Triton-X100 ( PBS-T ) , counter-stained with 0 . 1 μg/mL 4’ , 6-diamidino-2-phenylindole ( DAPI , Molecular Probes ) in 50% ( v/v ) Odyssey blocking buffer ( Li-Cor ) in PBS , washed with PBS-T , washed again with PBS and mounted using Mowiol mounting medium ( 0 . 1 mg/ml Mowiol , 0 . 1 M PBS , pH 7 . 4 , 25% glycerol , 2 . 4% triethylenediamine [DABCO] ) . Where indicated , an antibody recognizing calreticulin was used ( Abcam AB2907 ) to visualize endoplasmic reticulin . The fixed cell images were acquired using a 60×/1 . 42 NA oil PlanApoN objective using DAPI , FITC , RFP , and CY5 filter sets . Two approaches were used to determine the recombinant frequencies . In the first , BSC-40 cells were co-infected at a combined MOI = 5 with a 1:1 mix of mCherry-cro and pE/L-mCherry ( t ) viruses ( Fig 1A ) . Fresh media was added after 1 h and the plates returned to the incubator overnight . The cells were harvested next day ( 24 h ) , by scraping them into the medium , the virus released by three rounds of freeze-thaw and plated at high dilution on BSC-40 cells in 6-well dishes . A Zeiss inverted fluorescence microscope was used to detect and count well-resolved mCherry-positive plaques using an RFP filter set and DIC optics . The plates were then stained with crystal violet to determine the total plaque count . In a second experiment , BSC-40 cells were co-infected at a combined MOI = 5 with a 1:1 mix of pE/L-mCherry-lacZ ( Fig 9 ) and pE/L-mCherry ( t ) viruses ( Fig 1A ) . The viruses were cultured and plaqued , and the mCherry positive plaques were identified by fluorescence microscopy as described above . The plates were then stained with 5-bromo-4-chloro-3-indolyl-β-D-galactopyranoside to differentiate LacZ+ viruses from those bearing the guanosylphosphoribosyl transferase marker on the [pE/L-mCherry ( t ) ] virus . BSC-40 cells were cultured in 10 cm dishes and then infected with virus as described above . Twenty four hours post infection , the cells were harvested into cold PBS , centrifuged at 1 , 000× g for 3 min and lysed on ice in radioimmunoprecipitation assay ( RIPA ) buffer ( 50mM Tris-HCl pH7 . 4 , 150mM NaCl , 1mM EDTA , 1% NP-40 , 0 . 25% Na-deoxycholate ) containing 1× protease inhibitors ( Roche ) . The samples were clarified by centrifugation , and boiled briefly in sample buffer ( 50mM Tris·HCl pH6 . 8 , 3 . 7% sodium dodecyl sulfate , 0 . 6M β-mercaptoethanol , 1 . 5mM bromophenol blue , in 40% glycerol ) . The samples were size fractionated on 12 or 15% SDS-polyacrylamide gels , and then transferred to a nitrocellulose membrane ( Thermo Fisher Scientific ) . The membranes were incubated overnight at 4°C with appropriate antibodies . These included mCherry ( 1:2 , 000 diluted rabbit polyclonal; Clontech ) , VACV I3 ( 1:5 , 000 diluted mouse monoclonal antibody [39] ) , VACV A34 ( 1:10 , 000 diluted rabbit polyclonal antibody; this laboratory ) , and/or β-actin ( 1:20 , 000 diluted mouse monoclonal antibody; Sigma ) . The membranes were subsequently exposed to a 1:20 , 000 diluted secondary antibodies bearing infrared dyes ( goat-anti-rabbit 680 and goat-anti-mouse 800; Li-Cor ) for 1 h at room temperature and imaged using a Li-Cor Odyssey scanner . Gel images were analyzed using FIJI [40] . DNA was purified from virus-infected BSC-40 cells by phenol/chloroform extraction and ethanol precipitation , digested with XhoI or HindIII ( Fermentas ) , and size fractionated on 0 . 7% agarose gels . The DNA was denatured in situ in a solution containing 0 . 5M NaOH and 1 . 5M NaCl , transferred to a nylon membrane ( Pall Corporation ) , and fixed by UV cross-linking . A biotin-containing cro-gene probe was prepared using two primers ( 5’-TGATGGAACAACGCATAA-3’ and 5’-TTATGCTGTTGTTTTTTTGTTAC-3’ ) , biotin-16-dUTP ( Roche ) , template DNA , and the PCR . A second probe was purchased from IDT as a biotin-labeled oligonucleotide ( 5’-biotin-AAAAATTGAAATTTTATTTTTTTTTTTTGGAATATAA-3’ ) . It detects the synthetic early-late promoter driving mCherry gene expression . The probes were hybridized to the prepared membrane using ExpressHyb ( Clontech ) , and detected using streptavidin conjugated to IRDye 800CW ( Li-Cor ) and an Odyssey Li-Cor scanner . Image data files were exported as either Volocity or Softworx files and then assembled into composite images using FIJI and Photoshop CS6 [40] . The images acquired in each experiment were subjected to the same scaling adjustments using only linear gamma factors . Labels were added to the video images using Camtasia 2 . 0 . Greyscale images were prepared using Adobe Photoshop CS6 and GraphPad Prism v6 was used for statistical analyses .
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Recombination plays a critical role in DNA repair and also creates the genetic diversity that underpins evolution . This has important implications for viruses , since recombination may create new pathogens with new infectious properties . It has long been known that hybrids can be recovered from cells co-infected with related viruses , some of the first artificial recombinants were produced >50 years ago from variola and rabbitpox viruses . A particular property of poxviruses is that they replicate in membrane-wrapped cytoplasmic structures called “factories” , and each of these factories develops from a single infecting particle . However , if each genome is isolated inside different factories , when and how does the DNA mix to permit recombination ? To examine this question , we have developed a fluorescence-based virus recombination assay . Using live cell confocal microscopy , we have timed these reactions and observed that recombinants can be quickly formed when the recombining sequences are located on the same virus genome . However , when the gene fragments are located on different viruses , there is a significant delay ( and a reduction ) in recombinant gene formation . This delay supports the hypothesis that factories , and the ER-derived cell membranes that surround factories , impede recombination in poxvirus-infected cells .
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[
"Abstract",
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2016
|
Live-Cell Imaging of Vaccinia Virus Recombination
|
Type I interferons ( IFN-I ) broadly control innate immunity and are typically transcriptionally induced by Interferon Regulatory Factors ( IRFs ) following stimulation of pattern recognition receptors within the cytosol of host cells . For bacterial infection , IFN-I signaling can result in widely variant responses , in some cases contributing to the pathogenesis of disease while in others contributing to host defense . In this work , we addressed the role of type I IFN during Yersinia pestis infection in a murine model of septicemic plague . Transcription of IFN-β was induced in vitro and in vivo and contributed to pathogenesis . Mice lacking the IFN-I receptor , Ifnar , were less sensitive to disease and harbored more neutrophils in the later stage of infection which correlated with protection from lethality . In contrast , IRF-3 , a transcription factor commonly involved in inducing IFN-β following bacterial infection , was not necessary for IFN production but instead contributed to host defense . In vitro , phagocytosis of Y . pestis by macrophages and neutrophils was more effective in the presence of IRF-3 and was not affected by IFN-β signaling . This activity correlated with limited bacterial growth in vivo in the presence of IRF-3 . Together the data demonstrate that IRF-3 is able to activate pathways of innate immunity against bacterial infection that extend beyond regulation of IFN-β production .
Type I interferons ( IFN-I ) are expressed by macrophages and epithelial cells as part of the first line of defense against infection , and the IFN-I receptor ( IFNAR ) is expressed by most cells [1] . IFN-I signaling following bacterial infection leads to the production of pro-inflammatory cytokines and chemokines and promotes apoptosis of infected cells [2] . In some cases , a pathologic role for IFN-I activation has also been described [3] . Interferon regulatory factor 3 , IRF-3 , is a major transcription factor that induces IFN-I following cytosolic detection of a pathogen [4] . Toll-like receptor ( TLR ) activation or other host pattern recognition receptors signal independent of the adaptor MyD88 to phosphorylate IRF-3 and activate IRF-3 dependent innate immune defenses [5] . Following phosphorylation , IRF-3P is found in the nucleus where it forms a complex with p300 which can act as a potent transcription factor , binding to interferon stimulated response elements ( ISREs ) on target genes , including Ifnβ [6] , [7] . Secreted IFN-β binds IFNAR and signaling through STAT-1 and STAT-2 ( signal transducers and activators of transcription ) induces transcription of hundreds of ISRE-containing genes including many pro-inflammatory cytokines and chemokines . Further amplification of IFN-β expression occurs through an autocrine loop that requires IFNAR , IRF-3 and a second transcription factor IRF-7 and all three proteins play key roles in the expression of IFN-I [8] . Intracellular pathogens , such as Listeria monocytogenes , trigger activation of IRF-3 and subsequent induction of IFN-β as a result of their escape from the phagolysosome and replication within the host cytosol [9] . Subsequent IFN-β signaling in leukocytes , not necessarily infected by L . monocytogenes , promotes apoptosis , which is thought to result in a reduction of the number of effector cells available to defend against the infection [10] , [11] , [12] . Further immune suppression is caused by an IRF-3-dependent down-regulation of the IFN-γ receptor on macrophages , rendering them unresponsive to type II IFN [13] . Thus , for Listeria , IFN-I signaling is exploited as a virulence mechanism , allowing the bacterium to disarm the host immune system . However , other intracellular bacterial pathogens , such as Legionella pneumophila are successfully combated by the IFN-I response , and the replication of these bacteria appears directly affected [14] , [15] , [16] . Release of bacterial DNA following phagocytosis commonly activates IRF-3 , leading to pro-inflammatory cytokine production necessary for neutrophil recruitment and bacterial clearance [17] , [18] , [19] , [20] , [21] . Furthermore , cytosolic activation of the AIM2 inflammasome requires IRF-3 and IFN-β which subsequently initiate caspase-1-dependent pyroptosis and the secretion of the pro-inflammatory cytokines IL-1β and IL-18 [22] . However , IFN-I signaling can also inhibit the inflammasome from being activated via NLRP3 [23] . These data demonstrate that downstream effects of IFN signaling can be dependent on the mechanism of activation . Some bacteria , such as Yersinia pestis , are highly inflammatory even though they synthesize an altered lipopolysaccharide structure that poorly stimulates TLRs [24] . Deletion of Tlr2 or Tlr4 does not increase sensitivity or resistance to Y . pestis infection in mouse models , indicating that these pathways are likely not activated during the infection [24] , [25] , [26] . In the lungs , Y . pestis establishes an anti-inflammatory environment that is permissive to bacterial replication [27] . Following this initial anti-inflammatory state , robust neutrophil recruitment in response to the pathogen occurs but is ineffective [28] . Loss of CXC-chemokine signaling , a major pathway for neutrophil recruitment and activation , results in increased sensitivity to plague [29] . Though neutrophils are recruited early to infected lungs of Cxcr2−/− mice , they have reduced capacity to limit bacterial growth . Together these data suggest that wild type Y . pestis induces multiple pathways of neutrophil chemotaxis and is at least partially resistant to neutrophil killing . In contrast , if Y . pestis lack the pigmentation locus ( pgm− ) , a 102 kb chromosomal deletion that attenuates virulence , CXCR2 is not required for host defense suggesting that the pgm locus may be involved in neutrophil resistance . Infection by Y . pestis causes plague , a lethal disease that is characterized by rapid bacterial growth , massive pro-inflammatory responses and tissue necrosis which lead to the rapid demise of mammalian hosts including humans [28] , [30] , [31] , [32] . Late stage disease involves vascular dissemination , rapid replication of extracellular bacteria and the development of high titer septicemia . In contrast , early infection may involve an intracellular stage , as the bacteria survive well but grow slowly inside activated macrophages [28] , [33] . Perhaps due to this intracellular form , Y . pestis is thought to initially suppress inflammation causing an apparent biphasic inflammatory response to infection [28] , [32] , [34] , [35] . Production of IFN-γ , normally an effective response to activate macrophages and other phagocytic cells to destroy extracellular pathogens , is initially prevented and when given as a therapeutic to mice during the first 24 hrs post-infection , the bacteria are cleared without development of disease [36] , [37] , [38] . Recently , a related pathogen , Y . pseudotuberculosis , was shown to induce MyD88-independent type I IFN and NF-κB activation following infection of macrophages in vitro [39] . Infection resulted in increased transcription of the IFN regulatory factor , IRF-1 , as well as many IFN-responsive genes . Activation of type I IFN and NF-κB were not caused by intracellular bacteria , as they could not be prevented by cytochalasin D . Instead , the bacterial type III secretion system , but not its secreted effector proteins , was shown to be necessary for induction of both IFN-I and NF-κB responses leading to the hypothesis that the host may sense insertion of the type III translocation pore to activate innate immunity . However , these studies left unresolved the role of the IFN-I response in Yersinia pathogenesis . In this work , we investigated the role of IFN-I during pulmonary infection of mice by pgm− Y . pestis [40] . We found that transcription of Ifnβ was induced in the lungs early , and then declined . Ifnar-deficient mice were significantly more resistant to infection compared to wild type suggesting that IFN-I contributes to the pathogenesis of plague . Neutrophil depletion in the bone marrow became pronounced in wild type mice compared to Ifnar−/− even though both strains of mice initially developed a similar systemic infection . In contrast , the IFN-β transcription factor IRF-3 was required for host defense in a manner that was not dependent on IFN . Bacterial growth and inflammation proceeded more rapidly in Irf3−/− mice and in vitro , Y . pestis infection of bone marrow derived macrophages and neutrophils from the mutant mice resulted in decreased phagocytosis . Together , the data demonstrate the importance of IRF-3 to the basic process of phagocytosis , suggesting an interferon-independent role for the transcription factor during bacterial infection .
Intranasal infection of non-pigmented ( pgm− ) strains of Y . pestis leads to lethal septicemic plague , with little to no bacterial growth in the lungs , effectively slowing the progression of disease [40] . In this model , mice that were pre-treated with inorganic iron more uniformly progressed towards lethal septicemic plague compared to untreated mice over 5–9 days . In order to identify host genes that may be important to Y . pestis infection , we challenged wild type C57BL/6 mice that had been pre-treated with iron with the pgm− strain KIM D27 by intranasal infection . On days 2 , 4 and 7 , mice were euthanized , lungs , liver and spleen homogenized in sterile PBS and used to measure bacterial load and host gene expression . We found that bacteria disseminated early and could be recovered from the liver and spleen at 2 days post-infection after which they typically either replicated and caused lethal disease ( 5 of 6 mice survived until day 7 ) or appeared to be slowly clearing the infection ( Figure 1A ) . As early as 2 days post-infection , we observed a significant increase in transcription of Ifnβ in the lungs which peaked on day 4 then declined ( Figure 1B ) . Ip10 ( Cxcl10 ) , a pro-inflammatory cytokine activated by IFN-β , also appeared induced in the lungs on day 2 . Other IFN-responsive genes such as Mx1 were also significantly increased on day 2 post-infection whereas Ifnα was not induced by Y . pestis ( Table S1 ) . In contrast , mRNA levels of cytokines Ifnγ and Tnfα peaked late during infection on day 7 , a time where animals showed signs of acute disease . Together , it appears that Yersinia pestis activate type I IFN during pulmonary infection . Furthermore , similar to previous reports , we found the expression of type II IFN and the NF-κB-responsive Tnfα to be initially suppressed , only being activated after the onset of disease [28] , [32] . RAW 264 . 7 cells , a monocyte-derived macrophage cell line , were infected with Y . pestis KIM D27 and expression of cytokines was measured by real time PCR . These results demonstrated a significant increase in expression of Ifnβ and Ip10 mRNA at 4 hrs post-infection whereas Ifnα4 was not induced ( Figure 1C ) . Together the data suggest that macrophages produce IFN-β following infection by Y . pestis and may respond to it by activating expression of pro-inflammatory cytokines . We therefore sought to understand the role of IFN-β in the progression of plague . To understand the role of type I IFN signaling to Yersinia pestis infection , we studied susceptibility of mice lacking the IFN-I receptor , IFNAR , to pulmonary infection by Y . pestis . Mice lacking Ifnar were not more sensitive to infection , and in fact , they were more resistant with a significant reduction in mortality and increase in time to disease ( Figure 2A ) . Bacterial growth in the lungs , liver and spleen of Ifnar−/− mice appeared similar to wild type early during infection and in each , growth by more than 3 orders of magnitude was seen between days 2 and 4 ( Figure 2B ) . In contrast , wild type mice continued to progress and increased bacterial titers were recovered on day 7 whereas bacterial clearance in Ifnar−/− mice had occurred at this time point and only one mouse had recoverable bacteria . We also examined serum cytokines in these mice . Along with bacterial titer , pro-inflammatory cytokines in the serum of both strains of mice were similar early and no gross differences were apparent on days 2 and 4 post-infection for any cytokine ( Figure 2C–H ) . By day 7 , most pro-inflammatory cytokines were lower in Ifnar−/− mice compared to wild type , consistent with their reduction in bacterial load . Together these data suggest that IFN-I signaling becomes detrimental during the later stage of infection . Given that differences in host responses and bacterial clearance were evident between days 4 and 7 post-infection , we assayed the infection on day 5 in more detail to identify the host responses that differed between strains and determine how these influenced disease pathology . Similar to what we observed on days 2 and 4 , bacterial load in the lung , liver and spleen on day 5 appeared reduced in Ifnar−/− mice compared to wild type but these differences were not statistically significant ( Figure 3A ) . In addition , serum samples revealed similarities in the secretion of pro-inflammatory cytokines ( Figure 3B shows a subset of cytokines measured ) . Fixed tissues from these mice were examined by histopathology and immunohistochemistry . Pathological analysis indicated similarities in overall severity of lesions and degree of inflammation between wild type and mutant , with correlation between increased bacterial titers and disease progression ( Figure 3C ) . Upon further examination , we found a correlation between infiltration of polymorphonuclear cells with reduced bacterial titer in the liver and spleen of Ifnar−/− mice whereas for wild type mice , polymorphonuclear cells appeared less common in these tissues . Immunohistochemistry suggested these PMNs were Gr-1+ and therefore were likely neutrophils and/or monocytes ( Figure S1 ) . Caspase-3 staining appeared similar in both the liver and spleen of wild type and Ifnar−−/− mice , with the most pronounced staining in the necrotic lesions ( data not shown ) . Together , the results suggest a systemic , IFN-β-dependent reduction in neutrophil/monocyte populations in wild type mice in the late stage of infection . Since we observed an apparent systemic neutropenia that might be dependent on IFNAR , we asked whether neutrophils in the bone marrow were also reduced in wild type mice as disease progressed . Bone marrow was isolated from tibia and femurs , then fixed and stained with anti-Ly6G/6C and the cells examined by flow cytometry . Bone marrow from wild type and Ifnar−/− mice that were not infected contained similar numbers of neutrophils ( Ly6Ghi , Figure 4 ) . However , on day 5 post-infection , wild type mice harbored significantly fewer Ly6Ghi cells in the bone marrow compared to Ifnar−/− mice . Thus , on day 5 , neutropenia is more pronounced in wild type mice than in Ifnar−/− mice . This observation may explain why wild type mice lose control over the infection , while Ifnar−/− mice are able to contain it . Together these data support a model whereby IFNAR signaling contributes to the depletion of immune cells during Y . pestis infection . We also studied the sensitivity of mice lacking the IFN-β transcription factors Irf3 or Irf7 . We challenged wild type , Irf3−/− and Irf7−/− mice by intranasal infection of Y . pestis KIM D27 and followed development of acute disease over a 14 day period . At this challenge dose , 30% of wild type mice developed lethal disease on days 5–9 post-infection ( Figure 5A ) . Similarly , 50% of Irf7−/− mice developed plague with a similar time to lethal disease . However , Irf3−/− mice were significantly more sensitive and 90% developed lethal disease in only 4 days . Pre-treatment of mice with iron did not substantially impact development of disease in Irf3−/− mice as those that did not receive iron also developed an increased rate of mortality compared to wild type ( Figure S2 ) . To determine whether IRF-3 was responsible for synthesis of type I IFN during Y . pestis infection , we challenged wild type , Irf3−/− and Irf7−/− mice with Y . pestis KIM D27 and measured gene expression in the lungs on days 2 and 4 post-infection . While it appeared that Ifnβ expression was decreased in Irf3−/− and Irf7−/− mice compared to wild type on day 4 post-infection , expression of this cytokine was not fully dependent on either transcription factor ( Figure 5B ) . Similarly , Ip10 expression was not dependent on IRF-3 or IRF-7 , and in fact , increased expression of Ip10 was observed in both strains of mutant mice . Thus , Ifnβ expression is not likely to depend on IRF-3 or IRF-7 following Y . pestis infection of the lung . Bacterial load was examined in the lungs , liver , and spleen of wild type and mutant mice on days 2 , 3 and 4 post-infection . Results showed similar bacterial titers in all three tissues on day 2 post-infection , and in several animals in all groups the bacteria were undetectable ( Figure 6A , Figure S3 ) . In striking contrast , rapid bacterial growth occurred over the next 24 hrs in Irf3−/− mice but not in wild type or Irf7−/− mice . We also examined disease pathology by staining formalin fixed lungs , liver and spleen with hematoxylin and eosin ( H&E ) . Total pathological severity scoring of lungs , liver and spleen indicated a disease in Irf3−/− mice consistent with bacterial sepsis on day 3 post-infection , with a large degree of necrosis in infected tissues and increased inflammation , while wild type mice appeared to have less necrosis and inflammation ( Figure 6B ) . All mice harbored mild lesions on day 2 post-infection , and in the liver of Irf3−/− mice there were increased inflammatory foci , many of which contained dying cells while in wild type mice small neutrophilic foci formed that typically contained intact cells ( Figure 6C ) . Disease in Irf3−/− mice progressed rapidly and on day 3 , degenerating neutrophilic inflammatory foci were still present in the liver , but multiple large necrotic lesions were also observed in both the liver and spleen ( Figure 6D–E ) . In contrast , neutrophilic foci in the liver of wild type mice remained comprised primarily of intact cells and there was minimal damage to the spleen . Bacterial colonies could be seen in some areas of necrosis in the liver of mutant mice and these appeared larger and more numerous as disease progressed suggesting that rapid bacterial growth caused accelerated plague in the Irf3−/− mice ( Figure 6F ) . Noticeably , intact neutrophils were generally absent in areas of bacterial colonies which were instead surrounded by necrotic tissue . Likewise , spleens were necrotic in moribund mice ( Figure 6G ) , and , overall , pathology in moribund mice appeared similar in both groups . Lungs of wild type mice had neither inflammation nor disease while Irf3−/− mice had developed mild inflammation in the lungs on day 3 ( Figure S4 ) . Together the data suggest that Irf3−/− and wild type mice succumbed to septicemic plague though the mutant mice developed severe disease more rapidly . In contrast to Irf3−/− mice , all tissues of Irf7−/− mice examined had fewer lesions , mild to moderate inflammatory foci , and were indistinguishable from wild type ( Figure S5 ) . We also analyzed serum cytokine production in wild type and Irf3−/− mice on days 2 and 3 post-infection . Both strains harbored relatively low levels of pro-inflammatory cytokines in the serum on day 2 with no statistical significance between wild type and Irf3−/− mice for all cytokines analyzed ( Figure 7A–H ) . In striking contrast , however , Irf3−/− mice produced significantly higher levels of pro-inflammatory cytokines in the serum on day 3 post-infection compared to wild type , whereas IL-17 , IL-5 and MIP-1α were not detectably different between the strains ( data not shown ) . Together , the data suggest a burst in the production of inflammatory cytokines occurred between days 2 and 3 post-infection for Irf3−/− mice . Since this is the same time point where both bacterial growth and tissue necrosis became substantially greater in Irf3−/− mice compared to wild type , these data suggest that bacterial growth or the subsequent tissue injury may have triggered a massive pro-inflammatory response . Immunohistochemistry confirmed the identity of macrophages , monocytes and neutrophils and revealed the presence of apoptosis in the red pulp of the spleen ( Figure S6 ) . Inflammatory foci of the liver appeared to contain both macrophages and neutrophils and there was no caspase-3 staining in the degenerated foci . In the spleen , red pulp necrosis that stained positive for cleaved caspase-3 appeared to involve both macrophages and neutrophils . Together the phenotypic data suggest that Irf3−/− mice experience more extensive and accelerated tissue damage that correlates with an increase in bacterial growth . Previous work has shown that IFN-I helps limit intracellular growth of L . pneumophila in alveolar epithelial cells while , during L . monocytogenes infection of macrophages , IFN-I signaling reduces expression of IFN-γ receptor thereby preventing activation of macrophages by IFN-γ [13] , [14] . We therefore sought to determine whether IRF-3 and IFN-I had an impact on bacterial uptake and survival in macrophages . Towards this end , we isolated bone marrow derived macrophages ( BMDMs ) from wild type , Irf3−/− and Ifnar−/− mice and performed a gentamicin protection assay to enumerate intracellular bacteria following infection . BMDMs were pre-treated with PBS , anti-IFN-β or IFN-γ for 4 hrs prior to infection with Y . pestis KIM D27 . Bacteria ( 1×107 CFU ) grown at 37°C were added to the macrophages at a multiplicity of infection of 10 ( time 0 ) and incubated for 30 min before adding gentamicin to kill extracellular bacteria . After 90 min incubation in gentamicin ( time 2 hr ) , macrophages were lysed and intracellular bacteria enumerated by plating on agar medium . The results showed that 5–6% of Y . pestis was intracellular in wild type and Ifnar−/− BMDMs while only 2–3% of Y . pestis was intracellular in Irf3−/− BMDMs , a difference that was reproducible and statistically significant ( Figure 8A ) . This happened in the presence and absence of anti-IFN-β showing that IFN-β signaling does not appear to affect phagocytosis . Further , BMDMs from Ifnar−/− mice had no defect in phagocytosis compared to wild type . Treatment with IFN-γ yielded results similar to untreated in all mouse strains . BMDMs from wild type and mutant mice were able to kill intracellular Y . pestis similarly as approximately 0 . 5–1% of the bacteria found at 2 hrs were still present at 6 hrs post-infection . IFN-γ treatment had little effect in all three mouse strains , with each showing 0 . 5–1% recovery of bacterial titer between 2 and 6 hrs post-infection . These data suggest Irf3−/− macrophages have a defect in an early stage of phagocytosis . We also measured cell death in these samples by determining LDH release caused by the infection compared with detergent-lysis of BMDMs . These results showed no detectable differences in cytotoxicity between wild type , Ifnar−/− and Irf3−/− mice suggesting that macrophage viability following infection is not dependent on IRF-3 ( Figure 8B ) . Neutrophils play a key role in restriction of Y . pestis growth in vivo [41] . Furthermore , neutrophilic function is modified following injection of YopH by the Yersinia type III secretion system , a virulence mechanism that blocks intracellular calcium signaling and contributes to resistance of extracellular bacteria to neutrophilic killing [42] . We therefore wondered if the increase in susceptibility of Irf3−/− mice to plague might be due to a defect in bacterial phagocytosis by neutrophils . To address this , we measured phagocytosis of Y . pestis by bone marrow derived neutrophils ( BMNs ) from wild type and Irf3−/− mice . Similar to macrophages , approximately 10% of the bacteria were taken up by neutrophils from wild type mice whereas only 1% of the infecting dose was taken up by Irf3−/− neutrophils suggesting IRF-3 is necessary for phagocytosis ( Figure 8C ) . However , once internalized , neutrophils from Irf3−/− bone marrow were capable of killing Y . pestis to a similar degree as wild type neutrophils . When examined for cell death by LDH release , BMNs showed no detectable differences between wild type or Irf3−/− cells during this time period ( Figure 8D ) . Further , no significant differences were detected in anti-IFN-β treated compared to untreated BMNs from either wild type or Irf3−/− mice over the 6 hr time period examined . Together these data suggest that IRF-3 may be required for efficient phagocytosis of Y . pestis . Further , since anti-IFN-β did not affect phagocytosis , it appears the effect of IRF-3 on this pathway is not mediated by IFN-β . Since neutrophils are known to be important to host defense against Y . pestis , these data suggest that a defect in phagocytosis may be responsible for the increase in susceptibility of Irf3−/− mice [43] . Our septicemic plague model involves intranasal delivery of the non-pigmented laboratory strain Y . pestis KIM D27 which lacks 102 kb of chromosomal DNA including a high pathogenicity island [44] . We therefore wanted to understand the relevance of IRF-3 following challenge with fully virulent Y . pestis . Intranasal infection of the wild type Y . pestis strain KIM5 leads to primary pneumonic plague over a period of 3–4 days , and Irf3−/− mice succumbed to disease with an indistinguishable time course and mortality rate ( Figure 9 ) . Similar results were obtained following challenge with Y . pestis CO92 , another fully virulent strain from the Orientalis , rather than Mediavalis , biovar ( data not shown ) . Together the data suggest that the wild type bacteria bypass IRF-3 either because its contribution to host defense is less pronounced against the fully virulent bacteria or wild type Y . pestis carry virulence factors in the pgm locus that silence IRF-3's role in host defense . We also tested an isogenic non-pigmented mutant strain of KIM5 isolated by plating on congo red agar to verify that the role of IRF-3 is not limited to a lab-adapted Y . pestis strain isolated in 1965 [45] . This strain ( KIM5− ) was then used to challenge wild type and Irf3−/− mice by intranasal inoculation . Similar to D27 , challenge with Y . pestis KIM5− led to an apparent acceleration of disease in Irf3−/− mice compared to wild type , though for the single trial we performed , these differences were not significant ( p = 0 . 28 , Figure S7 ) . These data suggest that the role of IRF-3 in host defense is not specific to bacterial strain isolate and support a role for IRF-3 in preventing septicemic plague .
Yersinia pestis requires high blood titer to enable its transmission to fleas and persistence in the environment [46] . Its virulence , therefore , has evolved to promote replication to high titers in the blood , an environment that becomes overwhelmed by anti-bacterial defense mechanisms that attempt to prevent this massive growth . Multiple interactions between virtually all arms of the innate immune system and Yersinia determine the outcome of infection , with each having downstream consequences that may contribute further to disease . In this work , we addressed the role of type I interferon ( IFN-I ) activation in mice following Y . pestis infection . We identified production of IFN-β in the lungs early following pulmonary infection and found this was not dependent on the transcription factors IRF-3 or IRF-7 . Production of type I IFN led to increased susceptibility to plague in a manner that correlated with systemic neutrophil depletion . IRF-3 , however , was necessary for host defense and its expression led to decreased susceptibility to plague in a manner that correlated with decreased bacterial growth . Together these data raise new insight into the innate immune response . Wild type Y . pestis can survive and even replicate in macrophages yet cause disease due to exponential growth of extracellular bacteria that secrete cytotoxins ( Yops ) and other virulence factors into host cells [34] , [47] , [48] , [49] . Thus , the bacteria interface with the host in multiple environments where they may be recognized by surface-located and intracellular pattern recognition receptors or may perturb intracellular signaling through direct interactions involving the Yops of the type III secretion system ( Figure 10 ) . Bacterial pathogen recognition by one or more of these pathways , leads to phosphorylation of IRF-3 and perhaps also activation other interferon regulatory factors . Phosphorylation of IRF-3 causes its migration to the nucleus where it can form an active DNA binding complex , likely with p300 , but possible alternative DNA binding complexes cannot be ruled out . In this manner , IRF-3 not only activates transcription of IFN-β but may also stimulate up-regulation of key mediators of phagocytosis . Additional work is needed to identify genes that are affected by IRF-3 , whether the same IRF-3 has alternative phosphorylation or interactions in the nucleus that affect its activity or if other interferon regulatory factors are responsible for IFN-β production . IRF-3 phosphorylation allows its nuclear migration and multiple phosphorylation sites of IRF-3 have been demonstrated [6] . TRIF activation by other bacterial pathogens typically leads to the production of IFN-β which can limit the infection by stimulating the expression of pro-inflammatory cytokines and chemokines such as IP-10 or RANTES , thereby inducing neutrophil recruitment [20] , [50] . Here we found evidence that phagocytosis may require IRF-3 activation . Though we do not yet know if this requirement is caused by production of a secreted factor such as a cytokine or other inflammatory mediator , expression of proteins involved in bacterial uptake , or even if additional neutrophil or macrophage functions are dependent on IRF-3 , it is clear that IRF-3 may have a greater number of target genes than previously appreciated . Phosphorylated IRF-3 is a potent transcription factor with affinity for the conserved interferon stimulated response element ( ISRE ) on the promoters of hundreds of target genes in addition to IFN-β . IFN-independent anti-viral gene expression has been previously described involving IRF-3 and many of these induced proteins have been identified including those that regulate translation initiation , cell proliferation , apoptosis of infected cells , and others with unknown function [51] , [52] , [53] , [54] . Regulation of these genes is complex , with different cell types and different tissues activating distinct genes through IRF-3 and it is clear that the method of stimulation of IRF-3 following viral infection can lead to different programs of downstream gene expression [55] . Future experiments addressing IRF-3-dependent , IFN-independent gene expression during Y . pestis infection will be important to identify the proteins that are necessary for the antibacterial effects of IRF-3 . Wild type Y . pestis are known to survive better than pgm− strains inside activated macrophages and , in our analyses , we showed that the absence of IRF-3 had minimal effect on protection against infection by wild type bacteria . It is conceivable that the wild type pulmonary infection , which results in death due to acute bronchopneumonia , progresses so rapidly that IRF-3 protection is overwhelmed by the developing lung injury or that resident phagocytic cells of the lung are unable to activate the anti-bacterial defense mechanism mediated by IRF-3 . Alternatively , IRF-3-dependent phagocytosis may be neutralized by one or more gene products encoded by the pgm locus . Pgm-encoded ripA is a virulence factor necessary for intracellular bacterial growth , but not phagocytosis , in IFN-γ activated bone marrow derived macrophages [56] . At least two additional virulence factors are believed to be encoded in the pgm locus , only one of which is known: the siderophore , yersiniabactin , whose deletion severely attenuates virulence [57] . Future studies combining mouse and bacterial genetics will facilitate an understanding of the mechanism whereby wild type Y . pestis induce or evade IRF-3 . Our results suggest that it is likely that multiple transcription factors activate IFN-I during Y . pestis infection , as the absence of IRF-3 or IRF-7 did not result in a complete loss of Ifnβ production . Recently , expression of IRF-1 was shown to be induced following Y . pseudotuberculosis infection though the role of this transcription factor during Yersinia infection has not been investigated [39] . Thus , it is conceivable that IRF-1 or even another IRF is responsible for IFN-β production . Alternatively , it may be that small amounts of IFN-I that remain in Irf3−/− and Irf7−/− mice are sufficient to cause immune cell depletion such that deletion of multiple IRFs are required to achieve the phenotype caused by deletion of IFNAR . During Listeria infection , surface expression of IFN-γ receptor is down-regulated on macrophages making them less able to kill intracellular bacteria . We therefore analyzed phagocytosis and bacterial killing in macrophages lacking IFN-I signaling in the presence and absence of IFN-γ activation . In these experiments , we did not detect differences between wild type and Ifnar mutant macrophages in their ability to be activated by IFN-γ . Further , we did not observe a decrease in the production of pro-inflammatory cytokines such as KC or MCP-1 that are normally IFN-γ-dependent in the Ifnar−/− mice . Together these data suggest that the IFN-γ receptor is not down-regulated by IFN-I during Y . pestis infection . It is therefore likely that the downstream effects of IFN-I are somewhat pathogen-specific . Our data found a link between IFN-I and neutrophil depletion and we propose that either neutrophils or another population responsible for their maturation and/or recruitment undergoes cell death in response to IFN-I signaling during Y . pestis infection ( Figure 10 ) . Depletion of neutrophils or other immune cells that is associated with type I IFN has been reported for several pathogens , including Francisella , Listeria and influenza virus . Over 40 genes known to be involved in regulating apoptosis are ISGs , thus a role for IFN-induced immune cell death in host susceptibility to plague will require the study of potentially many genes and their effects on Y . pestis pathogenesis . Y . pestis joins a growing list of pathogens , such as Listeria monocytogenes , Mycobacterium tuberculosis , Staphylococcus aureus and Francisella tularensis that enhance virulence through IFNAR [10] , [11] , [12] , [58] , [59] , [60] , [61] , [62] . Consistent in all of these models , the detrimental effect of IFN-I appears more pronounced under conditions of high pathogen burden . Yet , the molecular mechanism remains elusive . Because of the clear benefits of IFN-I on viral and cancer defense , use of this cytokine in humans is currently under investigation . Thus it is imperative that its potential side effects against bacterial infection be recognized and understood so they can be avoided while gaining the full therapeutic benefits of type I IFN .
Fully virulent Y . pestis KIM5+ was grown fresh from frozen stock by streaking for isolation onto heart infusion agar ( HIA ) plates supplemented with 0 . 005% Congo Red and 0 . 2% galactose to screen bacteria that retain the pigmentation locus [63] . For pneumonic plague challenge studies , a single pigmented colony was used to inoculate heart infusion broth ( HIB ) supplemented with 2 . 5 mM CaCl2 and grown 18–24 hrs at 37°C , 120 rpm . All handling of samples containing live Y . pestis KIM5+ was performed in a select agent authorized BSL3 facility under protocols approved by the University of Missouri Institutional Biosafety Committee . Non-pigmented Y . pestis strain KIM D27 was routinely grown fresh from frozen stock on HIA , followed by aerobic growth at 27°C in HIB overnight prior to use in experiments . A naturally occurring non-pigmented variant of the fully virulent Y . pestis KIM5+ strain was isolated following plating on Congo Red agar [63] , [64] . Deletion of the pigmentation locus on the chromosome was verified by PCR as previously described prior to use in experiments [40] . All animal procedures were in strict accordance with the Office of Laboratory Animal Welfare and the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the University of Missouri Animal Care and Use Committee . Wild type C57BL/6 mice were commercially obtained from Charles River Laboratories ( MA , USA ) . C57BL/6 mice were the inbred strain background of the Irf3−/− , Irf7−/− , and Ifnar−/− mice which were kind gifts of Drs Michael Diamond and Herbert Virgin [65] , [66] . Mice were bred and raised at the University of Missouri barrier housing facilities . Male and female wild type and mutant mice , ranging from 15–30 g were used for challenge experiments . During challenge with fully virulent Y . pestis and the isogenic non-pigmented mutant , mice were maintained in select agent authorized animal biosafety level 3 facilities at the University of Missouri . All infected mice were monitored regularly by daily weighing and assignment of health scores . Animals that survived to the end of the 14 day observation period or were identified as moribund ( defined by pronounced neurologic signs and severe weakness ) were euthanized by CO2 asphyxiation followed by bilateral pneumothorax or cervical dislocation , methods approved by the American Veterinary Medical Association Guidelines on Euthanasia . Non-pigmented strains of Y . pestis , grown as described above , were diluted in sterile PBS to 1×106 CFU/0 . 02 ml just prior to use in challenge experiments . Actual dose was determined by plating in triplicate on HIA . Unless otherwise indicated , for intranasal infections involving pgm− Y . pestis strains , mice were given 50 µg FeCl2 by intraperitoneal injection just prior to challenge . For challenge with fully virulent Y . pestis , bacteria were diluted to 2×103 CFU/0 . 02 ml sterile PBS . All animals were lightly anesthetized by isoflurane inhalation just prior to infection . Immediately after euthanasia , blood was collected directly from the heart by cardiac puncture . Lungs , spleens and livers were removed , and half of each tissue was processed for bacterial load by homogenizing in 1 ml sterile PBS . Serial dilutions of homogenized tissues were then plated in triplicate onto HIA plates for quantification of bacterial titer ( CFU/organ ) . Serum was collected from the blood following centrifugation and stored at −80°C until analyzed . Approximately half of each tissue was placed in 10% formalin for 96 hrs . Lungs were first perfused with sterile PBS , removed and sectioned , then perfused with 10% formalin for histological analysis . Fixed tissues were embedded in paraffin , trimmed and stained with hematoxylin and eosin ( H&E ) . For histologic scoring of tissues , slides were evaluated in a single blind fashion by a veterinarian with expertise in pathology . Lesions observed as well as the severity scores ( 0 to 3 ) were documented for lungs , liver and spleen . For enumeration of pyogranulomatous inflammatory foci in the liver , 10 fields per tissue were counted on each slide . Tissues that had been fixed in 10% formalin and paraffin-embedded as described above were sectioned for immunohistochemical analysis . Sections were stained with anti-rat F4/80 ( AbD Serotec , Oxford , UK ) , monoclonal antibody NIMP-R14 ( Santa Cruz Biotechnology , CA , USA ) [67] or anti-rat Caspase-3 ( Trevigen , MD , USA ) and detection was achieved by secondary staining with biotinylated rabbit anti-rat IgG and HRP-streptavidin ( DAKO , CA , USA ) . Staining and detection were carried out according to the manufacturer's guidelines . For scoring , ten fields were counted for positive caspase-3 staining on each slide and scored from 0–3 ( 0 = no caspase-3 staining , 1 = infrequent caspase-3 staining , 2 = moderately frequent caspase-3 staining , 3 = positive caspase-3 staining in majority of tissue ) . Approximately half of the lung tissue was homogenized in RNAlater ( Qiagen , CA , USA ) . RNA isolation was performed using RNeasy Mini Kit according to manufacturer's instructions ( Qiagen , CA , USA ) . Total RNA was treated with Turbo DNase ( Ambion , TX , USA ) to remove genomic DNA contamination . First strand cDNA synthesis was carried out using MMLV-RT ( Promega , WI , USA ) on 2 µg of total RNA as per manufacturer's instructions . SYBR Green PCR master mix ( Applied Biosystems , CA , USA ) was used along with gene specific primers ( Table S2 ) to detect the presence of amplified product . Results were analyzed using relative quantification on 7300 SDS software ( Applied Biosystems , CA , USA ) . Data were normalized to the mouse gene Ywhaz , which is constitutively expressed with minimal change ( [19] , data not shown ) . Blood from C57BL/6 , Irf3−/− and Ifnar−/− mice was centrifuged and serum used for cytokine analysis with Premix 22-plex kit ( Millipore , MA , USA ) according to manufacturer's instructions and analyzed by Illuminex using IS 100 software ( Qiagen , CA , USA ) . IL-2 , IL-4 and IL-9 were undetectable in all samples and therefore they were removed from further analysis . BMDMs were isolated from C57BL/6 , Irf3−/− and Ifnar−/− mice essentially as previously described by culturing for 6 days in Dulbecco's modified Eagle's medium ( DMEM ) containing 20 ng/ml M-CSF ( eBiosciences , CA , USA ) in place of L cell media [68] . Twenty-four hours prior to infection , 1×106 cells were seeded into 12-well plates with DMEM containing 20 ng/ml M-CSF , 10% fetal bovine serum ( FBS ) . Where indicated , macrophages were pre-treated with 1 µg/ml anti-IFN-β or 500 µg/ml IFN-γ ( Abcam , Cambridge , MA , USA ) per well for 4 hrs prior to infection . Primary bone marrow-derived neutrophils were isolated from femurs of wild type C57BL/6 or Irf3−/− mice . Neutrophils were enriched by separation on a three-layer Percoll ( Sigma-Aldrich , St . Louis , MO ) gradient . Purity of the isolated cells was assessed by microscopy at >95% neutrophils . Neutrophils were used in assays within 1 h of purification . Where indicated , neutrophils were pre-treated with PBS or anti-IFN-β 10 min prior to infection . Overnight cultures of Y . pestis KIM D27 were diluted 1∶20 , incubated at 27°C for 3 hrs then shifted to 37°C for 1 hr . Bacteria were infected at a multiplicity of infection ( MOI ) of 10 and the plates were centrifuged at 86×g for 5 min before incubating at 37°C , 5%CO2 . Following 30 min , cells were washed with sterile PBS and incubated in cell culture medium containing 40 µg/ml gentamicin and the infection continued for an additional 5 . 5 hrs . For bacterial titer , media was aspirated and wells were washed once with sterile PBS . Infected macrophages and neutrophils were lysed with 300 µl of 0 . 1% Triton X-100 in PBS , cells were then washed once with PBS , serially diluted and plated in triplicate on HIA for enumeration of colony forming units . To ensure that only intracellular bacteria had been enumerated , 10 µl of aspirated media was plated on HIA for each well and no bacterial growth was recovered in these samples ( data not shown ) . Lactate dehydrogenase ( LDH ) content was measured in the aspirated media collected from BMDMs infected as described above after 2 or 6 hrs of infection . Samples were run in triplicate using the CytoTox-One kit ( Promega , WI , USA ) and the manufacturer's instructions . Percent cytotoxicity was assessed by comparing the amount of LDH in the supernatant to that recovered from control BMDMs that were not infected following lysis by 0 . 1% triton X-100 . Bone marrow cells from C57BL/6 and Ifnar−/− mice were isolated just prior to infection or on day 5 post-infection ( dpi ) from tibia and femurs using cold PBS . Cells were incubated with Fc receptor blocking solution ( BioLegend , CA , USA ) to eliminate non-specific binding for 10 min prior to fixing in 4% paraformaldehyde for 20 min . Cells were then washed 2× with cold PBS at 1200×g for 8 min and stained with FITC-conjugated anti-mouse Ly6G/6C antibody ( BD Biosciences , CA , USA ) for 30 min . Cells were washed 3× with cold PBS and analyzed on MoFlo XDP using Summit software ( Beckman Coulter , UT , USA ) . Data from all trials were analyzed for statistical significance . Statistical analyses were performed using R ( Gehan Wilcoxan , Kruskal Wallis ) or GraphPad prism ( Wilcoxan match paired , Student's t test , ANOVA , Mantel Cox ) software [69] .
|
Type I interferons ( IFN-I ) broadly stimulate innate immunity against viral , bacterial and parasitic pathogens . Many bacterial pathogens induce IFN-I through phosphorylation of Interferon Regulatory Factor 3 ( IRF-3 ) allowing it to bind promoters containing Interferon Stimulated Response Elements ( ISRE ) which include IFN-β and pro-inflammatory cytokines and chemokines . Secreted IFN-β is taken up by the IFN-αβ receptor ( IFNAR ) , triggering activation of the JAK-STAT pathway which also activates ISRE-containing genes . In this work , we have discovered a novel anti-bacterial function of IRF-3 . We show that the respiratory pathogen , Yersinia pestis , the causative agent of plague , activates IRF-3 and the IFN-I response and that these two events cause opposite outcomes in the host . While IRF-3 is necessary for an early stage of phagocytosis , IFNAR signaling promotes the infection and may directly contribute to neutrophil depletion during infection . These results demonstrate that an IFN-independent function of IRF-3 is important to host defense against bacterial infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biology",
"microbiology",
"host-pathogen",
"interaction",
"bacterial",
"pathogens"
] |
2012
|
Opposing Roles for Interferon Regulatory Factor-3 (IRF-3) and Type I Interferon Signaling during Plague
|
Apoptosis is a highly regulated cell death mechanism involved in many physiological processes . A key component of extrinsically activated apoptosis is the death receptor Fas which , on binding to its cognate ligand FasL , oligomerize to form the death-inducing signaling complex . Motivated by recent experimental data , we propose a mathematical model of death ligand-receptor dynamics where FasL acts as a clustering agent for Fas , which form locally stable signaling platforms through proximity-induced receptor interactions . Significantly , the model exhibits hysteresis , providing an upstream mechanism for bistability and robustness . At low receptor concentrations , the bistability is contingent on the trimerism of FasL . Moreover , irreversible bistability , representing a committed cell death decision , emerges at high concentrations which may be achieved through receptor pre-association or localization onto membrane lipid rafts . Thus , our model provides a novel theory for these observed biological phenomena within the unified context of bistability . Importantly , as Fas interactions initiate the extrinsic apoptotic pathway , our model also suggests a mechanism by which cells may function as bistable life/death switches independently of any such dynamics in their downstream components . Our results highlight the role of death receptors in deciding cell fate and add to the signal processing capabilities attributed to receptor clustering .
Apoptosis is a coordinated cell death program employed by multicellular organisms that plays a central role in many physiological processes . Normal function of apoptosis is critical for development , tissue homeostasis , cell termination , and immune response , and its disruption is associated with pathological conditions such as developmental defects , neurodegenerative disorders , autoimmune disorders , and tumorigenesis [1]–[5] . Due to its biological significance , much effort has been devoted to uncovering the pathways governing apoptosis . Indeed , recent progress has enabled the proliferation of mathematical models , both mechanistic and integrative [e . g . , 6]–[14] , which together have offered profound insights into the underlying molecular interactions . The current work takes a similarly mathematical approach and hence inherits from this legacy . There are two main pathways of apoptotic activation: the extrinsic ( receptor-mediated ) pathway and the intrinsic ( mitochondrial ) pathway , both of which are highly regulated [15] , [16] . In this study , we focus on the core machinery of the extrinsic pathway , which is initiated upon detection of an extracellular death signal , e . g . , FasL , a homotrimeric ligand that binds to its cognate transmembrane death receptor , Fas ( CD95/Apo-1 ) , in a 1∶3 ratio . This clusters the intracellular receptor death domains and promotes the ligation of FADD , forming the death-inducing signaling complex ( DISC ) [17]–[19] . The DISC catalyzes the activation of initiator caspases , e . g . , caspase-8 , through death effector domain interactions . Initiator caspases then activate effector caspases , e . g . , caspase-3 , which ultimately execute cell death by direct cleavage of cellular targets [20]–[23] . Apoptosis is typically viewed as a bistable system , with a sharp all-or-none switch between attracting life and death states . This bistability is important for conferring robustness [24] . Consequently , researchers have used computational models to identify and study potential sources of bistability in apoptosis , including positive caspase feedback [8] , inhibition of DISC by cFLIP [7] , cooperativity in apoptosome formation [10] , double-negative caspase feedback through XIAP [11] , and double-negative feedback in Bcl-2 protein interactions [25] . In this work , we propose that bistability may be induced upstream by the death receptors themselves . The current model of death ligand-receptor dynamics assumes that FasL activates Fas by direct crosslinking , producing a DISC concentration that varies smoothly with the ligand input [26] . However , recent structural data [27] suggests a different view . In particular , Fas was found in both closed and open forms , only the latter of which allowed FADD binding and hence transduction of the apoptotic signal . Moreover , open Fas were observed to pair-stabilize through stem helix interactions . This affords a mechanism for bistability , similar to the Ising model in ferromagnetism [28] , where open Fas , presumably disfavored relative to their native closed forms [29] , are able to sustain their conformations even after removal of the initial stimulus promoting receptor opening , past a certain critical density of open Fas . This induces hysteresis in the concentration of active , signaling receptors and therefore in apoptosis . We studied this proposed mechanism by formulating and analyzing a mathematical model . The essential interpretation is that FasL acts as a clustering platform for Fas , which establish contacts with other Fas through pairwise and higher-order interactions to form units capable of hysteresis ( Figure 1 ) . At low receptor concentrations , the model exhibits bistability provided that the number of receptors that each ligand can coordinate is at least three . This hence gives a theory for the trimeric character of FasL . Furthermore , at high concentrations , for example , through receptor pre-association [30]–[32] or localization onto lipid rafts [33] , irreversible bistability is achieved , implementing a permanent cell death decision . Thus , our model suggests a primary role for death receptors in deciding cell fate . Moreover , our results offer novel functional interpretations of ligand trimerism and receptor pre-association and localization within the unified context of bistability .
Constructing a mathematical model of Fas dynamics is not entirely straightforward as receptors can form highly oligomeric clusters [27] , [33] . A standard dynamical systems description would therefore require an exponentially large number of state variables to account for all combinatorial configurations . To circumvent this , we considered the problem at the level of individual clusters . Each cluster can be represented by a tuple denoting the numbers of its molecular constituents , the cluster association being implicit , so only these molecule numbers need be tracked . In our model , a cluster is indexed by a tuple , where represents FasL and , , and are three posited forms of Fas , denoting closed , open and unstable , and open and stable , i . e . , active and signaling , receptors , respectively . Within a cluster , we assumed a complete interaction graph and defined the reactions ( 1a ) ( 1b ) ( 1c ) ( 1d ) The first reaction describes spontaneous receptor opening and closing; the second , constitutive destabilization of open Fas; the third , ligand-independent receptor cluster-stabilization; and the fourth , ligand-dependent receptor cluster-stabilization ( Figure 2 ) . The orders of the cluster-stabilization events are limited by the parameters and , which capture the effects of receptor density and Fas coordination by FasL , respectively . Although only pair-stabilization ( ) has been observed experimentally [27] , higher-order analogues , for example , as facilitated by globular interactions , are not unreasonable . Formally , these reactions are to be interpreted as state transitions on the space of cluster tuples . However , the reaction notation is suggestive , highlighting the contribution of each elementary event , which we modeled using constant reaction rates ( for simplicity , we set uniform rate constants and for all ligand-independent and -dependent cluster-stabilization reactions of molecularity , respectively ) . Then on making a continuum approximation , we reinterpreted the molecule numbers as local concentrations and applied the law of mass action to produce a dynamical system for each cluster in the concentrations of . Validity of the model requires that the molecular concentrations are not too low and that the timescale of receptor conformational change is short compared to that of cluster dissociation . To study the long-term behavior of the model , we solved the system at steady state ( denoted by the subscript ) . Introducing the nondimensionalizations ( 2a ) ( 2b ) ( 2c ) ( 2d ) ( 2e ) where is a characteristic concentration and is time , and ( 3a ) ( 3b ) ( 3c ) ( 3d ) this is ( 4a ) ( 4b ) where is the nondimensional total receptor density , and is given by considering ( 5 ) and solving with , a polynomial in of degree . Clearly , the model is bistable only if ( two stable nodes must be separated by an unstable node as the model is effectively one-dimensional in ) . We used as a measure of the apoptotic activation of a cluster . In principle , all open receptors contribute to apoptotic signaling , but is small , at least at steady state ( since due to the assumed prevalence of the closed form [29] ) , and so can be neglected . While measures the coordination capacity of FasL and hence may be equated with its oligomeric order ( e . g . , in the biological context ) , an appropriate value for , relating to the total receptor concentration , is somewhat more elusive . Therefore , we began our analysis by performing a simple receptor density estimate . Approximating the cell as a cube of linear dimension m , the associated volume of pL implies the correspondence nM molecules molecules/nm on restricting to the membrane , i . e . , by averaging over the surface area of m . Thus , for a conservative receptor concentration estimate of nM [7] , [9] , [12] , [13] , the number of Fas molecules in the neighborhood of each receptor is only , assuming a charateristic size of nm . We hence found that receptors may be very sparsely distributed . In this low density mode , high-order Fas interactions in the absence of ligand can be neglected ( ) . Therefore , in this context , bistability is possible only if , and the trimerism of FasL thus demonstrates the lowest-order complexity required for bistability . From the form of , this bistability is reversible as a function of the FasL concentration since the governing polynomial for is of degree only at . This suggests that at the cluster level , the cell death decision can be reversed , which may have adverse effects on cellular and genomic integrity . However , irreversible bistability at higher receptor densities may also be achieved . Researchers have observed tendencies for death receptors both to pre-associate as dimers or trimers [30]–[32] and to selectively localize onto membrane lipid rafts [33] . The result of either of these processes may be to increase the local receptor concentration . In this high density mode , we set , as the preceeding approximation is no longer valid . Irreversible bistability then becomes attainable , representing a committed cell death decision . For the remainder of the study , we incorporated both the low and high receptor density regimes into a single model by setting , using as a continuous transition parameter . Furthermore , we set to correspond to observed biology . Calculation of the steady-state activation curves showed that the model indeed exhibits bistability ( Figure 3 ) for reasonable parameter choices ( Methods ) . Thus , we established the possiblity of a novel bistability mechanism in extrinsic apoptosis . The associated hysteresis enables threshold switching between well-separated low and high activation states . Biologically , these define local signals of life and death , which are integrated at the cell level to compute the overall apoptotic response . As per the previous analysis , reversibility of the bistability is dependent on , with irreversibility emerging for sufficiently high . This suggests a bivariate parameterization of , producing a multivalued steady-state surface over -space ( Figure 4 ) . The result is a cusp catastrophe , an elementary object of catastrophe theory , which studies how small perturbations in certain parameters can lead to large and sudden changes in the behavior of a nonlinear system [34] . A more instructive view of the dependence of the model's qualitative structure on and is shown in Figure 5 . We then focused on the activation and deactivation thresholds , respectively , defining the bistable regime . These are the points at which the steady state switches discontinuously from one branch to the other , and are given by the values of at which the hysteresis curve turns , i . e . , at ( Figure 6 ) . We performed a sensitivity analysis of by measuring the effects of perturbing the model parameters about baseline values ( Methods ) . For each threshold-parameter pair , we computed a normalized sensitivity by linear regression . Strong effects of , , and were observed ( Figure 7 ) ; for the corresponding Fas thresholds at , respectively , the parameters , , , and were emphasized . Thus , the bistability thresholds do not appear particularly robust . However , the data reveal that essentially all parameter sets sampled were bistable . This suggests a weaker form of robustness , namely , robustness of bistability , which nevertheless supports life and death decisions over a wide operating range . To probe this further , we sampled parameters with increasing spread about baseline values and computed the fraction of parameter sets that remained bistable ( Methods ) . The results show that has an exponential form ( Figure 8 ) . Extrapolating to , the data suggest an asymptotic bistable fraction of . Hence , robustness of bistability remains substantial even under significant parameter variation . Thus far , we have considered only the apoptotic activation of an individual cluster . To obtain the more biologically relevant cell-level activation , we must integrate over all clusters . In principle , this integration should account for intercluster transport as well as any intrinsic differences between clusters , e . g . , as due to spatial inhomogeneities . Here , however , we provide as demonstration only a very simple integration scheme . Specifically , we assumed that clusters are identical ( apart from their parameter values , which are drawn randomly ) and independent , and that FasL is homogeneous over the cell membrane . Then we can express the normalized cell activation as ( 6 ) where the subscript denotes reference to cluster . A characteristic cell-level hysteresis curve is shown in Figure 9 . As is immediately evident , such integration is a smoothing operator , averaging over the sharp thresholds of each cluster . Thus , the cell-level signal may be graded even though its constituents are not . Note , however , that the lack of a sudden switch from low to high Fas signaling does not necessarily imply the same at the level of the caspases which ultimately govern cell death , as downstream components may possess switching behaviors [7] , [8] , [10] , [11] , [25] . Finally , we sought to outline protocols to experimentally discriminate our model against the prevailing crosslinking model [26] , which we considered in a slightly simplified form [35] . To be precise , the crosslinking model that we used has the reactions ( 7a ) ( 7b ) ( 7c ) where is FasL , is Fas , and is the complex FasL∶Fas for . With ( 8a ) ( 8b ) ( 8c ) ( 8d ) ( 8e ) ( continuing the notational convention that lowercase letters denote the concentrations of their uppercase counterparts ) , the steady-state solution under mass-action dynamics is ( 9a ) ( 9b ) ( 9c ) where ( 10a ) ( 10b ) and ( 11a ) ( 11b ) are the total ligand and receptor concentrations , respectively . In analogy with our proposed model , hereafter called the cluster model , we used ( 12 ) as a measure of the apoptotic signal .
In this work , we showed through analysis of a mathematical model that receptor clustering can support bistability and hysteresis in apoptosis through a higher-order analogue of biologically observed Fas pair-stabilization [27] . Hence we add to the signal processing activities in which receptor clustering has been suggested to participate [37]–[39] . This bistability plays an important functional role by enabling robust threshold switching between life and death states . Significantly , our results indicate potential key roles for ligand trimerism [17] and receptor pre-association [30]–[32] and localization onto membrane lipid rafts [33] . Thus , we provide novel interpretations for these phenomena within the unified context of bistability . Our model suggests an additional cell death decision , supplementing those that have been studied previously [7] , [8] , [10] , [11] , [25] . Critically , the proposed decision is implemented upstream at the very death receptors that initially detect the death signal encoded by FasL . This decision is therefore apical in that it precedes all others in the system . Consequently , it operates independently of all intracellular components and so offers a general mechanism for bistability , even in cell lines with , for example , only feedforward caspase-activation networks [7] , [9] , [13] , [14] . Thus , receptor cluster-activation may explain how an effective apoptotic decision is implemented in such cells . Moreover , this suggests a novel target for induced cell termination in the treatment of disease [1] . We believe that our model provides an attractive theory for the observed biology . Although unlikely to be correct in mechanistic detail , the model may nevertheless reflect reality at a qualitative level . The significance of our work hence lies in its capacity to guide future research . We therefore readily invite experiment , which can reveal the true nature of the molecular mechanisms involved . Given their structural and functional homology , similar investigations on other members of the tumor necrosis factor receptor family may also prove fruitful . Such work serves to further our understanding of the formation and mode of action of complex signaling platforms such as the DISC , which in this view may be considered the macromolecular aggregates of active Fas .
The rationale for the choices is presented in the text; here , we further defend these by noting that no new behaviors are introduced with or . The remaining parameter values were guided by the following considerations . Specifically , we required and due to the assumed stabilities of the receptor species; all other parameters were assumed to be close to . Within these constraints , parameters were selected to ensure that is of the correct order of magnitude [7] , [9] , [12] , [13] . The baseline parameter values used were , , , , , , and . To analyze the effects of variability in the model parameters , parameter values were sampled from a log-normal distribution , characterized by a variation coefficient , defined as the ratio of the standard deviation to the median of the distribution . For the sensitivity analysis , parameter sets were drawn at ; for the robustness analysis , parameter sets were drawn over ; and for the cell-level integration , parameter sets were drawn at . All parameters were drawn about baseline median values . For each threshold-parameter pair , linear regression was performed on the threshold data against the parameter data , each normalized by reference values . For parameters , the reference is the baseline ( median ) value; for thresholds , the reference is the threshold computed at baseline parameters . The normalized sensitivity was defined as the slope of the linear regression . The cluster invariant was derived by considering at steady state , i . e . , with , and identifying . Similarly , the crosslinking invariant was derived by considering at steady state and identifying . For the full forms of the invariants , see Protocol S1 . For the model discrimination computation , parameter sets were drawn from log-normal distributions with median at . The active Fas concentration was calculated for each parameter set for each model at baseline parameters ( for the crosslinking model ) ; for the cluster model , if bistability was observed , one of the stable values of was chosen at random . The invariant error was minimized using SLSQP with a lower bound of for all parameters . All calculations were performed with Sage 4 . 5 [40] , using NumPy/SciPy [41] for numerical computation and matplotlib [42] for data visualization . The Sage worksheet containing all computations is provided in the Supporting Information ( Protocol S1 ) and can also be downloaded from http://www . sagenb . org/home/pub/1224/ or http://www . courant . nyu . edu/~ho/ .
|
Many prominent diseases , most notably cancer , arise from an imbalance between the rates of cell growth and death in the body . This is often due to mutations that disrupt a cell death program called apoptosis . Here , we focus on the extrinsic pathway of apoptotic activation which is initiated upon detection of an external death signal , encoded by a death ligand , by its corresponding death receptor . Through the tools of mathematical analysis , we find that a novel model of death ligand-receptor interactions based on recent experimental data possesses the capacity for bistability . Consequently , the model supports threshold-like switching between unambiguous life and death states; intuitively , the defining characteristic of an effective cell death mechanism . We thus highlight the role of death receptors , the first component along the apoptotic pathway , in deciding cell fate . Furthermore , the model suggests an explanation for various biologically observed phenomena , including the trimeric character of the death ligand and the tendency for death receptors to colocalize , in terms of bistability . Our work hence informs the molecular basis of the apoptotic point-of-no-return , and may influence future drug therapies against cancer and other diseases .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"cell",
"biology/cellular",
"death",
"and",
"stress",
"responses",
"cell",
"biology/cell",
"signaling",
"mathematics",
"biophysics/biomacromolecule-ligand",
"interactions",
"computational",
"biology/systems",
"biology"
] |
2010
|
Bistability in Apoptosis by Receptor Clustering
|
How cells establish and dynamically change polarity are general questions in cell biology . Cells of the rod-shaped bacterium Myxococcus xanthus move on surfaces with defined leading and lagging cell poles . Occasionally , cells undergo reversals , which correspond to an inversion of the leading-lagging pole polarity axis . Reversals are induced by the Frz chemosensory system and depend on relocalization of motility proteins between the poles . The Ras-like GTPase MglA localizes to and defines the leading cell pole in the GTP-bound form . MglB , the cognate MglA GTPase activating protein , localizes to and defines the lagging pole . During reversals , MglA-GTP and MglB switch poles and , therefore , dynamically localized motility proteins switch poles . We identified the RomR response regulator , which localizes in a bipolar asymmetric pattern with a large cluster at the lagging pole , as important for motility and reversals . We show that RomR interacts directly with MglA and MglB in vitro . Furthermore , RomR , MglA , and MglB affect the localization of each other in all pair-wise directions , suggesting that RomR stimulates motility by promoting correct localization of MglA and MglB in MglA/RomR and MglB/RomR complexes at opposite poles . Moreover , localization analyses suggest that the two RomR complexes mutually exclude each other from their respective poles . We further show that RomR interfaces with FrzZ , the output response regulator of the Frz chemosensory system , to regulate reversals . Thus , RomR serves at the functional interface to connect a classic bacterial signalling module ( Frz ) to a classic eukaryotic polarity module ( MglA/MglB ) . This modular design is paralleled by the phylogenetic distribution of the proteins , suggesting an evolutionary scheme in which RomR was incorporated into the MglA/MglB module to regulate cell polarity followed by the addition of the Frz system to dynamically regulate cell polarity .
The ability of cells to generate polarized distributions of signaling proteins facilitates many biological processes including cell growth , division , differentiation and motility [1] . The spatial confinement of the activity of signaling proteins lays the foundation for processes that require localized protein activity [2] , [3] . For instance , directional migration of neutrophils during chemotaxis depends on the dynamic localization of the activated small GTPases Rac and Cdc42 to the front edge of cells where they stimulate the formation of cellular protrusions via actin polymerization while Rho activity is spatially confined to the rear end of cells to drive actomyosin contractility with retraction of cellular protrusions [4] . Similarly , chemotaxing cells of Dictyostelium discoideum exhibit actin polymerization based cellular protrusions at the front that are dependent of the localization of a small Ras-family GTPase [5] . In both systems , the subcellular localization of small GTPases is highly dynamic and changes in response to environmental conditions [4] , [5] . Similar to eukaryotic cells , bacterial cells are highly polarized with proteins localizing to specific subcellular regions , often the cell poles [6] . Two major unresolved questions regarding cell polarity in general are how proteins achieve their correct subcellular localization and how this localization changes dynamically over time . In eukaryotic cells , members of the Ras-superfamily of small , monomeric GTPases have essential functions in regulating dynamic cell polarity [7] . Recent evidence suggests that the function of small Ras-like GTPases in dynamic cell polarity regulation is conserved from eukaryotes to prokaryotes [8] . Ras-like GTPases are binary nucleotide-dependent molecular switches that cycle between an inactive GDP- and an active GTP-bound form [9] . The GTP-bound form interacts with downstream effectors to induce a specific response . Generally , Ras-like GTPases bind nucleotides with high affinities and have low intrinsic GTPase activities [9] . Therefore , cycling between the two nucleotide-bound states depends on two types of regulators: Guanine-nucleotide exchange factors ( GEFs ) , which function as positive regulators by facilitating GDP release and GTP binding , and GTPase activating proteins ( GAPs ) , which function as negative regulators by stimulating the low intrinsic GTPase activity in that way converting the active GTP-bound form to the inactive GDP-bound form [9] , [10] . If placed on a surface , cells of the rod-shaped bacterium Myxococcus xanthus move in the direction of their long axis with a defined leading and lagging cell pole [8] , [11] . Occasionally , however , cells stop and then resume motility in the opposite direction with the old leading pole becoming the new lagging cell pole and vice versa [12] . These events are referred to as reversals and at the cellular level a reversal corresponds to an inversion of the leading and lagging cell poles [8] , [11] . Recent evidence suggests that a signal transduction module consisting of the small , monomeric Ras-like GTPase MglA and its cognate GAP MglB is at the heart of the regulatory system that controls motility and the cell polarity axis in M . xanthus . M . xanthus has two motility systems [11] . The S-motility system depends on type IV pili ( T4P ) , which localize to the leading pole [13] . T4P are thin filaments that undergo cycles of extension , adhesion and retraction [14] , [15] . During a retraction , a force is generated that is sufficiently large to pull a cell forward [16] , [17] . The A-motility system depends on protein complexes often referred to as focal adhesion complexes ( FACs ) that are assembled at the leading pole and distributed along the cell body [18]–[20] . Each FAC is thought to consist of a multi-protein complex that spans the cell envelope [19]–[21] . In a moving cell , FACs remain stationary within respect to the surface on which the cell is moving [18] . The two motility systems function independently of each other; however , their activity is coordinated to generate force in the same direction [22] . During a reversal , the polarity of the two motility systems is inverted synchronously . Several T4P proteins localize in clusters at both cell poles and remain stationary during reversals [23] . In contrast , the PilB ATPase , which catalyzes extensions , primarily localizes to the leading pole , and the PilT ATPase , which energizes retractions , primarily localizes to the lagging cell pole . During reversals , PilB and PilT switch poles thereby laying the foundation for the assembly of T4P at the new leading pole [23] . In the case of the A-motility system , several proteins including AglQ , which is part of the A-motility motor [19] , [21] , AglZ , GltD/AgmU and GltF , which are part of the FACs , localize to the leading cell pole as well as to FACs between reversals [18] , [21] , [24] . During reversals , the polar protein clusters relocate to the new leading cell pole and , in parallel , the FACs are thought to change polarity [18] , [19] , [24] . Therefore , at the molecular level , a reversal involves a switch in the polarity of dynamically and polarly localized motility proteins . MglA functions as a nucleotide-dependent molecular switch to stimulate motility and reversals at the cellular level [25]–[29] . MglA-GTP is the active and MglA-GDP the inactive form [26]–[28] . MglB is the cognate GAP of MglA [26]–[28] . Between reversals MglA-GTP localizes to the leading cell pole while MglA-GDP is distributed uniformly throughout cells [26] , [28] . MglB localizes to the lagging cell pole [26] , [28] . MglA-GTP generates the output of the MglA/MglB module and MglA-GTP is thought to stimulate motility at the leading cell pole by setting up the correct polarity of dynamically localized motility proteins and by stimulating T4P function and FACs assembly [26] , [28] . MglB localizes to the lagging cell pole and excludes MglA-GTP from this pole by converting MglA-GTP to MglA-GDP and , thus , sets up the MglA-GTP asymmetry . In this way , MglA-GTP together with MglB define the leading/lagging polarity between reversals [26] , [28] . The Frz chemosensory system induces cellular reversals but is not required for motility per se ( Blackhart et al . , 1985 ) The Frz system consists of seven protein [30] including the CheA histidine kinase FrzE and the FrzZ response regulator . Genetic and biochemical analyses have demonstrated that FrzZ is phosphorylated by FrzE and FrzZ serves as the output of the Frz system [31] , [32] . The effect of Frz on reversals depends on MglA as well as on MglB [26] , [28] and signaling by Frz induces the pole switch of MglA-GTP and MglB , thus , giving rise to an inversion of the leading/lagging polarity [26] , [28] . We previously showed that the RomR response regulator , which consists of an N-terminal receiver domain and a C-terminal output domain , is essential for A-motility in M . xanthus [25] . Full-length RomR localizes in a bipolar , asymmetric pattern with a large cluster at the lagging pole and a small cluster at the leading cell pole . During reversals the polarity of the RomR clusters switches . The activity of response regulators is regulated by phosphorylation of a conserved Asp residue in the receiver domain [33] . A RomR variant in which this Asp residue in the receiver domain is substituted to Glu ( RomRD53E ) , which is expected to partially mimic the phosphorylated state [34] , causes a hyper-reversing phenotype while a substitution to the non-phosphorylatable Asn ( RomRD53N ) causes a hypo-reversing phenotype [25] . Because a cellular reversal involves the synchronous switch in polarity of both A- and S-motility proteins [25] , these observations raised the question of the function of RomR in S-motility and in regulating the reversal frequency . Here we re-examined the function of RomR in M . xanthus motility . We provide evidence that RomR is important for A- as well as for S-motility . Moreover , we show that RomR interacts directly with MglA and MglB . We show that RomR is a polar targeting determinant of MglA-GTP and that RomR together with MglB sets up the asymmetric polar localization of the MglA-GTP defining the leading cell pole . Similarly , we find that RomR sets up the asymmetric localization of MglB and that MglB and RomR are targeted to the opposite cell pole of MglA-GTP in an MglA dependent manner , thereby , defining the lagging cell pole . Thus , correct localization of MglA and MglB to opposite poles depends on RomR . For reversals , we show that RomR functions between the Frz chemosensory module and the MglA/MglB GTPase/GAP module . These observations in combination with phylogenomic analyses suggest that the MglA/MglB module together with RomR constitute the basic module for establishing cell polarity in gliding motility systems , and that the Frz system was incorporated at a later point to allow the dynamic inversion of the polarity axis during reversals . The paper by Zhang et al . [35] describes results similar to those reported here .
We previously demonstrated that RomR is required for A-motility based on the motility phenotype of a romR insertion mutant [25] . To determine the function of RomR in S-motility an in-frame deletion of romR ( ΔromR ) was generated in the fully motile strain DK1622 , which serves as the wild type ( WT ) in this work . To assess A- and S-motility in the ΔromR mutant , motility was tested on soft ( 0 . 5% ) agar , which is favorable to S-motility , and hard ( 1 . 5% ) agar , which is favorable to A-motility [36] . S-motility is manifested by colony expansion with the formation of flares of cells at the edge of a colony and A-motility is manifested by colony expansion with the presence of single cells at the edge of a colony . As shown in Figure 1A , the WT DK1622 formed the flares characteristic of S-motility on 0 . 5% agar , the ΔromR mutant was significantly reduced in flare formation and colony expansion , and the A+S− control strain DK1300 did not form these flares . On 1 . 5% agar , the WT displayed the single cell movements characteristic of A-motility at the edge of the colony whereas neither the ΔromR mutant nor the A−S+ control strain DK1217 did . Time-lapse microscopy of ΔromR cells at the colony edge on 1 . 5% agar and on 0 . 5% agar confirmed that the ΔromR cells did not display single cell movements on 1 . 5% agar and only displayed very limited movements on 0 . 5% agar ( data not shown ) . To confirm that the motility defect in the ΔromR mutant was caused by lack of RomR , we created a complementation construct in which a functional fusion between full-length RomR and GFP ( RomR1–420-GFP ) was produced from the constitutively active PpilA promoter at native levels ( Figure S1 ) [25] . All motility defects were corrected by expression of RomR1–420-GFP ( Figure 1B ) [25] . From these analyses we conclude that RomR is important for S-motility in addition to A-motility . Previous characterization of RomR described distinct regions: a response regulator receiver ( REC ) domain , and an output domain composed of a proline rich ( Pro-rich ) region and a glutamate ( Glu-rich ) region [25] . To more universally characterize RomR , we identified its homologs from a set of 1611 prokaryotic genomes . Similarity searches against this genome set using full-length RomR support that it is composed of two conserved regions ( Materials and Methods ) . As expected , one conserved region corresponds to the REC domain . The output domain of RomR comprises two distinct regions: ( i ) a conserved α-helical C-terminal region ( RomR-C ) ( Figure 1C and 1D ) that corresponds to the previously described Glu-rich region and is not homologous to characterized domains; and , ( ii ) an unstructured region corresponding to the previously described Pro-rich region that links the two conserved regions ( Figure 1C ) . Sequence analysis of all identified homologs showed that most maintain conservation of the RomR-C domain ( Figure 1D; Figure 2 ) while the unstructured linker region was not conserved ( Figure 1E ) . The linker regions show length and composition conservation within taxonomic groups suggesting that they may be associated with lineage-specific functions . Previous studies [25] have shown that the REC domain alone cannot localize RomR to the poles but is important for reversals . In contrast , the output domain comprising the linker and RomR-C localize polarly and is important for stimulating motility . Informed by the RomR sequence conservation analyses , we carried out a detailed functional analysis of the individual parts of the RomR output domain fused to GFP . As mentioned , full-length RomR fused to GFP ( RomR1–420-GFP ) corrected the motility defects of the ΔromR mutant and displayed an asymmetric bipolar localization pattern ( Figure 1B ) consistent with previous observations [25] . The entire RomR output domain fused to GFP ( RomR116–420-GFP ) , RomR-C alone ( RomR369–420-GFP ) and the linker alone ( RomR116–368-GFP ) also localized in an asymmetric bipolar pattern ( Figure 1B ) . However , only the RomR116–420-GFP construct partially restored A- and S-motility in the ΔromR mutant ( Figure 1B ) . Because the RomR-C construct RomR369–420-GFP accumulated at a lower level than native RomR ( Figure S1 ) , we examined a RomR-C construct that included a portion of the linker region ( RomR332–420-GFP ) . RomR332–420-GFP accumulated at a level similar to native RomR ( Figure S1 ) and showed asymmetric bipolar localization ( Figure 1B ) . However , this construct was also unable to complement the motility defects of the ΔromR mutant ( Figure 1B ) . From these analyses we conclude that RomR possesses two pole-targeting determinants , the linker region and RomR-C , which are individually sufficient for polar targeting . Moreover , both regions are required for motility . In order to understand the potential interplay between RomR and other systems involved in motility , we compared its phyletic distribution to the distribution of mglA and mglB , in addition to genes that mark the presence of the Frz system ( frzE ) , T4P ( pilT ) and gliding motility ( gltF ) in our genome set . The proteins of interest were identified using BLASTP searches , gene neighborhood analysis , and characteristic features ( Materials and Methods ) . Informed by the analyses on which regions of RomR are conserved and functionally important , we used the REC and RomR-C portions of RomR to identify homologs . RomR was identified in 31 genomes whereas MglA ( 70 genomes ) and MglB ( 60 genomes ) are more widespread ( Figure 2 ) . Of the 60 genomes encoding both MglA and MglB , 26 also encode a RomR homolog ( Figure 2 ) . Thus , with the exception of five genomes , all genomes encoding a RomR homolog also encode MglA and MglB homologs . These five genomes support a close correlation between MglA , MglB and RomR: RomR in these five genomes have lost either REC or RomR-C , and none contain a complete , if any , MglA/MglB system ( Figure 2 ) . 10 of the 26 genomes encoding intact RomR proteins also encode a Frz system and all Frz encoding genomes encode homologs of MglA , MglB and RomR . The co-occurrence of Frz with RomR and RomR with MglA and MglB support a functional association between these proteins . Genes for T4P and gliding motility were found in 476 and 12 genomes , respectively ( Figure 2 ) . Generally , MglA , MglB , RomR and Frz encoding genes co-occurred with genes for gliding motility suggesting a functional connection between these proteins . Similarly , all 26 genomes encoding intact genes for MglA , MglB and RomR also contained T4P encoding genes also supporting a functional connection between these genes . To map the position of romR in the regulatory circuits controlling motility and reversals , we carried out genetic epistasis experiments , using motility and reversal frequencies as readouts for function . Motility assays confirmed that a ΔmglA mutant is non-motile [26] , [28] , unlike the ΔmglB or mglAQ82A mutants , which contain MglA locked in the active GTP-bound form , both of which display A- and S-motility and hyper-reverse [27] ( Figure 3A and 3B ) . Next , we deleted romR in these three backgrounds to establish the relative order of the genes . The motility assays showed that mglA , mglAQ82A , and mglB are epistatic to romR as evidenced by the similar phenotypes shared between the single mutants and corresponding double mutants ( Figure 3A ) . We analyzed the reversal frequencies of single cells in the ΔromR , mglAQ82A and ΔromR , ΔmglB double mutants and found that they displayed hyper-reversing phenotypes similar to mglAQ82A and ΔmglB single mutants ( Figure 3B ) , respectively , which further supports the epistasis relationships observed in the motility assays . These data also demonstrate that the mglAQ82A and mglB mutations cause a bypass of the motility defects caused by the ΔromR mutation . Previous work suggested that substitutions of D53 in RomR mimics the active phosphorylated state ( RomRD53E ) or the inactive non-phosphorylated state ( RomRD53N ) [25] . We confirmed that RomRD53N and RomRD53E both stimulate motility and that RomRD53N causes a hypo-reversing and RomRD53E a hyper-reversing phenotype ( Figure 3B ) [25] . Strains containing romRD53N or romRD53E in mglAQ82A or ΔmglB mutant backgrounds showed the hyper-reversing phenotypes similar to those of mglAQ82A or ΔmglB single mutants , respectively and no additive phenotype was observed ( Figure 3B ) . Thus , the observed epistasis relationships are independent of the activation state of RomR . The epistasis experiments combining the various mglA , mglB , and romR alleles suggest that romR acts in the same genetic pathway as mglA and mglB to stimulate motility and reversals . Moreover , the data are consistent with romR acting upstream of both mglA and mglB as a positive regulator and inhibitor , respectively . Because MglB is an inhibitor of MglA , an MglB inhibitor is formally similar to an MglA activator . Therefore , these experiments are consistent with three general models for how the effect of RomR on motility and reversals could be accomplished by ( i ) stimulating MglA; ( ii ) inhibiting MglB; or , ( iii ) a combination of the two . Because frz acts upstream of mglA and mglB for reversals [26] , [28] , we tested whether romR lies between frz and mglA and mglB . The FrzZ protein is the direct output of the Frz system [31] , [32] . To test the relationship between frz and romR , we combined a ΔfrzZ mutation , which causes a hypo-reversing phenotype [32] , with different romR alleles . Combining ΔromR with ΔfrzZ did not restore the motility defects caused by the ΔromR mutation ( Figure 3A ) . A strain containing romRD53N , which is active for motility but not for reversals , and ΔfrzZ was motile and hypo-reversed similarly to the strains only containing ΔfrzZ or romRD53N ( Figure 3B ) . A strain containing romRD53E , which is active for motility and causes hyper-reversals , and ΔfrzZ was motile and hyper-reversed with a frequency similar to that caused by romRD53E alone . In agreement with previous observations [26] , [28] , combining ΔfrzZ with mglAQ82A resulted in a strain that hyper-reversed with the same frequency as a strain only containing mglAQ82A . Thus , MglA is the most downstream part in the reversal circuit . These epistasis experiments suggest that romR and frzZ act in the same genetic pathway to stimulate reversals . Moreover , the data are consistent with frzZ acting upstream of romR and with frzZ acting as a positive regulator of romR for reversals . The performed epistasis analyses support that MglA , MglB , RomR and FrzZ are part of a signaling network that regulates motility and reversals in M . xanthus . Previous studies of MglA , MglB , and RomR have demonstrated that all three proteins localize polarly . To understand how MglA , MglB and RomR interact to stimulate motility and reversals , we systematically determined the localization of MglA , MglB and RomR in the presence and absence of each other . We have been unable to construct a functional FrzZ fusion protein; therefore , FrzZ was excluded from these analyzes . First , MglA , MglB and RomR were localized using active fluorescent fusion proteins expressed at native levels in strains deleted for the relevant native copies [25] , [26] ( Figure S2 ) . As previously observed , MglA predominantly localizes in a unipolar pattern , whereas MglB and RomR predominantly localize in a bipolar asymmetric pattern [26]–[28] ( Figure 4A ) . Next , we analyzed the localization of each protein in the absence of one other . We confirmed that MglA localization changes from unipolar to a predominantly bipolar symmetric pattern in the absence of MglB [26]–[28] ( Figure 4A ) . In contrast , we found that MglA localized diffusely throughout the cytoplasm in the absence of RomR . When examining MglB localization , we found that MglB shifts from a predominantly bipolar asymmetric pattern to a bipolar symmetric pattern in the absence of RomR and a unipolar pattern in the absence of MglA ( Figure 4A ) . RomR localization patterns showed a similar shift from predominantly bipolar asymmetric to unipolar in the absence of MglA , whereas it became more bipolar symmetric in the absence of MglB ( Figure 4A ) . Therefore , all three proteins are mutually dependent for correct localization in all three pair-wise directions . Lack of RomR causes diffuse localization of MglA . Because MglA-GDP localizes in a diffuse pattern [26] and MglA-GTP localizes polarly , we thought of four possibilities for how RomR could stimulate polar localization of MglA-GTP: ( i ) RomR acts as a GEF; ( ii ) RomR inhibits MglB GAP activity; ( iii ) RomR is an MglA polar targeting determinant; or , ( iv ) combinations of these activities . To explore these possibilities , we determined the localization of YFP-MglAQ82A , which is locked in the GTP-bound form and localizes in a bipolar pattern and with a central oscillating cluster in a ΔmglA mutant [27] ( Figure 4B ) . In the absence of MglB , YFP-MglAQ82A localizes as in the ΔmglA mglB+ mutant [27] . In contrast , in the absence of RomR , YFP-MglAQ82A only localized to the central oscillating cluster ( Figure 4B ) . Similarly , in the absence of RomR and MglB , YFP-MglAQ82A only localized to the central oscillating cluster ( Figure 4B ) . Finally , we observed that in the absence of both RomR and MglB , YFP-MglA was primarily diffuse or formed a non-polar cluster and rarely formed polar clusters ( Figure 4A ) . These localization patterns suggest that one function of RomR is as a direct polar targeting determinant of MglA; however , the data does not rule out the possibility that RomR may also regulate the nucleotide-bound state of MglA . MglB-mCherry and RomR-GFP show a similar localization pattern in WT and in the ΔmglA mutant ( Figure 4A ) . To determine whether MglB-mCherry and RomR-GFP colocalize , we constructed a strain expressing both fusion proteins . Consistent with the observations that RomR as well as MglB in moving cells localize with the large cluster at the lagging cell pole [26] , [28] , the two proteins colocalized in mglA+ cells with a bipolar , asymmetric localization ( Figure 4C ) . MglB-mCherry and RomR-GFP also colocalized in the absence of MglA ( Figure 4C ) . We previously showed that the unipolar RomR cluster in the ΔmglA mutant is at the pole containing T4P [25] and , thus , RomR and MglB both localize at the “wrong” pole in the absence of MglA . This observation in combination with the observation that in the absence of RomR , MglB becomes more symmetric and vice versa ( Figure 4A ) suggest that MglB and RomR depend on each other for bipolar , asymmetric localization and that MglA is important for establishing this pattern . To test whether RomR interacts directly with MglA and/or MglB , we performed pull-down experiments . To this end we purified N-terminal His6-tagged MglB ( His6-MglB ) and C-terminal His6-tagged MglA ( MglA-His6 ) . When bound to a Ni2+-NTA-agarose matrix His6-MglB interacted with RomR in total cell extracts of WT M . xanthus as determined using α-RomR antibodies ( Figure 5A ) . Similarly , when MglA-His6 was bound to the Ni2+-NTA-agarose matrix , it interacted with RomR in total cell extracts of WT M . xanthus ( Figure 5A ) . To discriminate between direct and indirect interactions between the three proteins , we purified N-terminally His6-tagged RomR ( His6-RomR ) and MalE-tagged RomR ( MalE-RomR ) and N-terminally GST-tagged MglA ( GST-MglA ) . As shown in Figure 5B , GST-MglA bound to a glutathione-agarose column interacted with His6-RomR . In control experiments with purified GST , His6-RomR was not pulled-down . In a separate control experiment , a His6-PilP protein was not pulled-down by GST-MglA ( data not shown ) . Thus , the interaction between GST-MglA and His6-RomR is specific and direct . In a separate set of experiments , MalE-RomR bound to an amylose matrix interacted with His6-MglB ( Figure 5C ) but not with a His6-PilP control protein ( data not shown ) . Moreover , purified MalE protein did not interact with His6-MglB . Thus , MalE-RomR interacts specifically and directly with MglB .
Here we report that M . xanthus motility is stimulated and regulated by two modules of signaling proteins: a polarity module consisting of the response regulator RomR , the small GTPase MglA , and the MglA GAP MglB , and a polarity inversion module consisting of the Frz chemosensory system with its output response regulator FrzZ . While the RomR/MglA/MglB polarity module is important for motility , the Frz polarity inversion module interfaces with the RomR/MglA/MglB module at the level of RomR to regulate motility by regulating the reversal frequency . Here we focused on understanding the network topology of the polarity module and how it interfaces with the polarity inversion module to ultimately regulate motility . MglA-GTP functions to stimulate motility and reversals in the absence of MglB whereas the opposite is not the case . Therefore , MglA-GTP is the output of the MglA/MglB GTPase/GAP module ( Figure 6 ) . RomR , MglA-GTP and MglB are all polarly localized whereas MglA-GDP is not . We found that correct localization of the three proteins is mutually dependent in all three pair-wise interactions . Moreover , pull-down experiments using purified proteins and WT M . xanthus cell extracts or direct interactions studies with purified proteins together with previous results [26]–[28] show that the three proteins interact in all three pair-wise directions . Based on the findings from the interaction and localization analyses , we suggest that RomR targets MglA-GTP to both poles and that MglB at the lagging cell pole is important for establishing the MglA-GTP/RomR asymmetry by means of its GAP activity . Thus , RomR is part of a MglA-GTP/RomR complex at the leading cell pole . Interestingly , MglA is neither polarly localized in the ΔromR mutant nor in the ΔromR , ΔmglB double mutant; however , the ΔromR mutant is strongly reduced in motility whereas the ΔromR , ΔmglB mutant is motile . We suggest that the crucial difference between the two strains is the presence and absence of the MglB GAP activity . In the ΔromR mutant , MglB is bipolar symmetrical and , consequently , the GAP activity is not confined spatially to a single pole and , therefore , MglA-GTP would be low . On the other hand , the ΔromR , ΔmglB mutant would not have GAP activity and , therefore , a sufficient level of MglA-GTP may accumulate to stimulate motility . In the ΔromR ΔmglB mutant , MglA is not polarly localized; nevertheless , this mutant is motile . Therefore , polar localization of MglA is not a strict requirement for motility . The localization and interaction data suggest that MglB and RomR form a complex that is essential for establishing the bipolar asymmetric localization of the two proteins and that this asymmetry is established in an MglA-GTP/RomR-dependent manner . In total , these interactions generate a mutually-dependent circuit for asymmetric localization of the three proteins: ( i ) RomR targets MglA-GTP to the poles in the MglA-GTP/RomR complex , ( ii ) the MglB/RomR complex is essential for establishing the MglA-GTP/RomR asymmetry by means of the MglB GAP activity , and ( iii ) MglA-GTP/RomR is essential for establishing the MglB/RomR asymmetry . Combining the localization and interaction data with the results of the epistasis experiments using motility and reversals as readouts , we suggest that between reversals RomR functions as a positive regulator of MglA by targeting MglA-GTP to the poles in the MglA-GTP/RomR complex and that RomR inhibits MglB ( and in that way also activates MglA ) by formation of the MglB/RomR complex that is targeted to the lagging cell pole in an MglA-GTP/RomR-dependent manner ( Figure 6 ) . The identification of the MglA/MglB/RomR polarity module for stimulation of motility provides a conceptual framework for detailed biochemical experiments to address whether RomR acts as a GEF on MglA and/or regulates MglB GAP activity . The output of the Frz polarity inversion module is the FrzZ response regulator and the reversal-inducing activity of the Frz system depends on phosphorylation of FrzZ [31] , [32] . Similarly , our data suggest that reversals are induced by RomR phosphorylation . Interestingly , the reversal frequency of the romRD53E mutant is two-fold lower than in the ΔmglB and mglAQ82A mutants possibly reflecting that RomRD53E is not a perfect mimic of phosphorylated RomR . Alternatively , the FrzZ signal is channeled to MglA and MglB in a pathway that is independent of RomR . Given that the romRD53N mutant has the same low reversal frequency as the ΔfrzZ mutant , we favor the former model . By combining our genetic data with previously published data [31] , [32] , we suggest that phosphorylated FrzZ acts as a positive regulator of RomR and that this effect likely depends on phosphorylation of RomR . In this model , RomR acts at the interface between the Frz polarity inversion module and the MglA/MglB/RomR polarity module ( Figure 6 ) . This potential phosphorylation of RomR by an unknown mechanism induces a switch in the polarity of the MglA , MglB and RomR proteins . RomRD53N and RomRD53E both localize in a bipolar asymmetric pattern [25] suggesting that the effect of RomR phosphorylation is not directly on its polar localization or release . Clearly , detailed biochemical experiments will be needed to elucidate the interaction between FrzZ/RomR , MglA/RomR and MglB/RomR and how these interactions depend on the phosphorylation status of RomR . Our preliminary results suggest that the FrzE kinase does not phosphorylate RomR in vitro ( Keilberg , D . unpubl ) . The widespread distribution of MglA , MglB and RomR in organisms lacking the Frz system suggests that the RomR phosphorylation state could be regulated by other mechanisms . Phosphorylated FrzZ could activate a yet to be identified histidine protein kinase , which would subsequently be involved in RomR phosphorylation , as has been described for the single receiver domain response regulator DivK in the activation of the histidine protein kinases DivJ and PleC in Caulobacter crescentus [37] . Alternatively , FrzZ and RomR may be part of a phosphorelay in which the phosphoryl-group would be transferred from FrzZ to RomR via a histidine-phosphotransfer protein as has been described for other phosphorelays [38] . Future experiments will be directed at distinguishing between these possibilities . In bacteria many proteins localize to the cell poles [6] . Sophisticated mechanisms are employed to bacteria to facilitate polar binding of proteins: This polar localization can be mediated by trans-acting polar targeting factors as in the case of PopZ , which interacts directly with ParB and targets it to the cell poles in C . crescentus [39] , [40] . Alternatively , proteins may localize to the cell poles based on recognition of membrane curvature as proposed for some peripheral membrane proteins in Bacillus subtilis [41] , [42] . Understanding how MglA , MglB , and RomR recognize the cell poles will add to our understanding of the diversity of protein localization mechanisms and potential common traits they share . The modular design of the regulatory circuits involved in motility and its control in M . xanthus are paralleled by the phylogenetic distribution of MglA , MglB , RomR and of the Frz system . With the exception of the M . xanthus proteins , the functions of these proteins are not known . Based on the analyses of the M . xanthus proteins , we suggest that MglA and MglB together with RomR may constitute a module for the spatial deployment of proteins , i . e . regulation of cell polarity ( and giving rise to unidirectional cell movements without reversals in M . xanthus ) . Subsequently , the Frz chemosensory module was incorporated by some of these systems to establish a scheme for the dynamic temporal control of cell polarity ( and giving rise to the irregular reversals observed in extant M . xanthus ) . As outlined in [43]–[46] the high degree of modularity of signaling systems makes these systems more evolvable in part because combining and integrating different modules allow for the comparatively simple evolution of signaling units with novel properties compared to building such units from scratch . The evolutionary scenario outlined here is in agreement with these concepts .
Plasmids were propagated in E . coli TOP10 ( F− , mcrA , Δ ( mrr-hsdRMS-mcrBC ) , φ80lacZΔM15 , ΔlacX74 , deoR , recA1 , araD139 , Δ ( ara-leu ) 7679 , galU , galK , rpsL , endA1 , nupG ) unless otherwise stated . E . coli cells were grown in LB or on plates containing LB supplemented with 1 . 5% agar at 37°C with added antibiotics if appropriate [46] . DK1622 was used as WT M . xanthus strain throughout and all M . xanthus strains used are derivatives of DK1622 . M . xanthus strains used are listed in Table 1 . Plasmids are listed in Table S1 . Plasmid constructions are described in Text S1 . Primers used are listed in Table S2 . All DNA fragments generated by PCR were verified by sequencing . All M . xanthus strains constructed were confirmed by PCR . Plasmids were integrated by site specific recombination at the Mx8 attB site or by homologous recombination at the native site . The in-frame deletions of frzZ ( ΔfrzZ ) and romR ( ΔromR ) were generated as described [47] using pFD1 and pSL37 , respectively . M . xanthus strains were grown at 32°C in 1% CTT broth [48] or on CTT agar plates supplemented with 1 . 5% agar . Kanamycin ( 50 µg/ml ) or oxytetracycline ( 10 µg/ml ) was added when appropriate . Cells were grown to a cell density of 7×108 cells/ml , harvested and resuspended in 1% CTT to a calculated density of 7 ×109 cells/ml . 5 µl aliquots of cells were placed on 0 . 5% and 1 . 5% agar supplemented with 0 . 5% CTT and incubated at 32°C . After 24 h , colony edges were observed using a Leica MZ8 stereomicroscope or a Leica IMB/E inverted microscope and visualized using Leica DFC280 and DFC350FX CCD cameras , respectively . To quantify differences in motility , the increase in colony diameter after 24 h was determined . Briefly , the diameter of each colony was measured at two positions at 0 and 24 h . The increase in colony diameter was calculated by subtraction of the size at 0 h from the size at 24 h . Colony diameters were measured for three colonies per strain . For microscopy , M . xanthus cells were placed on a thin 1% agar-pad buffered with A50 buffer ( 10 mM MOPS pH 7 . 2 , 10 mM CaCl2 , 10 mM MgCl2 , 50 mM NaCl ) on a glass slide and immediately covered with a coverslip , and then imaged . Quantification of fluorescence signals was done as follows . The integrated fluorescence intensity of polar clusters and of a similar cytoplasmic region was measured using the region measurement tool in Metamorph 7 . 7 . The intensity of the cytoplasmic region was subtracted from the intensity of the polar cluster . These corrected intensities of the polar clusters were used to calculate the ratios between the polar signals in individual cells . If the ratio is ≤2 . 0 , the localization is defined as bipolar symmetric , if the ratio is ≥2 . 1 and ≤10 . 0 the localization is defined as bipolar asymmetric , and if the ratio was ≥10 . 1 the localization is defined as unipolar . For each strain 200 cells were analyzed . For time-lapse microscopy , cells were recorded at 30-s intervals for 15 min . Images were recorded and processed with Leica FW4000 V1 . 2 . 1 or Image Pro 6 . 2 ( MediaCybernetics ) software . Processed images were visualized using Metamorph ( Molecular Devices ) . Reversals were counted for >50 cells of each strain followed for 15 minutes and displayed in a Box plot . Proteins were purified as described in Text S1 . 0 . 5 mg of purified His6-MglB or MglA-His6 in buffer H ( 50 mM NaH2PO4 pH 8 . 0 , 300 mM NaCl , 10 mM imidazole ) was applied to a Ni2+-NTA-agarose column ( Macherey-Nagel ) . M . xanthus cell lysate was prepared as follows: 200 ml of exponentially growing WT cells at a cell density of 7×108 cells/ml were harvested , resuspended in buffer H in the presence of proteases inhibitors ( Roche ) and lysed by sonication . Cell debris was removed by centrifugation at 4700×g for 20 min , 4°C and the cell-free supernatant applied to the Ni2+-NTA-agarose column with or without bound His6-MglB or MglA-His6 . After two washing steps with each 10 column volumes of the buffer H , bound proteins were eluted with buffer H supplemented with 250 mM imidazole . Proteins eluted from the columns were analyzed by two methods: SDS-PAGE and gels stained with Coomassie Brilliant Blue R-250 and SDS-PAGE with immunoblot analysis using α-RomR antibodies [25] . To test for direct protein-protein interactions , 0 . 2 mg of purified prey protein ( His6-RomR or His6-MglB or as a negative control His6-PilP ) was mixed with 0 . 2 mg of purified bait protein ( GST-MglA or MalE-RomR ) and as a control with 0 . 2 mg of GST or MalE , respectively . Proteins were incubated with 0 . 5 ml sepharose beads ( for MalE-tagged proteins: amylose beads; for GST-tagged proteins: glutathione beads ) in buffer D ( 50 mM NaH2PO4 pH 8 . 0 , 300 mM NaCl ) for 5 h , 4°C . After washing the beads with 25 column volumes of buffer D , the elutions were performed with buffer D supplemented with 10 mM glutathione for GST-tagged proteins , and with 10 mM maltose for MalE-tagged proteins . Proteins eluted from the columns were analyzed by immunoblot analysis using α-GST antibodies ( Biolabs ) , α-MalE antibodies ( Biolabs ) , α-RomR antibodies [25] and α-MglB antibodies [26] . Immunoblots were carried out as described [46] . The following software packages were used with the described settings unless otherwise specified . The HMMER3 software package [49] was used in conjunction with the Pfam26 domain library [50] for domain architecture analysis with default gathering thresholds . In the event of domain overlaps , the highest scoring domain model was chosen for the final architecture . The JackHMMER method [51] was used for iterative similarity searches with a 0 . 0001 e-value inclusion threshold . For non-iterative similarity searches , we used BLASTP from the BLAST+ software package version 2 . 2 . 26 [52] and considered hits with e-values of 0 . 0001 or lower to be significant unless otherwise specified . Multiple sequence alignments were built using the l-ins-i algorithm of the MAFFT version 6 . 864b software package [53] . Phylogenetic trees were constructed using FastTree version 2 . 1 . 4 [54] with default settings or PhyML version 3 . 0 [55] with empirical frequencies and SPR topology searches . Secondary structure was predicted using the Jpred3 webserver [56] . All complete prokaryotic genomes 1609 were downloaded from the NCBI Refseq [57] database on April 4th , 2012 . Due to our specific interest in Myxococcales , we also included the complete genomes of Stigmatella aurantiaca [58] and Corallococcus coralloides [59] from GenBank [60] as they were not yet available in Refseq at the time of genome collection . The MglA and MglB sequences from M . xanthus ( MXAN_1925 and MXAN_1926 , respectively ) were used in BLASTP queries against the genome set . All significant sequence hits were aligned using MAFFT and the core regions were extracted and used to build phylogenetic trees with FastTree . The tree representing 134 putative MglA homologs showed a distinct subfamily of 113 sequences that is associated with the characterisic intrinsic arginine finger [27] in comparison to a subfamily of 21 other putative small GTPases that lack it ( Figure S3 ) . We chose the subfamily of 113 sequences as our MglA set . In contrast , only 63 putative MglB homologs were collected by BLAST analysis , most of which are encoded near members of the MglA set . We used the core regions of the MglB homologs as BLASTP queries to identify more putative MglB partners of our MglA set . The collected sequences were aligned using MAFFT and the core regions were extracted and used to build a phylogenetic tree with FastTree ( Figure S4 ) . The tree of 86 putative MglB homologs revealed a subfamily of 71 sequences that were associated with our MglA sequence set based on genome context , and the members of this subfamily were chosen as our final MglB set . MglA and MglB sequences are listed in Table S3 . Initial BLASTP queries with the RomR sequence from M . xanthus ( MXAN_4461 ) revealed it to be a multi-domain protein with two regions of conservation , an N-terminal receiver domain and a C-terminal domain that is not homologous to previously characterized domains . Given the ubiquity of receiver domains , we chose to use the C-terminal domain ( 369–420 of MXAN_4461 ) in a jackHMMER query against our genome set , which converged after three rounds . The results identified 28 significant hits , 27 of which have N-terminal receiver domains typical of response regulators . We extracted the receiver domain and C-terminal domains of the 27 response regulators sequences ( Table S3 ) and used them as BLASTP queries against our database to identify potential divergent homologs . The queries with the C-terminal regions did not identify any new homologs , whereas the queries with the receiver domains identified 3599 homologs using our default gathering thresholds . Given this large data set , we chose to only gather hits of 1e-20 or lower from the BLASTP queries as this resulted in a set of only 133 sequences , which was more comparable to our previously defined MglA and MglB data sets . The 133 sequences were aligned using the e-ins-i algorithm of MAFFT . We used FastTree to build a phylogenetic tree from the receiver domain regions of the sequences because the remaining portions of the sequences could not be aligned . The resulting tree revealed a subfamily of 31 sequences most of which contain the previously defined C-terminal domain ( Figure S5 ) . Those lacking the domain were encoded in genomes from species closely related to their most similar sequence ( e . g . two RomR sequences in members of Acidobacteria that lack the C-terminal domain group with a complete RomR sequence from another member of Acidobacteria ) , which supports their classification as RomR sequences . We chose these 31 sequences for our final RomR set . RomR sequences are listed in Table S3 . The Frz system was previously identified as a member of the ACF class of chemosensory systems [61] . We collected the core regions of all the CheA sequences from those analyses and built multiple sequence alignments for each class using MAFFT . Hidden markov models ( HMMs ) were built from each class specific alignment after being reduced such that no members of the alignment shared more than 80% identity . CheA sequences can be identified by the presence of HATPase_c and CheW domains from Pfam [62] , and all sequences with HATPase_c and CheW domains were collected from our genome set . The sequences were compared to our CheA HMM library and assigned to classes based on the highest scoring model . All CheA sequences assigned to the ACF class were collected ( 164 sequences ) and aligned using the e-ins-i algorithm of MAFFT . The core regions corresponding to the P3–P5 domains and the C-terminal receiver domain characteristic of this family were used to build a phylogenetic tree in PhyML . Sequences lacking any of these four domains or the N-terminal histidine phosphotransfer domain were predicted to be non-functional and removed from the analysis . We identified a FrzE specific subfamily in the tree based on Frz system features , genome context , and paralogy events ( Figure S6 ) . All FrzE sequences have a FrzZ encoded in nearby genes based on BLASTP queries using neighboring response regulator protein sequences . FrzE sequences are listed in Table S3 . Recent computational analysis of FAC proteins identified two distinct groups of genes: Group A genes that are only present in organisms that have gliding motility ( members of Myxococcales and Bdellovibrionales ) , and Group B genes that have homologs in the Group A lineages in addition to Fibrobacter succinogenes and members of β/γ-proteobacteria for which gliding motility has not been observed [24] . We chose the Group A gene gltF as a marker for the presence of gliding motility because it is the most unique based on initial BLAST searches ( many Group A genes are putative outer membranes proteins or proteins that contain TPR repeats , both of which result in non-specific BLAST hits ) . We used the MXAN_4868 GltF sequence as a query in a JackHMMER search , which identified 29 homologs that were present in all Myxococcales and Bdellovibrionales genomes consistent with previous observations [24] . All identified GltF sequences are listed in Table S3 . We used the retraction ATPase PilT as a marker for the presence of T4P . The PilT sequences from M . xanthus [63] , Neisseria meningitidis [64] , Pseudomonas aeruginosa [65] , and Synechocystis sp . PCC6803 [66] share the same Pfam domain architecture , a single T2SE domain . We collected 3756 sequences from our genome set that matched this domain architecture , aligned them in MAFFT using default settings , and a phylogenetic tree was built from the alignment using FastTree ( Figure S7A ) . This sequence set is expected to also include sequences for PilB and ATPases in type II secretion systems . To identify the branches corresponding to PilT , the PilT sequences from the four aforementioned organisms were used to identify a smaller set of 1219 PilT candidates . The 1219 sequences were realigned in MAFFT using default regions and the core region of the alignment corresponding to residues 5–327 of the M . xanthus PilT ( MXAN_5787 ) was extracted and used to build a phylogenetic tree in FastTree ( Figure S7B ) . Identification of characterized PilT proteins in this tree was used to identify a set of 547 PilT sequences ( Table S3 ) .
|
Most cells are spatially organized with proteins localizing to specific regions . The ability of cells to polarize facilitates many processes including motility . Myxococcus xanthus cells move in the direction of their long axis and occasionally change direction of movement by undergoing reversals . Similarly to eukaryotic cells , the leading pole of M . xanthus cells is defined by a Ras-like GTPase and the lagging pole by its partner GAP MglB . We show that MglA and MglB localization depends on the RomR protein . RomR recruits MglA to a pole and MglB GAP activity at the lagging pole results in MglA/RomR localizing asymmetrically to the leading pole . Conversely , RomR together with MglB forms a complex that localizes to the lagging pole , and this asymmetry is set up by MglA/RomR at the leading pole . Thus , MglA/RomR and MglB/RomR localize to opposite poles because they exclude each other from the same pole . RomR also interfaces with the Frz chemosensory system that induces reversals . Thus , RomR links the MglA/MglB/RomR polarity module to the Frz signaling module that triggers the inversion of polarity . Phylogenomics suggests an evolutionary scheme in which the MglA/MglB module incorporated RomR early to impart cell polarity while the Frz module was appropriated later on to direct polarity reversals .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"bacteriology",
"evolutionary",
"biology",
"microbiology",
"gtpase",
"signaling",
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"signaling",
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2012
|
A Response Regulator Interfaces between the Frz Chemosensory System and the MglA/MglB GTPase/GAP Module to Regulate Polarity in Myxococcus xanthus
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Tropical pathogens often cause febrile illnesses in humans and are responsible for considerable morbidity and mortality . The similarities in clinical symptoms provoked by these pathogens make diagnosis difficult . Thus , early , rapid and accurate diagnosis will be crucial in patient management and in the control of these diseases . In this study , a microfluidic lab-on-chip integrating multiplex molecular amplification and DNA microarray hybridization was developed for simultaneous detection and species differentiation of 26 globally important tropical pathogens . The analytical performance of the lab-on-chip for each pathogen ranged from 102 to 103 DNA or RNA copies . Assay performance was further verified with human whole blood spiked with Plasmodium falciparum and Chikungunya virus that yielded a range of detection from 200 to 4×105 parasites , and from 250 to 4×107 PFU respectively . This lab-on-chip was subsequently assessed and evaluated using 170 retrospective patient specimens in Singapore and Thailand . The lab-on-chip had a detection sensitivity of 83 . 1% and a specificity of 100% for P . falciparum; a sensitivity of 91 . 3% and a specificity of 99 . 3% for P . vivax; a positive 90 . 0% agreement and a specificity of 100% for Chikungunya virus; and a positive 85 . 0% agreement and a specificity of 100% for Dengue virus serotype 3 with reference methods conducted on the samples . Results suggested the practicality of an amplification microarray-based approach in a field setting for high-throughput detection and identification of tropical pathogens .
Many infectious diseases are more prevalent in the tropical and subtropical regions where ecological , geographical and socioeconomic factors facilitate their propagation . The high diversity of such tropical pathogens include bacteria , fungi , helminths , parasites , and viruses that mirrors the rich biodiversity in the tropics and sub-tropical regions [1]–[3] . Many of these pathogens are transmissible through an insect vector or an invertebrate host [4]–[7] , and transmission is affected by climate that can significantly influence vector behavior and physiology [8] , including the extrinsic incubation period of vector-borne pathogens [9] , [10] . Furthermore , global changes such as anthropogenic climate change and climate variability , habitat encroachment by the growing human population , volume of international travel , migration , trade and pollution create new opportunities for microbial spread [11]–[13] . The world is subjected to a plethora of tropical pathogens . Table 1 provides an overview of 14 tropical diseases , stratified into protozoan , bacterial , and viral infections that are globally important . However , some of these tropical diseases are often intimately connected to paucity of local and global burden estimates , poverty , geographical isolation and lack of coordinated approaches for disease controls [14] . Firstly , there are protozoan infections: malaria , which remains one of the most devastating and difficult parasitic diseases to be controlled and further threatened by the emergence and spread of resistance to anti-malarial drugs [15]–[17]; Chagas disease which is one of the most neglected tropical disease with a lifelong infection [18]–[20]; and human African trypanosomiasis with 60 million people at risk in Africa [21]–[23] . Next are bacterial infections: leptospirosis , which has been identified as one of the most widespread zoonosis in the world , exemplified by outbreaks in rural and urban environments [24]–[27] , and more recently , emerged as a disease of the adventure traveler [28]; meliodosis that has been reported with a global distribution [29] , [30]; and salmonellosis , which causes enteric fever and has a high global incidence [31] . Finally , the most prevalent infections are those of viral origins: Chikungunya fever in the Indian Ocean islands , the Indian subcontinent , southeast Asia , Africa , Europe and its emergence in the Americas [32]–[37]; Dengue fever including the emergence of dengue hemorrhagic fever [38]–[43]; West Nile fever in America [44] , [45] and the increasing extensive distribution through Africa , Middle East , Europe and Asia [46]; Japanese encephalitis in Australasia [47] and in Asia [48]; yellow fever in West and Central Africa [49]; high incidence rates of hand , food and mouth disease in Asia [50]–[53]; Rift valley fever which has spread to Yemen , Saudi Arabia , northern Egypt and the French island of Mayotte [54]; and Hantavirus hemorrhagic fever which can cause serious diseases in humans with mortality rates of 12% ( hemorrhagic fever with renal syndrome ) and 60% ( Hantavirus pulmonary syndrome ) in some outbreaks [4] , [55] . Despite being medically important , the incidence rates of some of these diseases are grossly underestimated and this reflects the clinical index of suspicion of the diseases which could have resulted from a lack of access to rapid diagnostics [18] , [25] , [29] . The global spread of tropical diseases emphasizes the importance of preparedness to address them . The first goal of this preparedness is fast and accurate diagnosis of medically important diseases . Differential diagnosis is based mainly on clinical examination , taking into account which diseases are locally prevalent , potential exposure , and the relevant travel history . However , the similarity and the non-specific nature of the symptoms provoked by many tropical pathogens ( Table 1 ) complicates correct diagnosis by classical clinical observations [25] , [56]–[61] . Yet , a correct diagnosis is necessary to institute effective control measures , from timely therapeutic intervention [62] , [63] , to effective treatment [64] and effective clinical management in deploying appropriate community-wide control measures to improve the patients' clinical outcome , disease mapping , impact monitoring , and post-elimination surveillance . Correct diagnosis can only be determined through reliable laboratory-confirmed detection and identification of tropical pathogens in clinical specimens . Polymerase chain reaction ( PCR ) has been used in the diagnosis of several infectious diseases [51] , [65]–[70] as it is a highly specific and sensitive method for molecular detection [71]–[73] . Moreover , much progress has been made with molecular multiplexing [74]–[78] . With the advent of microarray technology which permits simultaneous detection of a given sequence in a sample by hybridization to thousands of defined probes [79] , amplification and microarray integrated assays have been made possible [74] , [80]–[82] . In this study , microfluidic technology was combined with reverse transcription ( RT ) , PCR amplification , and microarray hybridization to develop a silicon based micro electro mechanical systems ( MEMS ) integrated lab-on-chip that can simultaneously detect and differentiate between 26 pathogen species ( including bacteria , parasites and viruses ) that cause 14 tropical diseases . The detection platform is composed of the disposable lab-on-chip , a temperature control system ( TCS ) for the accurate control of thermal process and an optical reader for the fluorescence microarray image acquisition . The ability of the lab-on-chip to provide a “blood-to-diagnosis” solution in the detection of known and divergent pathogens was demonstrated on retrospective patient specimens . This system allows the simultaneous identification and discrimination of a large number of candidate tropical pathogens . It is undoubtedly a potential game-changer in the field of molecular diagnostics , as it provides an effective and rapid means to establish the presence of defined potential pathogens .
The use of human samples was approved by the National Healthcare Group's Domain-Specific Ethics Review Board ( DSRB reference no . B/08/026 ) , and written informed consent was obtained from all participants . Approval was also obtained for the use of archived samples from The Oxford Tropical Research Ethics Committee ( OxTREC ) as part of the surveillance routine . Plasma samples from 30 PCR-confirmed Chikungunya virus ( CHIKV ) patients who were admitted to the Communicable Disease Centre at Tan Tock Seng Hospital ( TTSH ) during the outbreak from August 1 to September 23 , 2008 [83] , [84] were included . Plasma samples were also collated from 10 healthy donors with informed consent ( DSRB reference no . B/08/026 ) and used as negative controls . RNA samples were extracted using the QIAamp viral RNA mini kit ( Qiagen , Hilden , Germany ) , according to manufacturer's instructions . One hundred and twenty five archived nuclei acid samples extracted from specimens at the Shoklo Malaria Research Unit ( SMRU ) clinic on the Thai-Burmese border between 1999 and 2011 as part of two surveillance studies [16] , [85] were included . DNA extracts from packed red blood cells obtained from patients ( refugees and migrants ) with a clear malaria diagnosis ( part of the malaria burden observational study ) were tested with the lab-on-chip assay . The sensitivity and specificity of the chip assay was then determined against microscopy diagnosis used by the Thailand clinics [16] . Non-malaria specimens collected from patients presenting with undifferentiated febrile illness were also evaluated with the lab-on-chip . Viral RNA extracted from acute plasma specimens that had been stored at −80°C since 2008 were used . These had previously been tested with a range of tests including Dengue RT-PCR [85] . In addition , whole blood samples from 5 native healthy volunteers were extracted using the DNeasy blood and tissue kit and QIAamp viral RNA mini kit ( Qiagen ) and used as negative controls . Cultures of the 3D7 clone of the NF54 strain of Plasmodium falciparum ( P . falciparum ) were performed using sealable flasks with RPMI-HEPES medium at pH 7 . 4 , supplemented with 50 mg/mL hypoxanthine , 25 mM NaHCO3 , 2 . 5 mg/mL gentamicin , and 0 . 5% ( weight/volume ) Albumax II ( Gibco , Singapore ) in an atmosphere containing 5% CO2 , as previously described [86] , [87] . The CHIKV isolate used in this study was originally isolated from a French patient returning from Reunion Island during the 2006 CHIKF outbreak [88] . After passages in Vero-E6 cultures , virus stocks were washed , and precleared by centrifugation before storing at −80°C . All virus stocks were titered by plaque assay and quantified by quantitative RT-PCR ( qRT-PCR ) as previously described [89] , [90] . Target gene sequences of each pathogen ( Table S1 in Text S1 ) were first obtained from Genbank database . Sequence alignments were performed using the ClustalW algorithm [91] in MegAlign ( DNAStar , Inc . , Madison , WI ) . A consensus sequence representing clinically relevant strains ( Table S1 in Text S1 ) was created for each pathogen . Each target oligonucleotide sequence was designed through multiple , successive steps of evaluation of candidate sequences , based on user-defined criteria , followed by analysis with Basic Local Alignment Tool ( BLAST ) [92] against the nucleotide sequence database ( nr/nt ) [93] for non-target genomes potentially present in the specimen that could cause interference . Genus-specific PCR primers were designed for all chosen target genes sequences as previously described [94] , [95] . Genus-specific ( for Plasmodium , Flaviviruses and Hantaviruses ) and species-specific capture probes were selected to target 2 to 4 regions of the targeted gene to confirm specificity and to overcome the problem of poor hybridization within the amplicon as a result of strain-specific gene polymorphisms . Efforts to improve specificity included the design of short length capture probes of 20 to 30 nucleotides in line with other studies which have shown that shorter length probes showed higher specificity [96] . For each pathogen , a PCR product encompassing the targeted region was prepared using the consensus sequence and cloned into the T7 polymerase expression vector pGEMT-easy ( Promega , Madison , WI ) as described [70] . Serial diluted plasmid DNA or in vitro-transcribed RNA from respective quantified stocks was used as the DNA copy number control for DNA pathogens or RNA copy number control for RNA pathogens . The lab-on-chip was manufactured on a silicon wafer based on MEMS and mounted on a printed circuit board ( PCB ) support that provides mechanical , thermal , and electrical connection [94] , [95] , [97] ( Figure S1 in Text S1 ) . It encompassed two silicon microreactors ( 12 µL ) connected to a microarray chamber . The microarray chamber ( 3 . 5 mm×9 . 0 mm ) contains 126 spots consisting of duplicate oligo-probes spotted onto the surface through a piezo-array system [95] to ensure that differential signals do not occur by chance . The enzymatic thermal cycling and hybridization reactions on the lab-on-chip are performed by the electronic TCS . Tropical pathogen detection was split into two chip versions to be subjected to two different multiplex reactions; DNA chip with a customized microarray layout specific for DNA pathogens and RNA chip specialized for RNA pathogen detection . PCR was performed on a DNA chip in a constituted reaction of 200 nM forward and 500 nM Cy5-conjugated reverse primers in 23 µL final volume using the QuantiTect multiplex RT-PCR NoROX kit ( Qiagen ) . Amplification was carried out with initial denaturation at 90°C for 15 min , followed by 45 cycles of 95°C for 15 sec , 60°C for 40 sec , and 72°C for 30 sec , then final extension at 72°C for 60 sec . RT-PCR was carried out on the RNA chip using SuperScript III one-step RT-PCR system with platinum Taq ( Life Technologies ) in a 23 µL reaction volume containing a concentration of forward and Cy5-conjugated reverse primers in the range of 200 nM to 700 nM . After a 20-min reverse transcription step at 50°C , enzyme activation was initiated at 95°C for 120 sec , followed by denaturation at 95°C for 10 sec . Amplification was performed in a manner of touch down PCR to enhance the specificity of the initial primer-template duplex formation and hence specificity of the final PCR product [98] . The annealing temperature in the initial cycle was initiated at 60°C ( 5°C above the average melting temperature of the primers for RNA pathogen detection ) . In the subsequent 10 cycles , the annealing temperature was decreased in steps of 1°C/cycle until a temperature was reached to 50°C , and followed by extension at 72°C for 50 sec . Following these 10 cycles , 40 cycles with a temperature of 95°C for 15 sec , annealing temperature of 56°C for 40 sec , and then a final extension for 50 sec at 72°C completed the program . Upon completion of PCR or RT-PCR , denaturation of amplicons proceeded at 95°C for 3 min , followed by hybridization at 58°C for 30 min . The lab-on-chip was washed and spin dried . The dried chip was scanned in the optical reader [95] ( Veredus Laboratories ) which has an excitation filter for Cy5 . Accompanied software analysis was based on hybridization of amplicons to target-specific capture probes with the highest signals expected from a perfect match . Spot segmentation and intensity calculation of the microarray image was performed by overlaying a virtual grid over the microarray image using the corner features as reference points . For positive detection of Plasmodium parasites , Flaviviruses and Hantavirus , at least 1 out of 2 genus-specific probes must give a positive signal to indicate the presence of the respective genera , and at least 50% of species-specific probes must hybridize for species differentiation ( Table S1 in Text S1 ) . For the rest of the pathogens , at least 2 out of 3 pathogen-specific probes must give a positive signal for a positive detection of the pathogen ( Table S1 in Text S1 ) . The detection threshold and specificity of the lab-on-chip assay was evaluated by using 4 µL of quantitative standards ( to cover a range of 101 to 104 copies per chip for each pathogen ) and assessing the signal intensity and presence of cross hybridization at each copy number . Triplicates were run to ensure intra-experimental reproducibility . The lowest titer ( DNA or RNA copies per chip ) with 2 or more out of 3 chips positive for the assayed pathogen was further expanded to another 21 replicate runs to confirm the LoD which was the indicated titer that would yield more than 95% positive detection , as well as to evaluate inter-assay reproducibility . Sorted P . falciparum parasites were serial diluted in phosphate-buffered saline ( PBS ) and added to whole blood to obtain spiked samples with final concentrations of 1 to 103 parasites/µL [87] . In parallel , CHIKV virus stock was serial diluted before spiking into aliquots of whole blood to cover 1 to 105 PFU/µL . Spiked experiments were repeated twice for inter-experimental reproducibility . Sensitivity of the chip assay was compared with that of nested PCR [99] or qRT-PCR [70] respectively . The volume of the isolated nuclei acid subsequently used in for all comparison assays was kept constant at 4 µL . All statistical analyses were performed using Prism 6 . 03 ( GraphPad Software , Inc . , La Jolla , CA ) . Lab-on-chip outcome on previously laboratory-confirmed samples was analyzed using Fisher exact test . P values less than 0 . 05 were considered statistically significant .
The objective of developing a portable microfluidic integrated lab-on-chip ( Figure S1 in Text S1 ) was to provide a seamless one-time screening test for multiple tropical pathogens that exhibit similar or non-specific symptoms . Twenty-six pathogen species that cause 14 globally important but yet neglected tropical diseases ( Table 1 and Table S1 in Text S1 ) were considered for the panel . A typical workflow for the detection of these pathogens was defined . It comprises of a processing step ( blue ) that includes the sample extraction and reaction setup . This is then followed by the on-chip identification and differentiation ( red ) ( Figure 1 ) to ensure accurate implementation of the assay . Microarray spots were simultaneously assessed to calculate differences in signal intensities , thereby identifying unique patterns ( Figure S2 in Text S1 ) . Hybridization to a series of target-specific probe sets provided presence/absence information for the tropical pathogen , while also revealing the species of the causative agent ( Figures S2 , S3 in Text S1 ) . The rationale of the analytical evaluation of the lab-on-chip was to define the LoD of the assay for all the pathogens . LoD of the lab-on-chip was determined as the lowest copy number which , in terms of plasmid copy for DNA or RNA transcript copy for RNA , when added to the chip , led to more than 95% positive pathogen identification outcome . Table S1 in Text S1 shows the lowest detectable dilution for each pathogen . The results revealed an individual sensitivity that ranged from 102 to 103 copies per chip ( Figure 2 ) . Target-specific hybridization signal saturation was observed at concentrations as low as 104 copies for all the pathogens ( Figure 2 ) . Notably , a highly sensitive detection range of 3 orders of magnitude between LoD and signal saturation was achieved for most of the tropical pathogens , mainly S . enterica , T . brucei and T . cruzi under the DNA pathogen category , together with RNA viruses such as West Nile virus , yellow fever virus , Enterovirus 71 and rift valley virus ( Figure 2 ) . Although a narrow detection range of 10 copies was observed for Hantaviruses with LoD at 103 copies , the rest of the pathogens stayed within the broad detection range of approximately 2 orders of magnitude . It should be noted that to date , cases of Hantavirus infections in patients yielded very low or non-detectable viral load levels [55] , [100] . Probe specificity evaluation showed no significant cross reactivity ( Figures S2 and S3 in Text S1 ) . The efficiency of a detection assay is often dependent on the efficiency of the nuclei acid extraction method from clinical specimens [101] , [102] . Some methods may even interfere with the PCR reaction [103] , [104] . The purpose of the investigation was to assess the efficiency of the extraction method and the sensitivity of the lab-on-chip using P . falciparum and CHIKV as targets . The read-out for the lab-on-chip and that of nested PCR is illustrated in Table 2 and in Figure 3 . The presence of P . falciparum in the extracted spiked samples was demonstrated by the presence of hybridized genus-specific and species-specific probes on the microarray for lab-on-chip , while that by nested PCR relied on the presence a PCR band on agarose gel [99] . Positive detection of P . falciparum by the lab-on-chip was observed at 100 parasites , while positive bands were detected at 5 parasites by nested PCR ( Table 2 and Figure 3 ) . Although the nested PCR method [99] is more sensitive with a difference of more than one log when compared to the lab-on-chip ( Table 2 and Figure 3 ) , it is more labor intensive . The estimated PFU isolated from CHIKV-spiked samples ( in red ) compared to the viral load derived from qRT-PCR is shown in Table 3 and in Figure 4 . The detection threshold for CHIKV was 50 PFU ( Figure 4B , 4C ) . More importantly , the sensitivity of the detection range of the lab-on-chip and viral load quantification by qRT-PCR are similar , clearly demonstrating the superiority of the lab-on-chip ( Figure 4 ) . In order to assess the clinical performance of the assay , the lab-on-chip was evaluated on retrospective clinical specimens to compare its diagnostic capability with reference methods . The screening and order of diagnostic testing of 170 samples received in Singapore and Thailand are illustrated in Figure 5 . Sixty-four out of 77 P . falciparum positive samples and 21 out of 23 P . vivax positive samples were concordant with the microscopic diagnosis ( Tables 4 , 5 ) . The sensitivity and the specificity for the detection of P . falciparum was 83 . 1% ( 72 . 9% to 90 . 7% ) and 100% ( 96 . 1% to 100% ) ( Table 4 , Figure 6 ) , and that of P . vivax was 91 . 3% ( 71 . 9% to 98 . 9% ) and 99 . 3% ( 96 . 3% to 99 . 9% ) ( Table 5 , Figure 6 ) . Fourteen P . falciparum positive samples with low levels of parasitemia did not yield a positive detection for P . falciparum , but 11 out of the 14 were tested positive for Plasmodium . Although species differentiation was not achieved with these 11 samples , the assay did provide a diagnosis for Plasmodium . The validation also yielded a good positive 90 . 0% agreement ( 73 . 5% to 97 . 9% ) and excellent specificity 100% ( 97 . 4% to 100% ) for the CHIKV detection ( Table 6 , Figure 6 ) . Finally , the assay showed an average positive 85% agreement ( 62 . 1% to 96 . 8% ) ( 17 out of 20 DENV positive samples ) and a specificity of 100% ( 97 . 5% to 100% ) for DENV 3 detection ( Table 7 , Figure 6 ) . The 3 CHIKV samples that were not detected positive by the lab-on-chip were that with low viral load of less than 102 viral copies/µL quantified by qRT-PCR [70] . All healthy donor samples tested were negative .
While every disease presents specific diagnostic challenges , clinical needs associated with specificity , sensitivity , total analysis time , and implementation would eventually impact the design and development of the diagnostic method . In this study , an integrated strategy for miniaturizing and simplifying complex laboratory assays for the detection of 14 globally important tropical diseases stood out favorably in terms of seamless implementation and pathogen coverage compared to conventional laboratory diagnostic methodologies . The mainstay to detect protozoan infections such as Chagas disease , human African trypanosomiasis , and malaria infection relies in the conclusive visualization of the parasites in blood [18] , [21] , [105] . The reliable identification of these infections requires high quality training in specimen preparation and a competency in identifying the parasites when compared to the facile interpretation of the lab-on-chip microarray analysis . Bacteria culture remains as one of the most effective procedures in identifying bacterial infections [106]–[108] and is also crucial in generating pools of clinical strains for pathogenesis studies . However , the process is labor and time intensive , spanning from a few days to several weeks when compared to the lab-on-chip assay that is completed within 4 hours . It is also dependent on stringent transport conditions and well-maintained equipments to maintain specimen viability . While methods based on serological reactivity to pathogen-specific antibodies [109]–[111] have been developed to identify several viral infections and are useful in differentiating viruses within the same family or genus , cross reactivity remains a conflicting issue [100] , [112] , [113] . In spite of cross reactivity issues , serology is still widely used to confirm diagnosis due to limitations in the detection window of nucleic acids [83] , [85] , [100] . Here , the analytical performance of the lab-on-chip has highlighted its specificity with no cross reactivity observed between the 5 Plasmodium species , between DENV and the other 3 Flaviviruses , and among the 6 Hantaviruses , achieved in just one test . Future iterations of the lab-on-chip could include protein-based arrays as additional serology screens [114] , [115] for some diseases that are clinically warranted as orthogonal diagnosis based on nucleic acid , protein , and other biomarkers will be where the field is heading . Simultaneous laboratory screening of a clinical specimen from a patient with unspecific symptoms for as many tropical agents as possible would either lead to pathogen identification or narrow down the possible causes through elimination . However , combining the various assays for parallel screening of tropical diseases is not a feasible approach given the high diversity of the protocols with many limitations associated with each pathogen . Even though amplification microarray assays [80]–[82] have been developed to circumvent the need for parallel tests , detection in these assays was restricted to one virus family , despite an improvement in pathogen coverage , and thus still considered as low throughput . Moreover , simultaneous detection was achieved only after 3 separate amplification reactions for the 3 respective virus families [80] . Miniaturized total analysis systems [116] have evolved , that has led to miniaturized PCR devices being extensively studied [117] . A few reports have demonstrated rapid on-chip detection of Influenza A virus [118] , [119] and human immunodeficiency virus [120] , however the development of a miniaturized assay for the detection of multiple tropical diseases pathogens including the validation on patient specimens has yet to be demonstrated . The design and process of the lab-on-chip evaluation was approached systematically . It was first evaluated using quantitative standards . The LoD of the lab-on-chip was shown to range from 102 to 103 copies and signal saturation for target-specific capture probes' hybridization was at 104 copies . This observation was crucial as the efficiency of the chip to detect the relevant pathogen in a clinical sample load on the chip containing 104 or more copies of that pathogen would be 100% . When considering the detection limit of the lab-on-chip of the pathogen in a clinical sample , the target concentration required to get the minimum amount of nuclei acids after sample extraction in the amplification reaction must be investigated . Comparison of the lab-on-chip with nested PCR using spiked P . falciparum samples and with qRT-PCR on spiked CHIKV samples has proven the efficiency of the extraction method and also emphasized a more superior trade-off between the assay's sensitivity and its utility in the systemic differentiation of P . falciparum and detection of CHIKV . The lab-on-chip assay's ability to detect CHIKV at 50 PFU/µL demonstrated high clinical relevance as it was shown that the mean CHIKV viral load in patients ranged between 126 to 241 PFU/µL [83] . One of the key objectives of the clinical validation was to investigate the lab-on-chip's performance and acceptability in field settings and the degree to which the results would determine the quality of the diagnosis for surveillance and patient management to improve health outcomes . The clinical validation of P . vivax offered a sensitivity that was equivalent to microscopy . Although there was a proportion of P . falciparium samples ( 14 out of 77 samples ) with low parasitiamia that were not positively detected for P . falciparum on the lab-on-chip , the assay did manage to give a partial diagnosis ( of the samples being Plasmodium positive ) for 11 of these samples . Although the lab-on-chip did not positively differentiate samples with extremely low levels of parasitemia , the low parasite burden of these patients could represent the early stages of malaria . Taken together , the analytical performance of the lab-on-chip for P . falciparum and P . vivax in the range of 102 copies , and the demonstration of its diagnostic utility using spiked samples and clinical specimens showed the applicability of the assay for Plasmodium detection . The clinical performance of the lab-on-chip for DENV and CHIKV was comparable to RT-PCR . For DENV , comparisons among the diagnostic tests at SMRU have demonstrated RT-PCR to have the best operating characteristics ( sensitivity 89% , specificity 96% , positive predictive value 94% , negative predictive value 92% ) [85] . This suggested that the chip would be potentially sufficient to function as a single assay for confirmation of Dengue infection , since it allowed for accurate confirmation . Similarly , the assay sensitivity for CHIKV was on par with that of RT-PCR , and achieved a positive 90% agreement with patients' samples . The cost of the assay compared to that of single assays is high . Advancements in the integration of the lab-on-chip with nuclei extraction capabilities [95] and a higher density microarray with reduced chip cost would provide a more cost-effective comprehensive coverage . While the lab-on-chip assay has showed that miniaturized multiplex PCR could reach the desired clinical sensitivity , future work should attempt to recalibrate the mix of multiplex primers and modify amplification cycling conditions for improved sensitivity . One of the key milestones for lab-on-chip systems would be the direct testing of clinical specimens obtained during the acute infection phase and provide accurate diagnosis to complement clinical assessments .
|
Tropical diseases consist of a group of debilitating and fatal infections that occur primarily in rural and urban settings of tropical and subtropical countries . While the primary indices of an infection are mostly the presentation of clinical signs and symptoms , outcomes due to an infection with tropical pathogens are often unspecific . Accurate diagnosis is crucial for timely intervention , appropriate and adequate treatments , and patient management to prevent development of sequelae and transmission . Although , multiplex assays are available for the simultaneous detection of tropical pathogens , they are generally of low throughput . Performing parallel assays to cover the detection for a comprehensive scope of tropical infections that include protozoan , bacterial and viral infections is undoubtedly labor-intensive and time consuming . We present an integrated lab-on-chip using microfluidics technology coupled with reverse transcription ( RT ) , PCR amplification , and microarray hybridization for the simultaneous identification and differentiation of 26 tropical pathogens that cause 14 globally important tropical diseases . Such diagnostics capacity would facilitate evidence-based management of patients , improve the specificity of treatment and , in some cases , even allow contact tracing and other disease-control measures .
|
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"Introduction",
"Materials",
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"Discussion"
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"bacteriology",
"infectious",
"diseases",
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"diagnostic",
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2014
|
An Integrated Lab-on-Chip for Rapid Identification and Simultaneous Differentiation of Tropical Pathogens
|
Yaws is an infectious , debilitating and disfiguring disease of poverty that mainly affects children in rural communities in tropical areas . In Cameroon , mass-treatment campaigns carried out in the 1950s reduced yaws to such low levels that it was presumed the disease was eradicated . In 2010 , an epidemiological study in Bankim Health District detected 29 cases of yaws . Five different means of detecting yaws in clinical and community settings were initiated in Bankim over the following five years . This observational study reviews data on the number of cases of yaws identified by each of the five yaws detection approaches: 1 ) passive yaws detection at local clinics after staff attended Neglected Tropical Disease awareness workshops , 2 ) community-based case detection carried out in remote communities by hospital staff who relied on community health workers to identify cases , 3 ) yaws screening following mass Buruli Ulcer outreach programs being piloted in the district , 4 ) school-based screening programs conducted as stand-alone and follow-up activities to mass outreach events , and 5 ) house to house active surveillance activities conducted in thirty-eight communities . Implementation of each of the four community-based approaches was observed by a team of health social scientists tasked with assessing the strengths and limitations of each detection method . Eight hundred and fifteen cases of yaws were detected between 2012 and 2015 . Only 7% were detected at local clinics . Small outreach programs and household surveys detected yaws in a broad spectrum of communities . The most successful means of yaws detection , accounting for over 70% of cases identified , were mass outreach programs and school based screenings in communities where yaws was detected . The five interventions for detecting yaws had a synergistic effect and proved to be valuable components of a yaws eradication program . Well planned , culturally sensitive mass outreach educational programs accompanied by school-based programs proved to be particularly effective in Bankim . Including yaws detection in a Buruli Ulcer outreach program constituted a win-win situation , as the demonstration effect of yaws treatment ( rapid cure ) increased confidence in early Buruli ulcer treatment . Mass outreach programs functioned as magnets for both diseases as well as other kinds of chronic wounds that future outreach programs need to address .
Yaws is an infectious , debilitating , and disfiguring disease of poverty that mainly affects children and adolescents living in rural communities in tropical areas of Africa , the Pacific Islands , and Southeast Asia with high levels of rainfall . Caused by the spirochete bacteria Treponema pallidum , subspecies pertenue is closely related to syphilis and one of three endemic non-venereal treponemal diseases . The bacterium causes a chronic relapsing treponematosis characterized by highly contagious primary and secondary cutaneous lesions and non-contagious tertiary destructive lesions of the bones . Humans are the primary reservoir for yaws and transmission occurs through skin to skin contact with the exudate of lesions by those who have skin abrasions or cuts . Yaws is usually contracted in childhood ( 75% of cases occur before age 15 ) and infectious lesions are infrequent after the age of 30 [1 , 2] . In the early stage of the disease , which may last from weeks to months , skin lesions are highly contagious and present differently by season with more open infectious lesions and papillomatous frambesides in the wet season and drier , scalier , maculopapular lesions in the dry season . Painful and itching lesions commonly appear on the upper and lower limbs , fingers , toes , soles of the feet , face , genital areas , and buttocks . The early stage is typically characterized by a single elevated primary lesion that develops a crust that is shed , followed by secondary lesions on other parts of the body . After 3–4 months lesions subside due to host immune response . The disease then becomes latent . In about 10% of untreated patients , the infection progresses to the tertiary stage characterized by destruction of tissue , bone , and cartilage resulting in disfigurement and disability . Once widespread in the tropics , the incidence of yaws has been controlled though a combination of mass treatment with single dose of antibiotics ( injectable benzathine benzylpenicillin ) along with better hygiene and access to clean water . It has been estimated that yaws control efforts mounted by the World Health Organization ( WHO ) and United Nations International Children's Emergency Fund ( UNICEF ) resulted in up to a 95% reduction of the disease burden worldwide . Efforts are currently underway to eradicate the disease by 2020 following the Morges strategy , which calls for an initial mass treatment of endemic communities with Azithromycin in tablet form [2] followed by ongoing active community-based surveillance system and if required surveys every 3–6 months to detect and treat remaining cases of yaws and their contacts [3] Yaws continues to be endemic in at least 13 countries globally , of which Cameroon is one [4] . Eradication will require better surveillance , health worker training , community outreach , and targeted mass drug treatment when and where necessary . In Cameroon , mass-treatment campaigns carried out in the 1950s reduced yaws to such low levels that it was presumed the disease was eradicated except among groups of pygmies living in the dense forest . In 2007 and 2008 outbreaks of yaws occurred among pygmy groups in Lomié health district . Cameroon’s National Neglected Tropical Disease ( NTD ) Control Program ( covering Buruli ulcer ( BU ) , leishmaniasis , yaws , and leprosy ) working in conjunction with the NGO FAIRMED carried out an epidemiological survey in the district of Lomié in 2009 . One hundred sixty-seven cases of yaws were detected in 35 small communities surveilled . Seventy five percent of cases were children under the age of 15 years with a majority between 9–11 years of age . Yaws surveillance was not included in the routine disease surveillance system elsewhere in Cameroon and assumed to be a problem confined to the pigmy population . This changed when an epidemiological study of leprosy , yaws , and BU was carried out in Bankim district in 2010 . The study entailed an intensive house-to-house survey conducted in late March to mid-April during which time 9 , 344 households were visited and 48 , 962 people examined . Twenty-nine confirmed cases of yaws were detected [5] . It became evident that either those afflicted with yaws were not coming to clinics for treatment or health staff were failing to recognize and treat the disease , confusing it perhaps for scabies . As a follow up to the survey , three day NTD workshops were conducted by the National Disease Control Program in 2012 in Bankim and surrounding districts attended by hospital and clinic staff . The objective of the workshops was to better familiarize health workers with the signs of BU , leprosy , and yaws; encourage them to identify presumptive cases; and send swabs for laboratory confirmation . Disease control officers began visiting communities in 2012–2013 in an attempt to identify cases and inform community health workers ( CHWs ) about the disease . During these visits , CHWs were shown posters displaying the signs of yaws and BU and asked to identify suspected cases in their communities . In 2013 , an innovative community-based outreach program was launched in Bankim by the NGO FAIRMED working in conjunction with the government health service and the Stop Buruli Consortium . The three objectives of the outreach program were to raise awareness about BU , identify cases , especially early category one cases , and create collaborative relationships between clinic staff , CHWs , traditional healers , and local chiefs . Community health workers were mobilized and tasked with organizing mass community BU outreach events . The culturally sensitive program that was introduced differed from previous outreach programs conducted in the Cameroon . In the past , information about BU was conveyed from health staff to the local population in a top down manner without community feedback elicited . The innovative program being piloted drew upon a year of formative research carried out by teams of social scientists in Bankim on patterns of health care seeking for BU and other chronic ulcers . The education program introduced went well beyond educating the public about the signs of BU . It employed a question and answer format that encouraged two-way dialogue between community members and health staff . Participants were shown before and after photographs of BU-related wounds depicting the healing process when appropriate treatment was followed . Time was allotted for testimonials by those cured of BU . Former patients attested to the quality of care they had received by clinic staff in what was described as a newly upgraded BU treatment program . Community members were also given explanations for all health staff actions including the collection of blood for disease confirmation . Following the educational program , screening by government health workers took place for those having lesions that were possible signs of BU . Although the focus of the outreach program was BU , many cases of yaws began to be detected . In communities where yaws was identified , teams returned and conducted school-based yaws screening and education programs . This paper examines the relative utility of five approaches to yaws detection in rural settings of Cameroon: We then present a brief overview of data collected on the distribution of yaws cases in the community and lessons learned about the best times to conduct yaws detection activities .
The National Ethics Committee for Health Research overseen by the Cameroon Ministry of Public Health Cameroon approved this study . All study participants voluntarily opted into the study through documented informed consent . In cases where children were interviewed or their blood was drawn for testing , consent forms were secured from parents after being informed why a test was being administered .
Eight hundred and fifteen ( 815 ) cases of suspected yaws were detected between 2012 and 2015 in Bankim district . Blood samples from 120-suspected cases were sent to Bankim Health District Laboratory for testing , of which 100 cases were < 16 years of age , 16 were 16–30 years of age , and four over > 30 years of age . The RPR confirmation rate was 85% and the TPHA confirmation rate 77% . It is possible that some of the remaining 23% of cases included people treated for yaws or syphilis in the past . All 815 cases were followed up four weeks after antibiotic treatment was administered . Complete recovery was observed in 89% of cases with the remaining 11% of cases found symptom free three weeks later . Tables 1 and 2 summarize how the 815 yaws cases were detected . As a means of assessing the cumulative effect of the four community outreach activities , it may be noted that no yaws cases were recorded in Bankim district in the five years prior to 2012 , when outreach activities were initiated . Furthermore , between 2012 and 2015 only four cases of yaws were reported at clinics in the neighboring district of Malentouen , although health staff in this district had attended a three-day NTD workshop in 2012 alerting them to the presence of yaws in the region . In Malentouten district , community based outreach activities had yet been introduced . Five observations may be highlighted . First , even after yaws awareness training for health staff working in clinics and three years of outreach activities where health staff encouraged community members with yaws-like symptoms to visit clinics , only 7% of all yaws cases were detected at clinics . This suggests that community members with yaws-like symptoms are not commonly visiting clinics for treatment ( see Agana-Nsiire [6] for a similar finding in Ghana ) . Ethnographic research confirmed this observation . The symptoms of yaws ( itching and moderate levels of pain ) are not seen to be serious enough to warrant seeking care at a clinic , especially if a clinic is distant . The fact that the symptoms of yaws eventually subside ( as the disease becomes latent ) led some community members to conclude that the disease was self-limiting , recurrent , or seasonal . Despite a rising level of awareness within the local population about yaws and the effectiveness of drug therapy resulting from outreach programs , most community members afflicted with yaws-like symptoms preferred to wait for outreach screening events rather than travel to clinics . This is evidenced by clinic data that documents only a small increase in yaws cases seen at clinics in Bankim during the four-year period . Second , NTD outreach activities in remote communities identified yaws cases largely based on the mobilization efforts of CHWs . In 2012 and 2013 health staff visited six small to moderate sized ( < 100 households ) remote communities in December and January . Thirty-one cases of yaws were identified and treated . In December–January 2014 , the Stop Buruli team visited another 10 remote communities ( of similar size ) searching for both BU and yaws cases . CHWs exposed to basic information about the two diseases were asked to identify possible cases in their community . Together with health staff , CHWs detected sixty cases of yaws . In total 11% of all cases of yaws were identified through this outreach approach , yielding a mean of 5 . 5 cases per outreach activity . Third , a big spike in yaws detection occurred with the initiation of mass BU outreach program events followed by school screenings in communities where yaws was detected . These programs were held in mid-November through January , months when a majority of the local population reside in their homes and are not engaged in agricultural activities some distance away . Programs were conducted in moderate to large sized communities with schools . They were held in the early evening , attended by community leaders , and seen by community members as a major event . Light entertainment preceded the education program . At first , the outreach team only focused on BU and did not pay much attention to other kinds of skin lesions . However , after a number of yaws cases were detected among children in 2013 , the decision was made to be more proactive and screen for yaws . Between 2013 and 2015 , 44 screenings were held after mass BU outreach events . When cases of yaws were identified in a community , school screenings were arranged and carried out . Three hundred and twenty-eight cases of yaws were detected with a mean yield of 9 . 4 case per mass event/school screening activity set . Notably , the case yield increased over time as people came to see health staff as accessible and free medication available for both yaws and BU . In 2013 , 4 . 4 cases of yaws per activity set were detected . In 2014 the case yield was 8 . 9 , and in 2015 17 . 5 cases . A fourth observation focuses attention on the importance of school based yaws detection programs . Twenty-seven percent of all yaws cases detected between 2012 and 2015 were identified during stand-alone school screenings in communities where no mass BU outreach event had been held . In 2012 , school programs held in five moderate sized communities yielded 8 cases of yaws per activity . By 2015 , stand-alone school screening events yielded 9 . 8 cases per screening . Social scientists found that large BU outreach events supported by community leaders in nearby communities helped legitimize school- based screening programs . The parents of children had a much better idea of why screenings in schools were being held and had confidence in the medication offered given the circulation of stories of successful yaws treatment . In addition to detecting cases at schools , school-based programs taught students how to identify the signs of yaws . The social science team investigated whether identifying children with yaws in the school would be stigmatizing . Observations and interviews with children did not find this to be the case . Those conducting the education program made it clear that yaws was easily treated with just one injection and the demonstration effect of classmates recovering rapidly from symptoms made the program popular . By 2014 , students were asked to examine each other for “tell-tale signs” of yaws and encouraged to identify possible cases of yaws in children either too young to attend school or who stopped going to school as a result of painful or unsightly lesions . In short , schoolchildren were enlisted to assist in community based identification of yaws among their peers and those children they helped care for at home . A fifth observation entails the effectiveness of house-to-house surveys as a strategy for achieving yaws eradication in rural areas of Cameroon . As the result of an unfortunate break in funding in 2015 , Stop Buruli mass outreach activities were suspended . To keep up the momentum of NTD activities , FAIRMED in conjunction with Cameroon’s government NTD program conducted school based screenings as well as an intensive house-to-house NTD survey . The two phase survey was carried out during late August and mid-November . In all , 1 , 889 houses in 38 communities were canvassed and 110 cases of yaws detected , 13% of total yaws cases identified between 2012 and 2015 . Two points may be made . First , the number of yaws cases in the 2015 survey far exceeded the number of cases detected in 2010 , when a much larger survey ( N = 9 , 344 ) was carried out in the month of March . March is a busy month for agriculturalists and many people are working in fields far from their community . In 2015 , yaws was detected in 6% of all households while in the 2010 survey cases were found in only . 3% of households visited . A second point was that the program required a significant investment of health staff . In 2015 , the participation of seven hospital staff members was required for 10 days of arduous surveillance activities . This constituted a significant opportunity cost for a busy district hospital like Bankim , which serves a population of about 100 , 000 inhabitants . Table 3 summarizes the relative advantages , limitations and logistical challenges of each type of yaws detection activity observed .
Data was collected on the distribution of yaws cases by age , gender , ethnic group , and household occupation as well as season . An analysis of cases of yaws by age conforms to a well-described epidemiological pattern ( Fig 2 ) . Eighty-four percent ( 84% ) of yaws cases were under 15 years of age with 26% of children being under the age of 5 years . The large number of young children suffering from yaws suggests that school-based programs alone are insufficient to reach a significant percentage of high-risk children . The gender distribution of children detected with yaws is presented in Table 4 . We found a greater number of male cases in all age categories over the course of the four years of the study . In Bankim , as in much of West Africa , sibling care is common . Sixty-six percent ( 66% ) of all children symptomatic for yaws are of school-going age . They not only have frequent skin to skin contact with classmates in school , but younger siblings . Observations of sibling childcare by social scientists documented that while school-going females more commonly care for younger siblings , school aged males do so as well . Breaking the chain of transmission required teaching school children how to recognize the signs of yaws in their siblings . The most common types of yaws lesions detected between 2012 and 2015 are presented in Table 5 . Notably , the most common lesions are also the most contagious ( ulcers—69% and papilloma—19% ) . We examined the distribution of yaws cases by locale as well as ethnic and occupational group . Data on the six health areas that comprise Bankim district revealed that yaws cases were widely distributed with hot spots in both towns and villages . The maximum number of cases was detected in locales proximate to the Mbam River and Mape dam . Analysis of data on yaws cases by ethnic groups likewise revealed broad distribution of cases across the six largest ethnic groups in the district as distinct from clustering in any particular group . One finds both single and mixed ethnic group settlements in Bankim . The largest ethnic groups were the groups with the most exposure to outreach programs and the most cases of yaws detected . Analysis by occupation found broad distribution across communities that rely on both agriculture and fishing . We next looked at yaws cases detected by month . The majority of cases were identified during outreach screening activities when community members were more likely to be at home . Although carried out throughout the year , outreach activities were easier to conduct in some seasons due to climate , transportation , agricultural cycles , school registration , and ritual activities in the region . Peak months of yaws detection in the community were August–September and November–December . Fewer cases were detected from January to June .
In this study , we ascertained the relative effectiveness of five methods of detecting yaws from a review of records of patients treated for the disease in different clinic and community contexts . One of the lessons learned is that single NTD disease focused outreach programs , like the mass BU events described in this paper , attracts community members with a wide range of chronic skin diseases like yaws . We initially identified yaws cases serendipitously . Over time we felt the need to be more proactive in identifying yaws cases , as we deemed it unethical not to do so . A limitation of the project was that we did not add yaws messages to the mass BU outreach events given that the novel BU education approach piloted was being evaluated . The addition of yaws messages might have constituted a confounding variable negatively impacting BU message evaluation . Furthermore , we had not conducted formative research on yaws necessary for the design of culturally appropriate messages . Basic yaws recognition was included in school-based programs . In the near future , we will integrate yaws messages into all community and school based NTD education programs once such messages are pretested . WHO recommendations for eradicating yaws include mass treatment with oral azithromycin , the use of recently developed rapid diagnostic tests , and three to six month follow up in endemic communities . Cameroon is in the process of adopting these measures as soon as resources and manpower become available . In remote locations like Bankim , yaws eradication will prove challenging due to poor transportation , population movements , and ethnic diversity requiring that education to be delivered in multiple languages . The five kinds of interventions described in this paper will need to be coupled with mass treatment strategies in order to achieve the level of community outreach needed for eradication . Yaws eradication may also require a more comprehensive approach to neglected tropical skin diseases and wound care . During Bankim outreach activities , health staff encountered many cases of chronic wounds that were either neglected or being treated inappropriately , leading to complications . Only focusing on BU and yaws and neglecting other wounds and lesions sends a message to the community that some wounds and skin diseases matter more than others , and that treating specific diseases matters more than providing care to people suffering from debilitating skin conditions . Better diagnostic testing will only add to this perception as more lesions , that to community members look similar to yaws or BU , are ruled out for free treatment [18 , 19 , 20] . Outreach programs in community and school settings combining NTD disease surveillance with wound care education , and when appropriate free treatment , would help address this problem . Such programs would serve a broader primary health care agenda [21] and minimize the kinds of rumors that have undermined other NTD programs in Africa [22 , 23 , 24] including BU [25] . In West Africa , local perceptions of illness and the agenda of those conducting public health programs matter . In order to be sustainable , NTD programs will need to build community trust . Much of the success of the Bankim program can be attributed to concerted efforts to respect and involve all community stakeholders in NTD activities .
|
Yaws is an infectious and disfiguring disease of poverty primarily affecting children in rural communities in tropical areas . Yaws is easily treated by a single dose of antibiotics and is on the World Health Organization’s eradication list . Yaws was thought eradicated in the Cameroon in the 1950s following aggressive mass-treatment campaigns . In 2010 , epidemiological research revealed a resurgence of the disease . This paper discusses the relative success of five different means of detecting yaws in rural areas of Bankim District between 2012 and 2015 . While few cases of yaws were detected at local clinics during this time , many cases were detected in the community . The most successful means of detecting yaws were mass outreach programs designed to educate the public about neglected tropical diseases found in the region , and follow up school-based screening programs . These programs were supported by local chiefs and traditional healers and found to be the best way of increasing community awareness about yaws , motivating community health workers to participate in outreach , and fostering trust in the free medical treatment being provided .
|
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2017
|
Yaws resurgence in Bankim, Cameroon: The relative effectiveness of different means of detection in rural communities
|
In most eukaryotes , the prophase of the first meiotic division is characterized by a high level of homologous recombination between homologous chromosomes . Recombination events are not distributed evenly within the genome , but vary both locally and at large scale . Locally , most recombination events are clustered in short intervals ( a few kilobases ) called hotspots , separated by large intervening regions with no or very little recombination . Despite the importance of regulating both the frequency and the distribution of recombination events , the genetic factors controlling the activity of the recombination hotspots in mammals are still poorly understood . We previously characterized a recombination hotspot located close to the Psmb9 gene in the mouse major histocompatibility complex by sperm typing , demonstrating that it is a site of recombination initiation . With the goal of uncovering some of the genetic factors controlling the activity of this initiation site , we analyzed this hotspot in both male and female germ lines and compared the level of recombination in different hybrid mice . We show that a haplotype-specific element acts at distance and in trans to activate about 2 , 000-fold the recombination activity at Psmb9 . Another haplotype-specific element acts in cis to repress initiation of recombination , and we propose this control to be due to polymorphisms located within the initiation zone . In addition , we describe subtle variations in the frequency and distribution of recombination events related to strain and sex differences . These findings show that most regulations observed act at the level of initiation and provide the first analysis of the control of the activity of a meiotic recombination hotspot in the mouse genome that reveals the interactions of elements located both in and outside the hotspot .
In most eukaryotes , the formation of at least one reciprocal exchange , or crossover ( CO ) , per chromosome pair between nonsister chromatids provides the physical connection that is required for the segregation of homologous chromosomes at the first meiotic division . The molecular mechanism of meiotic recombination has been described in Saccharomyces cerevisiae . Recombination is initiated by the formation of DNA double-strand breaks ( DSBs ) , catalyzed by Spo11 [1] . These DSBs are repaired by interactions with a nonsister chromatid , giving rise to two types of homologous recombination products , CO and gene conversion without associated crossover ( NCO ) . Both types of recombination events have in common the gene conversion of the sequences surrounding the DSB site . Despite the fact that both events are initiated by Spo11-dependent DSBs , formations of CO and NCO have different genetic requirements and are thought to involve different molecular intermediates [2 , 3] . Therefore , both the distribution of DSBs and the local variation in the proportion of events giving rise to a CO contribute to the final distribution of CO along the genome . Among the strongest evidence in favor of the conservation of the molecular mechanism of meiotic recombination among eukaryotes is the wide conservation of Spo11 , which has been found to be required for meiotic recombination in all organisms in which it has been tested [1] . A number of other proteins involved in later steps in the process of meiotic recombination are also structurally and functionally conserved [4 , 5] . Meiotic recombination events are not distributed evenly within the genome , but vary both locally and on a large scale . An important feature of local variation is the presence of recombination hotspots , which are defined as intervals where the recombination rate is significantly higher than in neighboring intervals . In S . cerevisiae recombination hotspots result from the clustering of initiating DSBs at localized preferential sites , spreading over 50–500 bp . The recombination events , both CO and NCO , extend in short intervals ( <4 kb ) surrounding these initiation sites ( reviewed in [6] ) . Most recombination events take place at these hotspots , as shown by the correlation of the genetic map with the distribution of meiotic DSBs [7–10] . Sperm-typing analyses of a few mouse and human CO hotspots revealed a structure reminiscent of the distribution of recombination events in yeast hotspots: CO are clustered over 2 kb or less , with a density that peaks at hotspot centers and decreases on both sides ( reviewed in [11 , 12] ) . A high frequency of gene conversion without reciprocal exchange ( NCO ) has been detected at the mouse Psmb9 hotspot and at several human hotspots , demonstrating that they correspond to sites of recombination initiation [13–16] . Consistent with the hypothesis of CO hotspots as initiation sites , DNA breaks have been detected in testes at the mouse Ea CO hotspot [17] . Several observations show that most recombination is concentrated in hotspots in mammals as well as in yeast . In the mouse major histocompatibility complex ( MHC ) , five to eight CO hotspots have been identified by pedigree analyses , accounting for most of the CO detected in this region [18 , 19] . In human , the distribution of CO has been analyzed in a 292-kb interval in the class II region of MHC by population studies , which were completed by sperm-typing analyses over short selected intervals . These studies revealed that the vast majority of the CO occurring in this region localize at seven hotspots [20 , 21] . Similarly , the majority of CO is clustered in short hotspot intervals also in intervals located outside the MHC , as shown by one pedigree analysis in mouse and a few sperm-typing analyses in men [22–24] . These conclusions have been extended recently to the whole human genome by genome-wide determination of historical CO rates on the basis of population analysis [25 , 26] . With this methodology , Myers et al . [26] came to the conclusion that most CO occur in hotspots: They estimated that 80% of all recombination occurs in 10%–20% of the human genome and identified more than 25 , 000 CO hotspots . The factors controlling the activity of recombination hotspots remain elusive . One approach has been to search for features related to the DNA sequence by comparing the genome-wide distribution of hotspots with the variation of various features along genomes . In budding yeast , one of the most striking feature of DSBs is their localization in intergenic intervals containing a transcription promoter , though transcription activity per se is not required [6–8] . However , all transcription promoters do not contain an initiation site . On a large scale , initiation sites are not randomly distributed and are clustered over large chromosomal domains several tens of kb long , often in regions with a high GC content [6 , 8 , 9] . In mouse and human , only a small number of contemporaneous recombination hotspots have been identified and characterized . Nevertheless , they do not localize preferentially in promoter regions , contrary to what is observed in budding yeast ( see [11] ) . The genome-wide distribution of historical CO hotspots in human revealed several motifs that are overrepresented in hotspot regions , the most prominent being a 7-mer ( CCTCCCT ) associated with 11% of the hotspots [26] . Interestingly , the regions with high contemporaneous CO frequency in mouse , defined at the Mb scale , are also enriched for this motif , suggesting that it might correspond to a conserved function across mammals [27] . It should be noted that these short motifs are abundant in the genome , and therefore are not sufficient solely to explain the localization of the hotspots . A second approach aims to find factors involved in hotspot activity control through the detailed analysis of individual hotspots . In budding and fission yeasts , the presence of open chromatin at initiation sites is important for the formation of DSBs [7 , 28] . At some hotspots , the binding of transcription factors or chromatin remodeling factors has been shown to be required for DSB formation ( reviewed in [6 , 29] ) . However , no generalization could be made about the factors regulating recombination initiation . In humans and mice , the activity of several CO hotspots has been shown to vary between individuals ( humans ) or strains ( mice ) , as shown either by pedigree analysis or by sperm typing . Individual variation in CO rates , up to 75-fold , have been observed for most of the human hotspots that have been analyzed by sperm typing in several individuals [30] . At two of these hotspots , a single nucleotide change at the center of the hotspot appears to be at the origin of a 3- to 6-fold variation in cis of the initiation frequency [31 , 32] . At two others , MSTM1a and MSTM1b , CO rates are mainly determined by factors other than their own sequence [30] . In mice , a wide range of MHC haplotypes isolated in congenic lines have been instrumental for drawing a picture of haplotype-specific differences in recombination patterns across this region . Some of the hotspots identified in the mouse MHC by pedigree analysis , like the one located in the Eb gene , are common to most haplotypes from common laboratory strains . Others , like the Ea and Psmb9 hotspots , are specific to one or a few haplotypes , though a significant rate of CO might remain undetected in other haplotypes due to the insufficient sensitivity of pedigree analyses [18] . The CO rate at these two hotspots appears to be regulated , at least in part , by determinants located outside the interval where exchanges actually occur [33 , 34] . DNA sequence independent control of meiotic recombination is also illustrated by sex-specific differences in rate and distribution of CO as observed in many species . In human , and to a lesser extent in mouse , overall CO rates are significantly higher in female than in male ( 4 , 400 cM versus 2 , 700 cM in human , reviewed in [35]; 1 , 800 cM versus 1 , 400 cM in mouse [27] ) . Though the succession of regions with high and low CO densities is broadly the same in both sexes , some regions display sex-specific variation . In particular , both in mouse and human , males exhibit higher rates of recombination than females in telomeric regions , while the opposite is true near the centromeres [27 , 36 , 37] . The factors at the origin of this sex-specific variation are not known . It has been proposed that the regions subject to parental imprinting may display different recombination rates and therefore that sex-specific epigenetic modifications , such as imprinting , might play a role in the sex-specific distribution of recombination events [38 , 39] . On a small scale , little information on the sex-specific distribution of CO is available , especially because most information on local CO distribution comes either from the analysis of population diversity ( in human ) , which does not discriminate between sexes , or from sperm typing . Nevertheless , the high concordance observed in a few intervals between the location of the human hotspots predicted from population diversity analyses and the ones detected directly by sperm typing suggests that a large fraction of hotspots are shared by both genders . Therefore , sex-specific differences in CO distribution might be explained by differences in the activity level of the same hotspots rather than by the presence of male- and female-specific hotspots [21 , 22 , 24] . Consistent with this view , two human hotspots ( TAP2 and β-globin ) , which have been well characterized by sperm typing , have been shown also to have CO activity in female meiosis [16 , 40–43] . At the TAP2 hotspot , available data suggest that the CO frequency might be on average 20- to 40-fold higher in female than in male meiosis [41] . Similarly , there is no evidence for sex-specific activity at the few mouse CO hotspots that have been analyzed in detail by pedigree , with the exception of the hotspot located near the 3′ end of the Psmb9 gene ( previously Lmp2 ) in the class II region of mouse MHC , defined as the Psmb9 hotspot [19 , 23 , 44 , 45] . Several unique properties of the Psmb9 hotspot indicate that this region is an interesting model for studying the control of meiotic recombination and understanding the mechanisms involved ( e . g . , initiation and DSB repair ) . The formation of a high rate of CO at Psmb9 requires the presence of either the wm7 MHC haplotype , derived from Mus musculus molossinus , or the CAS3 MHC haplotype , derived from M . m . castaneus [46 , 47] . The hotspot appears to be female specific in mice carrying the wm7 haplotype but is active in both sexes carrying the CAS3 haplotype [46] . However , recombinant mice carrying a shorter wm7 fragment display a high CO rate in both sexes , demonstrating that the genetic element responsible for the lower CO rate in males is physically distinct from the element at the origin of the high CO rate at this hotspot [33] . To get a better description of the recombination events at the Psmb9 hotspot and gain insights into the factors controlling its activity , we adapted a PCR-based method recently developed for the direct molecular detection and analysis of both CO and NCO recombination products [13 , 14] . We extended this method for the detection , quantification , and analysis of recombination products in both male and female germ lines . This method , which provides a sensitivity several orders of magnitude higher than pedigree analysis , allowed us to measure the recombination rate in hybrids in which no recombination can be detected from pedigrees , and therefore to address the questions of both the strain and sex-specificities of recombination activity at Psmb9 hotspot . Moreover , the analysis of the distribution of exchange points among CO allowed us to infer initiation activity on each homolog within hybrid strains and thus to reveal a complex regulation of recombination initiation at Psmb9 , involving the interaction of elements acting in cis and in trans .
The hybrids used here for analyzing recombination at Psmb9 have been obtained by crosses between lines that were all congenic to C57BL/10 ( abbreviated as B10 ) . The CO hotspot located near the 3′ end of Psmb9 was identified first in hybrids between a laboratory strain and the strain B10 . MOL-SGR ( H-2wm7 ) ( abbreviated as SGR ) . This strain contains a fragment of Chromosome 17 covering the MHC derived from a wild mouse M . m . molossinus ( wm7 haplotype ) . The wm7 fragment covers an interval including H-2K and H-2D in the MHC , but its extent outside this 300-kb region had not been determined [46 , 48] . We used microsatellite markers to map this wm7 fragment . It extends well beyond the MHC , covering approximately the proximal half of Chromosome 17 ( Figure 1A; Table S1 ) . The most proximal and distal markers to be included are D17Mit164 ( 4 . 1 cM ) , the closest marker to the centromere analyzed , and D17Mit35 ( 23 . 50 cM ) , respectively . The lines used for the various crosses analyzed in the present study were B10 , B10 . A , SGR , and B10 . A ( R209 ) ( abbreviated as R209 ) , which differed from each other by their MHC haplotype ( Figure 1A ) . B10 . A contains a fragment derived from strain A . SGR contains the fragment of wm7 haplotype described above . R209 is a recombinant line issued from a CO between SGR and B10 . A at the Psmb9 hotspot [46] . The interval proximal to the Psmb9 hotspot is identical to that of SGR , while the distal side is identical to that of B10 . A . The breakpoint occurred at the center of the hotspot , between markers 38 and 70 ( Figure 1B ) . Thereafter , we designated each Chromosome 17 in hybrids resulting from the cross between two strains by the name of the strain that it comes from . The genetic analysis performed by Shiroishi et al . [33] led to two major conclusions: First , a high frequency of CO ( 1%–2% ) is found at the Psmb9 hotspot in hybrids carrying the wm7 haplotype ( B10 × SGR , B10 . A × SGR , and B10 × R209 ) ; second , the high CO activity is specific to female meiosis in hybrids with SGR , but present in both sexes in hybrids with R209 . To get precise evaluation and comparison of the Psmb9 hotspot activity in both male and female meioses in these various hybrids , we measured CO by direct detection of recombinant molecules in sperm and ovaries . In addition , we set to determine whether the variation in CO rates is specific to the CO pathway or not , by measuring the frequency of NCO products as well . Our previous analyses allowed us to determine the interval where most CO occurred and to define the region of initiation where high frequencies of NCO could be detected [13 , 14] . First , no CO was detected among 8 × 105 sperm in a hybrid without the wm7 haplotype ( B10 × B10 . A ) , demonstrating that the rate of CO in the 3-kb interval covered by our assay is lower than the genome average of 0 . 5 cM/Mb ( Table 1 ) . In contrast , COs were detected with high rates in both male and female germ lines of every hybrid containing the wm7 haplotype ( 0 . 3%–2 . 0%; Table 1 ) . Therefore , the presence of the wm7 haplotype increases CO frequencies at Psmb9 by 2 , 000-fold or more . In oocytes , CO frequencies were 2 . 0% ± 0 . 4% in B10 × R209 , and 1 . 1%–1 . 2% ± 0 . 3% in B10 × SGR and B10 . A × SGR . These values are consistent with previous genetic data ( 1 . 3%–5 . 4% and 1 . 1%–3 . 3% for R209 × B10 and SGR × B10 or SGR × B10 . A , respectively [33] ) and thus validate our method for measuring recombination frequencies in oocytes . In hybrids carrying the SGR chromosome ( B10 . A × SGR and B10 × SGR ) , the CO frequency was about 4-fold lower in male than in female meiosis ( Table 1 ) . NCO frequencies were measured at the BsrFI polymorphic site , located close to the center of the hotspot , in most hybrids or at a nearby marker ( marker 38 ) in the B10 × B10 . A hybrid , which is homozygous at BsrFI ( Figure 1B ) . As for CO , we have not detected any NCO product in B10 × B10 . A sperm DNA ( among 22 , 000 genomes ) , therefore indicating the absence of any recombination activity . In all other hybrids ( B10 × R209 , B10 × SGR , and B10 . A × SGR ) , NCO were detected at frequencies varying from 0 . 06% to 1 . 3% ( Table 1 ) . This variation parallels that observed for CO . Indeed , like CO , the highest rates of NCO were observed in B10 × R209 males and females and in females of B10 × SGR and B10 . A × SGR ( 0 . 27%–1 . 3% ) . In addition , lower , but still detectable , NCO rates were observed in males of these two last hybrids ( 0 . 06% ) . The comparison between CO and NCO frequencies could also be more accurately evaluated by measuring both events in parallel on the same DNA pools , thus allowing a direct determination of the CO:NCO ratio . These ratios are similar between the three hybrids ( Table 1 , column CO:NCO ) . In every hybrid where the activity of the Psmb9 hotspot has been detected , the wm7 haplotype was at the heterozygous state in the interval from the centromere to Psmb9 . Whether the presence of the wm7 haplotype at the homozygous state in this interval would also be able to induce meiotic recombination at the Psmb9 hotspot was unknown . To answer this question , we measured the recombination frequency in sperm from an R209 × SGR hybrid , which carries the wm7 haplotype in the interval from the centromere to Psmb9 on both homologs ( Figure 1A ) . This hybrid is homozygous for half the interval where exchanges occur , and therefore only a fraction of CO could be detected , those with an exchange point distal to marker 87 ( Figure 1B ) . In addition , the primers cannot discriminate between CO and NCO products with coconversion of markers 70 and 87 , which are only 17 bp apart from each other . As in other hybrids containing either R209 or SGR chromosome , we detected a high level of recombination in this R209 × SGR ( 0 . 29% ± 0 . 12%; Table 1 ) . This demonstrates that the enhancement of recombination at Psmb9 is an intrinsic property of the wm7 haplotype , independent from the heterozygosity in the region to the left of marker 70 . We examined in detail the distribution of CO exchange points in B10 × R209 and B10 . A × SGR . Most of the main properties of the distribution of CO along the hotspot are conserved between these hybrids in both sexes ( Figure 2 ) : Exchanges are distributed over 2 . 5 kb ( 90% of exchanges over 1 . 2 kb ) , centered on the 210-bp BsrFI-StyI interval , with densities decreasing progressively on both sides of the hotspot . However , B10 × R209 and B10 . A × SGR displayed striking differences in the distribution of exchanges . In B10 × R209 , the highest density of exchanges lies at the center of the hotspot , with a gradient of decreasing densities on both sides . In contrast , in B10 . A × SGR , the region at the center of the hotspot ( interval 70-StyI ) displayed a 2- to 5-fold lower exchange density than the surrounding intervals ( Figure 2 ) . Mechanisms that could explain these observations are discussed below . Following the current models for meiotic recombination , the sequences surrounding the initiating DSBs are converted in both NCO and CO products ( Figure 3 ) . Therefore , the initiation activity can be evaluated on one and the other homologous chromosome in a given hybrid by measuring the conversion frequencies in the corresponding directions . For NCO , this is achieved by measuring the NCO frequency on each homolog . For CO , the conversion tracts cannot be detected directly . However , we took advantage of the fact that the exchange points of the two reciprocal products of a CO event are located on opposite sides of the conversion tract ( Figure 3 ) . Consequently , if initiation is more frequent on one homolog than on the other , the distribution of exchange points for CO products in each orientation are expected to be shifted relatively to each other in a direction depending upon which homolog initiates more frequently ( Figure 3 and [31] ) . Associated with this shift , a transmission bias favoring the allele of the chromosome with a lower initiation rate is expected for the markers closest to the center of the hotspot . Conversely , if initiation occurs at equivalent frequencies on both homologs , exchange points for CO products in both orientations are expected to display a similar distribution , and no transmission bias should be observed . We first compared NCO frequencies on one and the other homolog in B10 × R209; they were undistinguishable ( Table 2 ) . Then , we examined the distribution of reciprocal exchanges in the same hybrid . Distributions of exchanges in one orientation ( i . e . , B10–R209 ) and the other ( R209–B10 ) were similar ( χ2 , p > 0 . 2 in both sexes ) , each of them following the overall distribution of CO ( Figure 2 ) . Together , these observations suggested that recombination events giving rise to both NCO and CO are initiated at the same frequency on R209 and B10 Chromosomes . The presence of events initiated on the B10 Chromosome demonstrated that initiation at the Psmb9 hotspot is activated in trans by a wm7-specific element . In contrast , in B10 . A × SGR , NCO were at least ten times more frequent on B10 . A chromosome than on the SGR chromosome ( Table 2 ) . Moreover , the distribution of exchanges in each orientation was different , with B10 . A-SGR exchanges being displaced to the left and SGR-B10 . A exchanges to the right ( Figures 2 and 4A ) . As explained above , an explanation for these distributions is that most COs result from events initiated on the B10 . A chromosome . This asymmetry , observed in both sexes , is accompanied by the overtransmission of the SGR alleles among exchange molecules for markers located close to the center of the hot spot , reaching 93% in male and 88% in female for the marker 70 ( Figure 4B ) . Similar results were obtained in the B10 × SGR hybrid ( Table 2 and unpublished data ) . Together , these observations suggested that initiation is at least ten times less frequent on the SGR chromosome than on a non-SGR chromosome ( i . e . , B10 . A or B10 ) . In addition to giving insight on the initiating chromosome , the extent of the shift between the distributions of CO in each orientation also provides a minimal estimate of the average length of the associated conversion tracts . The curves of cumulative frequencies for B10 . A × SGR hybrids indicated that conversion tracts associated with CO were approximately 500 bp long , in both sexes ( Figure 4 ) . The numerous recombinant products recovered by our method allowed us to do a fine-scale analysis of the distributions of CO between male and female in the B10 . A × SGR hybrid , which has the highest density of polymorphisms around the center of the hotspot ( Figure 1 ) . The curves of cumulative frequencies revealed a shift of about 100 bp to the left for the distribution of exchanges in female compared to male ( Figures 4A and 5 ) . In this hybrid , in which the distributions of CO in each orientation were different , this 100-bp shift was observed for both orientations ( Figure 4A ) . A similar shift of CO distribution in female relatively to male was observed in B10 × R209 ( Figure 2 ) . These differences between male and female CO distributions were statistically significant in both hybrids ( χ2 , p < 0 . 05 ) .
In principle , a variation in CO frequency could result either from a change of the DSB frequency or from a change of the proportion of DSBs repaired towards CO rather than NCO . The parallel analysis of CO and NCO in the various hybrids and sexes analyzed showed that the variations of CO and NCO rates were largely correlated . In particular , the 10-fold reduction of frequencies of CO initiated on the SGR chromosome is correlated with a similar reduction of NCO frequencies ( see Results and Table 2 ) . These observations indicate that most of the variation in CO frequencies measured at Psmb9 hotspot results from the variation in the initiation rate . The presence of the wm7 haplotype is required for the formation of recombinant products at the Psmb9 hotspot . Indeed , neither CO nor NCO has been detected in sperm from the B10 × B10 . A hybrid , which lacks the wm7 haplotype . The absence of CO in 8 × 105 sperm from the B10 × B10 . A hybrid indicates that the density of residual COs in the 3-kb interval amplified in our assay , if there are any , is less than 0 . 2 cM/Mb . Sperm-typing analyses of one mouse hotspot ( Eb ) and 16 human hotspots evidenced a moderate variation in CO frequencies , up to 76-fold , but there is only one documented example of a mammalian CO hotspot that is active in some individuals and inactive in others [30 , 49] . Therefore , the increase by more than 2 , 000-fold of the CO frequency at Psmb9 hotspot induced by the presence of the wm7 haplotype is by far the largest genetically controlled variation at a mammalian hotspot to date ( Table 1 , compare B10 × R209 with B10 × B10 . A ) . The wm7 haplotype-specific element that activates recombination at Psmb9 has several unusual properties , summarized in Figure 5 ( element 1 ) . First , the mapping of CO breakpoints ( Figure 2 ) together with the detection of NCO on both homologous chromosomes ( Table 2 ) demonstrate that recombination initiation is activated in trans , both on the wm7 chromosome and on the homologous , non-wm7 chromosome . Second , the hotspot activator is physically distinct from the interval where exchanges actually occur , as noted by Shiroishi et al . [33] . By producing and analyzing an additional recombinant line , we confirmed that this element is located outside the hotspot ( F . Baudat and B . de Massy , unpublished data ) . Third , the following observations indicate that the increase of recombination activity is specific to one ( Psmb9 ) or a limited number of loci . There is no global genome-wide or even chromosome-wide increase of CO , as evidenced by the normal number of Mlh1 foci on pachytene spermatocytes ( C . Grey and B . de Massy , unpublished data ) . Only little differences outside the Psmb9 hotspot have been found by pedigree analysis of the whole MHC between the hybrids that carry the wm7 haplotype and the ones that do not [46] . Moreover , a classical genetic mapping study failed to show any increase of the genetic length along the proximal half of Chromosome 17 in B10 × R209 hybrids , except for the interval containing Psmb9 ( B . de Massy , unpublished data ) . The finding of a haplotype-specific element activating recombination at a specific locus raises the question of what is the mechanism involved in this process . The size of the recombination activating element is not known . The specificity of the target ( the Psmb9 hotspot ) suggests that a specific interaction , either direct or indirect , between the recombination activator and the locus of the hotspot is involved . Several hypotheses could be proposed . One is that the activator is a gene , coding for a factor interacting with the Psmb9 hotspot . There are several examples in which factors , such as transcription or chromatin-modifying factors , are required for detectable levels of recombination initiation at specific yeast hotspots ( for example , see [50 , 51] ) . An alternative mechanism involves a long-distance interaction between the activator ( of wm7 haplotype ) and the hotspot . Cases of long-distance locus-to-locus associations have already been reported , such as between Igf2/H19 and Wsb1/Nf1 , or between the H enhancer and one olfactory receptor gene promoter in olfactory sensory neurons [52 , 53] . A gene conversion bias favoring the alleles from the SGR chromosome is observed for both NCO and CO products ( Figure 4B; Table 2 ) . This could in theory be explained either by a bias in initiation or a bias during the mismatch repair of the heteroduplex DNA formed in recombination intermediates . However , we consider the last hypothesis is unlikely , because biased mismatch repair leading to restauration would be expected to increase the frequency of exchange points near initiation , a situation that we did not observe ( Figure 2 ) . Therefore , our data show that the rate of initiation is at least 10-fold lower on the SGR chromosome than on the homologous non-SGR chromosome . This suggests strongly that a cis-acting repressing element is present on the SGR chromosome . One could note that the repression of initiation was identical whether the SGR chromosome has been transmitted to the hybrid by its mother or father , eliminating the hypothesis that this cis-effect results from a mark dependent upon the sex of the parent having transmitted the chromosome ( unpublished data ) . The element at the origin of the repression of recombination initiation on the SGR chromosome should be located in the interval differing between SGR and R209 , which includes the marker 70 at the center of the hotspot and extends up to at least D17Mit35 , 11 Mb centromere distal to Psmb9 , ( Figure 1; Table S1 ) . The two SNPs , 70 and 87 , being located in the predicted region of initiation ( Figure 1 ) , are interesting candidates . There are already two documented cases ( the human hotspots DNA2 and NID1 ) where a particular allele for a single SNP located at the hotspot center correlates with the repression in cis of recombination initiation [31 , 32] . The activity of the Psmb9 hotspot , evaluated by pedigree analysis , has been reported previously to be female specific in hybrids carrying the SGR chromosome [33] . Our more sensitive assay demonstrates that this is not the case , but reveals a moderate decrease of 4- to 7-fold in male recombination rate compared to female ( Table 1 ) . Interestingly , this male-specific effect of the SGR chromosome involves recombination products resulting from initiation on the non-SGR chromosome , indicating a regulation in trans . Like the cis effect described above , this SGR-specific regulation could be due to markers 70 and/or 87 or other SGR-specific elements in the interval from marker 70 to D17Mit35 . The lowering of recombination rate in males could result either from an effect on the level of initiation or on the processing of recombination intermediates . Two types of sex-specific mechanisms could be invoked , either related to the presence of a diffusible factor or to a specific interaction between homologs . Consistent with the last hypothesis , interactions in trans between homologous chromosomes have been shown to modulate the frequency of recombination at S . cerevisiae hotspots [54 , 55] . Perhaps reflecting a similar process , a sperm typing study at the mouse Eb hotspot showed that the rate of CO resulting from initiation on the chromosome of a particular haplotype ( haplotype s ) is modified depending on the haplotype of the homologous chromosome [49] . Whatever the molecular mechanism is , this effect might be another manifestation of the mechanism that is responsible for the repression in cis of recombination initiated on the SGR chromosome , described above . In addition to the determinants controlling the rate of recombination , strain- and sex-specific differences have also been detected in the distribution of recombination products along the sequence of the hotspot . The overall distribution of CO at the Psmb9 hotspot in B10 × R209 is similar to the other mouse and human hotspots that have been characterized so far . In particular , the CO density peaks at the center of the hotspot and decreases progressively on both sides over a few hundreds bases [11–13] . Quite differently , in B10 . A × SGR , the CO density is higher on both sides of the hotspot than in a 140–300-bp central interval where it is about 4-fold lower ( Figure 2 ) . However , NCO frequencies are higher at markers located in the BsrFI-StyI interval ( markers 38 , 70 , and 87 ) than at BsrFI and StyI , suggesting that most of the initiation occurs inside this interval ( F . Baudat and B . de Massy , unpublished data ) . Therefore , the observation that most CO exchanges points are outside the BsrFI-StyI interval suggests that the gene conversion tracts associated with CO are bidirectional , extending on both sides of the initiating DSBs with a minimal length of about 300 bp overall . We found that two types of hypotheses ( not mutually exclusive ) could explain this difference between the B10 . A × SGR and B10 × R209 hybrids . With the first hypothesis , initiating DSBs would be distributed on a shorter interval in B10 . A × SGR than in B10 × R209 . Alternatively , a difference in the processing of recombination intermediates might result in longer gene conversion tracts in B10 . A × SGR than in B10 × R209 . This could be due to a higher density of heterozygosity near the initiation site . A similar situation has been observed in one individual at the human DPA1 hotspot , where the density of CO breakpoint was lower at the center of the hotspot , in an interval with a particularly high density of heterozygous SNPs , than in the neighboring intervals on both sides ( Figure 4A in [21] ) . In addition , the shift by about 100 bp to the left in female compared to male , observed in both B10 . A × SGR and B10 × R209 , suggests that the location of the initiating DSBs can vary independently from the DNA sequence ( Figures 2 and 4A ) . In B10 . A × SGR this shift is observed independently for exchanges in each orientation and therefore involves both sides of the conversion tracts ( Figure 4A ) . We therefore consider the possibility of a difference in the processing of recombination intermediates unlikely , as it would require the simultaneous shortening and lengthening of conversion tracts on each side of initiation . Therefore , the simplest explanation is a difference in the location of DSBs , which would then be predicted to be shifted to the left by about 100 bp in female meiosis . A possibility to account for a difference in the localization of initiating lesions is a difference in chromatin organization between spermatocytes and oocytes , such that the accessible region to the recombination initiation machinery is not exactly at the same position in both sexes . The so-called hotspot paradox refers to the fact that hotspots are maintained in genomes despite the evolutionary force that tends to eliminate them [56–59] . Indeed , in the current models for meiotic recombination , the region surrounding the initiating DSB is converted into the allele of the noninitiating chromosome [2 , 3] . Therefore , if the rate of DSB formation at a given hotspot is controlled in cis by elements localized in the converted interval , alleles displaying a low initiation rate are predicted to replace progressively the “hotter” alleles . This meiotic drive is expected to act against the introduction of new hotspots in the population , as well as to favor the fixation of the less active alleles and eventually the extinction of existing hotspot [56–59] . The continuous presence of recombination hotspots in genomes might therefore be ensured by a mechanism of turnover , which would compensate for the extinction of some hotspots with the birth of new ones [22] . Coop and Myers proposed that the newly arising hotspots that escape a premature elimination do not initially experience this meiotic drive [59] . This could be achieved if the elements responsible for hotspot activity are localized outside the frequently converted region and/or enhance the initiation of recombination in trans . At established hotspots , in contrast , cis-acting local alleles repressing recombination are expected to spread over the population , owing to the mechanism of meiotic drive described above . The regulation of recombination at the Psmb9 hotspot displays properties consistent with this model . First , the wm7-specific genetic element activating recombination is localized outside the hotspot and acts in trans ( Element 1 on Figure 5 ) . Therefore this element could be responsible for the existence of the Psmb9 hotspot without being affected by the mechanism of meiotic drive described above . This might also be exemplified at the human MSTM1a hotspot , of which the activity is controlled by factors other than its own sequence [22] . Second , as discussed above , the repression in cis of recombination initiation on the SGR chromosome might be controlled by one or two SNPs localized at the center of the hotspot ( markers 70 and 87 ) . The putative repressive alleles ( from the SGR chromosome ) represent the derived state of these two markers , which is consistent with the idea that the spreading of such repressing alleles in a population is favored in the presence of the active hotspot ( comparison with the sequence of Rattus norvegicus from Ensembl , RGSC3 . 4 of December 2004 , http://www . ensembl . org/Rattus_norvegicus/index . html ) . Interestingly , at DNA2 and NID1 human hotspots , the repression also occurs specifically on chromosomes carrying the derived allele for a SNP located at the center of the hotspot [32] . Our data and other studies on recombination hotspots thus reveal the various levels of hotspot control that could be either DNA sequence dependent or not and that are able to act locally or at distance . These controls are indeed consistent with the observation that a communication has to operate somehow along each chromosome arm to produce , among other things , at least one CO per chromosome arm . Given the current explosion of hotspots identified in mammals [60] , understanding their control elements are significant challenges for the future .
The mouse lines used in this study were C57BL/10JCrl ( purchased from Charles River Laboratories , http://www . criver . com ) , B10 . A ( purchased from The Jackson Laboratory , http://www . jax . org ) , and B10 . MOL-SGR and B10 . A ( R209 ) ( from T . Shiroishi , National Institute of Genetics , Mishima , Japan ) . B10 . MOL-SGR was established by repeated backcrosses ( 13 generations ) to the C57BL/10J background [61] . The strategy of the PCR-based method developed for the direct molecular detection and analysis of recombination products in male and female germ lines has been described in detail in Guillon et al . ( Figure S1 ) [14] . The primers used for the detection of CO and NCO in the various hybrids are listed in Tables S2 and S3 . The method was adapted for estimating the absolute frequencies of recombination products in oocytes . A cell suspension was obtained by EDTA collection of the pooled ovaries from a litter of new-born mice , according to [62] , modified as follows . Ovaries were incubated in 1 mM EDTA in PBS at room temperature for 30 min , after which cells were released by pricking the gonads with fine needles in 0 . 05% BSA in PBS . The cells were collected in 500 μl 0 . 05% BSA in PBS . The proportion of oocytes was determined on a 25-μl aliquot of each cell suspension by immunofluorescence with antibodies recognizing germ cell-specific antigens , GCNA1 ( gift from G . Enders , University of Kansas , Kansas , United States of America ) and SYCP3 ( prepared in guinea-pig against a mouse SYCP3 oligopeptide ) ( Figure S2 ) . DNA was extracted from the remainder of each cell suspension and used to detect recombinant products . Estimates of recombinant frequencies were eventually corrected for the proportion of oocytes in each suspension , assuming that all detected recombinant events occurred in oocytes and that the genomic content of oocytes is 4n versus 2n for most somatic cells . The primers used in the various hybrids are shown in Figure 1 and Table S2 . Frequencies of recombination events were given by the ratio between the mean number of events per pool and the number of amplifiable genomes , both calculated according to the Poisson law . For estimating both the concentration of amplifiable molecules and the number of recombinant products , dilutions were chosen such that the proportion of negative pools was comprised between 0 . 2 and 0 . 8 . The standard deviation was estimated using the normal approximation of the Poisson distribution , and 95% confidence intervals were calculated as the estimate of the recombination frequency plus or minus 1 . 96 standard deviation . For comparing two frequencies , the difference between the frequencies to be compared was estimated , with its standard deviation . The statistical significance of the observed difference was then evaluated ( at the 5% significance level ) by examining if the value of 0 was enclosed in the interval made by the estimate of the difference plus or minus 1 . 96 standard deviation [63] . When the frequency of recombinant products was too low , or when no recombinant product was detected , the 95% confidence interval was calculated using the Poisson approximation for the binomial distribution of the recombinant product number over the estimate of the total number of amplifiable molecules . CO were detected either by a direct selection involving two rounds of allele-specific PCR or by the assay of parallel detection of CO and NCO described in [14] . The CO:NCO ratio was the ratio between the mean number of CO and the mean number of NCO estimated in the same series of pools in the parallel assay . For every hybrid , DNA extracts from at least two mice ( males ) or two litters ( females ) have been analyzed independently , with the exception of B10 × SGR males for which only one individual has been analyzed . CO frequencies measured in different individuals of identical genotype were not significantly different ( p > 0 . 1 ) , with the exception of B10 × SGR females , for which DNA extracts from three litters gave CO frequencies of 0 . 6 ± 0 . 2% , 1 . 1 ± 0 . 6% , and 1 . 6 ± 0 . 8% , the two extreme values being significantly different from each other ( 0 . 02 < p < 0 . 05 ) . Nevertheless , all three CO frequencies were significantly higher than the male CO frequency when analyzed separately and were pooled for generating the data presented in this study . The intervals where exchanges occurred were mapped with restriction site polymorphisms or by sequencing of the secondary PCR products . The number of events detected in each interval was corrected according to the Poisson distribution as described in [64] . The distributions of exchange points were compared by performing a χ2 test on the numbers of exchange points in the various intervals . The sequences of the primers used for mapping the markers listed in the Table S1 have been found on the Mouse Genome Informatics site ( http://www . informatics . jax . org ) . The PCR cycling conditions were 94 °C for 10 s , 55 °C for 30 s , and 72 °C for 30 s for 36 cycles .
|
In most sexually reproducing species , during meiosis a high level of recombination between homologous chromosomes is induced . These events are not evenly distributed in the genome but clustered in small regions called hotspots . The genetic factors controlling their activity in mammals are still poorly understood . We have performed experiments to identify factors that influence the recombination activity of a hotspot in the mouse genome . By detecting the recombination products by a PCR-based method , we show that the variation of hotspot activity ( up to 2 , 000-fold ) is mainly due to differences of initiation frequencies , rather than differences at later steps of recombination . In addition , we identify several levels of controls . First , the initiation of recombination is activated by a haplotype-specific element , localized outside the hotspot and acting in trans ( when heterozygous , this element allows for recombination initiation on both homologous chromosomes ) . This suggests a unique type of regulation requiring the presence of a diffusible factor and/or of communications between homologous chromosomes before recombination . A second element represses the recombination initiation in cis , which might indicate the influence of local polymorphisms affecting initiation events . Our results provide the first functional analysis of the control of recombination initiation sites for meiotic recombination in mammals .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"mus",
"(mouse)",
"molecular",
"biology",
"developmental",
"biology"
] |
2007
|
Cis- and Trans-Acting Elements Regulate the Mouse Psmb9 Meiotic Recombination Hotspot
|
Large-scale adaptive radiations might explain the runaway success of a minority of extant vertebrate clades . This hypothesis predicts , among other things , rapid rates of morphological evolution during the early history of major groups , as lineages invade disparate ecological niches . However , few studies of adaptive radiation have included deep time data , so the links between extant diversity and major extinct radiations are unclear . The intensively studied Mesozoic dinosaur record provides a model system for such investigation , representing an ecologically diverse group that dominated terrestrial ecosystems for 170 million years . Furthermore , with 10 , 000 species , extant dinosaurs ( birds ) are the most speciose living tetrapod clade . We assembled composite trees of 614–622 Mesozoic dinosaurs/birds , and a comprehensive body mass dataset using the scaling relationship of limb bone robustness . Maximum-likelihood modelling and the node height test reveal rapid evolutionary rates and a predominance of rapid shifts among size classes in early ( Triassic ) dinosaurs . This indicates an early burst niche-filling pattern and contrasts with previous studies that favoured gradualistic rates . Subsequently , rates declined in most lineages , which rarely exploited new ecological niches . However , feathered maniraptoran dinosaurs ( including Mesozoic birds ) sustained rapid evolution from at least the Middle Jurassic , suggesting that these taxa evaded the effects of niche saturation . This indicates that a long evolutionary history of continuing ecological innovation paved the way for a second great radiation of dinosaurs , in birds . We therefore demonstrate links between the predominantly extinct deep time adaptive radiation of non-avian dinosaurs and the phenomenal diversification of birds , via continuing rapid rates of evolution along the phylogenetic stem lineage . This raises the possibility that the uneven distribution of biodiversity results not just from large-scale extrapolation of the process of adaptive radiation in a few extant clades , but also from the maintenance of evolvability on vast time scales across the history of life , in key lineages .
Much of extant biodiversity may have arisen from a small number of adaptive radiations occurring on large spatiotemporal scales [1]–[3] . Under the niche-filling model of adaptive radiation , ecological opportunities arise from key innovations , the extinction of competitors , or geographic dispersal [1] , [4] , [5] . These cause rapid evolutionary rates in ecologically relevant traits , as diverging lineages exploit distinct resources . Rates of trait evolution then decelerate as niches become saturated , a pattern that has been formalised as the “early burst” model ( e . g . , [6] , [7] ) . Most phylogenetic studies of adaptive radiations focus on small scales such as island radiations and other recently diverging clades , including Anolis lizards , cichlid fishes , and geospizine finches [2] , [6] , [8]–[10] . Detailed study of these model systems has demonstrated the importance of ecological and functional divergence as drivers of speciation early in adaptive radiations ( e . g . , [11] , [12] ) . Surprisingly though , early burst patterns of trait evolution receive only limited support from model comparison approaches for these and other adaptive radiations occurring in geographically restricted areas and on short timescales ( <50 million years [Ma]; most <10 Ma ) [6] ( but see [13] , [14] ) . Studies of morphological evolution on longer timescales , unfolding over 100 Ma or more , are central to establishing whether niche-filling or early burst patterns of trait evolution are important evolutionary phenomena on large phylogenetic scales . A small number of recent studies quantified patterns of trait evolution on large scales using neontological phylogenies . For example , diversification rates and morphological rates are positively correlated in actinopterygians [15] ( ∼400 Ma ) ; rapid rates of both morphological and molecular evolution occur on deep , Cambrian , nodes of the arthropod tree of life [16] ( ∼540 Ma ) ; and the early evolution of placental mammals was characterised by rapid rates of diversification [17] ( 100–65 Ma ) and perhaps body size evolution [18] ( but see [19] ) . However , even the largest neontological studies [15]–[18] , [20] , [21] are limited to explaining the rise of important extant groups . A more complete characterisation of macroevolutionary processes on long timescales should also explain the ascent and demise of important extinct groups ( e . g . , [22] ) , which in fact represent most of life's diversity . Substantial evidence for the dynamics of past adaptive radiations might have been erased from the neontological archive , and macroevolutionary models for extinct or declining/depauperate clades may be tested most effectively using deep time data from the fossil record [23] , [24] . Palaeontologists often quantify patterns of morphological radiation using time series of disparity ( e . g . , [25] , [26] ) . However , few phylogenetic studies including fossil data have attempted to explain patterns of morphological radiation in large clades on timescales >100 Ma , and most have individually targeted either the roots of exceptional modern clades such as birds or mammals ( e . g . , [19] , [27] , [28] ) or extinct/depauperate clades ( e . g . , [29]–[31]; studies based on discrete characters ) . Thus , patterns of morphological evolution in major extinct clades , and their links to successful modern clades , are not well understood . Non-avian dinosaurs are an iconic group of terrestrial animals . They were abundant and ecologically diverse for most of the Mesozoic , and included extremely large-bodied taxa that challenge our understanding of size limits in terrestrial animals [32] . The first dinosaurs appeared more than 230 Ma ago in the Triassic Period , as small-bodied ( 10–60 kg ) , bipedal , generalists . By the Early Jurassic ( circa 200 Ma ) , they dominated terrestrial ecosystems in terms of species richness [33] , [34] , and Cretaceous dinosaurs ( 145–66 Ma ) had body masses spanning more than seven orders of magnitude ( Figure 1A ) . Non-avian dinosaurs became extinct at the catastrophic Cretaceous/Paleogene ( K/Pg ) boundary event , at or near the peak of their diversity [35] , [36] . In contrast , extant dinosaurs ( neornithine birds ) comprise around 10 , 000 species and result from one of the most important large-scale adaptive radiations of the Cenozoic [3] , [21] . The proposed drivers of early dinosaur diversification are controversial . Although various causal factors have been suggested to underlie a presumed adaptive radiation , few studies have tested the predictions of niche-filling models , and these have yielded equivocal results . An upright , bipedal gait , rapid growth , and possible endothermy have been proposed as key innovations of Triassic dinosaurs ( reviewed by [34] ) , and mass extinctions during the Triassic/Jurassic boundary interval removed competing clades , perhaps leading to ecological release and rapid rates of body size evolution in Early Jurassic dinosaurs [37] ( but see [34] ) . However , quantitative studies using body size proxies [34] and discrete morphological characters [33] have found only weak support for the niche-filling model during early dinosaur evolution , instead favouring gradualistic evolutionary rates . These studies focussed on the Late Triassic–Early Jurassic , so it is unclear whether Early Jurassic dinosaur evolution differed from later intervals ( consistent with radiation following a mass extinction ) , or how the Middle Jurassic–Cretaceous radiation of birds and their proximate relatives relates to overall patterns of dinosaur diversification . We used phylogenetic comparative methods [6] , [14] , [38] , [39] to analyse rates of dinosaur body mass evolution ( Materials and Methods; Appendix S1 ) . For this study , we compiled a large dataset of dinosaur body masses ( 441 taxa; Dataset S1 ) using the accurate scaling relationship of limb robustness ( shaft circumference ) derived from extant tetrapods [40] ( Appendix S1; Dataset S1 ) . Body mass affects all aspects of organismal biology and ecology ( e . g . , [41] , [42] ) , including that of dinosaurs ( e . g . , [43]–[45] ) . Because of its relationship with animal energetics and first-order ecology , understanding the evolution of body mass is fundamental to identifying the macroevolutionary processes underlying biodiversity seen in both ancient and modern biotas . Therefore , by studying body mass evolution , we assess the broad pattern of niche filling in the assembly of dinosaur diversity through 170 Ma of the Mesozoic . In many hypotheses of adaptive radiation , ecological speciation is an important process generating both morphological and taxonomic diversity ( e . g . , [2]; but see [46] ) , according to which ecological differentiation is essentially simultaneous with lineage splitting [12] . In consequence , many large-scale studies of adaptive radiation have focussed on diversification rates ( e . g . , [17] , [21] , [47] ) . A correlation between diversification rates and morphological rates is consistent with adaptive radiation ( e . g . , [15] ) . However , even when this can be demonstrated , the occurrence of ecological speciation is difficult ( perhaps impossible ) to test in clades even only a few Ma old [48] . Methods for estimating diversification rates on non-ultrametric trees ( e . g . , those including deep time data ) have recently become available [49] . However , these methods require accurate estimates of sampling probability during discrete time intervals , and it is not clear that it is possible to obtain such estimates from the dinosaur fossil record , which contains many taxa known only from single occurrences . Therefore , our study focuses on the predictions of niche-filling models of morphological evolution during adaptive radiation , as done in some previous studies ( e . g . , [6] , [13] ) .
Most of the earliest dinosaurs weighed 10–35 kg ( Figure 1 ) ; Herrerasaurus was exceptionally large at 260 kg . Maximum body masses increased rapidly to 1 , 000–10 , 000 kg in sauropodomorphs , with especially high masses in early sauropods such as Antetonitrus ( 5 , 600 kg; Norian , Late Triassic ) and Vulcanodon ( 9 , 800 kg; Early Jurassic ) , whereas minimum body masses of 1–4 kg were attained by Late Triassic ornithischians and theropods ( Figure 1 ) . Jurassic Heterodontosauridae ( ∼0 . 7 kg [50] ) , Middle Jurassic and younger Paraves ( e . g . , Epidexipteryx , 0 . 4 kg; Anchiornis , 0 . 7 kg ) , and Cretaceous Avialae ( birds: 13–16 g to 190 kg [51] ) extended this lower body size limit ( Table 1 ) . Archaeopteryx weighed 0 . 99 kg ( the largest , subadult specimen [52] ) and the Cretaceous sauropod Argentinosaurus weighed approximately 90 , 000 kg ( Table 1 ) . Our full set of mass estimates is available in Dataset S1 and a summary is presented in Table 1 . Our node height tests indicate that evolutionary rate estimates at phylogenetic nodes ( standardised phylogenetically independent contrasts [39] ) vary inversely with log-transformed stratigraphic age for most phylogenies ( Figure 2 ) . This relationship is significant ( based on robust regression [14] , [53] ) for most phylogenies of non-maniraptoran dinosaurs , and for ornithischians and non-maniraptoran theropods when analysed separately ( Figure 2B ) . This result is weakened , and becomes non-significant , when Triassic nodes are excluded ( Figure S1 ) . Declining evolutionary rates through time are not found in any analyses including maniraptorans . Indeed , when maniraptorans are added to analyses of Dinosauria , a burst of high nodal rate estimates is evident in lowess lines spanning the Middle Jurassic–Early Cretaceous interval of maniraptoran diversification ( Figure 2A ) . Maniraptorans have a weakly positive ( non-significant ) relationship between evolutionary rates and body mass , and do not show diminishing evolutionary rates through time ( Figure 2B–C ) . This contrasts with non-maniraptoran dinosaurs , in which evolutionary rates vary inversely with body mass ( Figure 2C ) . Maximum-likelihood models [6] , [38] were fitted to phylogenies calibrated to stratigraphy using the “equal” and “mbl” ( minimum branch length ) methods ( see Materials and Methods ) , and complement the results of our node height tests in showing support for early burst models only in analyses excluding Maniraptora ( Table 2; Figure S2 ) . Note , however , that the maximum-likelihood method has less statistical power to detect early burst patterns than does the node height test when even a small number of lineages escape from the overall pattern of declining rates through time [14] . Two models that predict saturation of trait variance through a clade's history were commonly supported in our analyses: the early burst model of exponentially declining evolutionary rates through time , and the Ornstein–Uhlenbeck ( OU ) model of attraction to a “trait optimum” value . Other models ( e . g . , Brownian motion , stasis ) had negligible AICc weights in all or most ( directional trend model ) analyses ( AICc is Akaike's information criterion for finite sample sizes ) . Early burst models received high AICc weights for analyses of ornithischians , non-maniraptoran theropods , and non-maniraptoran dinosaurs when using the “equal” branch length calibration method ( Table 2; Figure S2 ) . Early burst models had comparable AICc weights to Ornstein–Uhlenbeck models for sauropodomorphs when using the “equal” branch length calibration method , and for ornithischians and non-maniraptoran theropods when using the “mbl” method . Early burst models had generally lower AICc weights for non-maniraptoran dinosaurs and for sauropodomorphs when using the “mbl” branch length calibration method ( Table 2; Figure S2 ) . Support from some phylogenies for Ornstein–Uhlenbeck models of attraction to a large body size optimum from small ancestral body sizes [54] , [55] in ornithischians [56] , non-maniraptoran theropods , and especially sauropodomorphs and non-maniraptoran dinosaurs ( Table 2; Figure S2 ) , suggests the occurrence of Cope's rule in dinosaurs . All phylogenies provide strong support for this pattern in maniraptorans ( Table 2 ) . Exceptionally high rates at individual nodes in our phylogenies were identified as down-weighted datapoints in robust regression analyses [14] , [53] . Five sets of exceptional nodes in the Triassic–Early Jurassic represent rapid evolutionary shifts from primitive masses around 10–35 kg to large body masses in derived sauropodomorphs ( >1 , 000 kg ) , armoured ornithischians ( Thyreophora; Figure 1B ) and theropods ( Herrerasaurus , and derived taxa such as Liliensternus ( 84 kg ) and Dilophosaurus ( 350 kg ) ) , and to smaller body sizes in heterodontosaurid ornithischians ( Figure 3; Table 3 ) . Rapid body size changes were rare in later ornithischians and sauropodomorphs , which each show only one exceptional Jurassic node , marking the origin of body sizes greater than 1 , 000 kg in derived iguanodontians , and of island dwarfism in the sauropod Europasaurus [57] . By contrast , up to six exceptional Jurassic nodes occur in theropod evolution , with especially high contrasts at the origins of body sizes exceeding 750 kg in Tetanurae , and marking phylogenetically nested size reductions on the line leading to birds: in Coelurosauria ( e . g . , Ornitholestes , 14 kg; Zuolong , 88 kg ) and in Paraves , which originated at very small body masses around 1 kg [58] . The contrast between theropods and other dinosaurs is even greater in the Cretaceous , when no exceptional nodes occur in Sauropodomorpha , and only two in Ornithischia: at the origins of large-bodied Ceratopsidae and island dwarf rhabdodontid iguanodontians ( e . g . , Mochlodon [59] ) . At least nine shifts occurred during the same interval of theropod evolution , including seven in maniraptorans ( Figure 3; Table 3 ) .
Patterns of dinosaur body size evolution are consistent with the niche-filling model of adaptive radiation [1] , [4] , [6] . Early dinosaurs exhibit rapid background rates of body size evolution , and a predominance of temporally rapid , order-of-magnitude shifts between body size classes in the Triassic and Early Jurassic . These shifts reflect radiation into disparate ecological niches such as bulk herbivory in large-bodied sauropodomorphs ( e . g . , [60] ) and thyreophoran ornithischians , herbivory using a complex masticating dentition in small-bodied heterodontosaurids ( e . g . , [61] , [62] ) , and increasing diversity of macropredation in large theropods ( Table 3 ) . Subsequently , rates of body size evolution decreased , suggesting saturation of coarsely defined body size niches available to dinosaurs in terrestrial ecosystems , and increasingly limited exploration of novel body size space within clades . The early burst pattern of dinosaurian body size evolution is substantially weakened when Triassic data are excluded ( Figure S1 ) . This suggests that key innovations of Triassic dinosaurs ( e . g . , [63] , [64] ) , and not the Triassic/Jurassic extinction of their competitors [37] , drove the early radiation of dinosaur body sizes [34] . Indeed , phylogenetic patterns indicate that many basic ecomorphological divergences occurred well before the Triassic/Jurassic boundary . It is not clear which innovations allowed dinosaurs to radiate [34] , or whether the pattern shown here was part of a larger archosaurian radiation [65] . However , the evolution of rapid growth rates may have been important [64] , especially in Sauropodomorpha [66] , and the erect stance of dinosaurs and some other archosaurs [34] might have been a prerequisite for body size diversification via increased efficiency/capacity for terrestrial weight support [63] . Maniraptoran theropods are an exception to the overall pattern of declining evolutionary rates through time: exhibiting numerous instances of exceptional body size shifts , maintaining rapid evolutionary rates , and generating high ecological diversity [67] , [68] , including flying taxa . Although a previous study found little evidence for directional trends of body size increase in herbivorous maniraptoran clades [69] , this does not conflict with our observation that some body size shifts in maniraptorans ( and other coelurosaurs ) coincide with the appearance of craniodental , or other , evidence for herbivory ( Table 3; e . g . , [67] , [68] , [70] ) . Much of our knowledge of Late Jurassic and Early Cretaceous maniraptorans comes from a few well-sampled Chinese Lagerstätten , such as the Jehol biota . Without information from these exceptional deposits , we would have substantially less knowledge of divergence dates and ancestral body sizes among early maniraptorans . However , this is unlikely to bias comparisons between maniraptorans and other groups of dinosaurs for two reasons: ( 1 ) these deposits provide equally good information on the existence and affinities of small-bodied taxa in other clades , such as Ornithischia; and ( 2 ) exceptional information on early maniraptoran history should bias analyses towards finding an early burst pattern in maniraptorans . Inference of high early rates in Maniraptora would be more likely , due either to concentration of short branch durations at the base of the tree ( especially using the “mbl” stratigraphic calibration method ) , or observation of additional body size diversity at the base of the tree that would remain undetected if sampling was poor . We cannot speculate as to the effects on our analyses of finding comparable Lagerstätten documenting early dinosaur history . However , there is currently little positive evidence that the general patterns of body size evolution documented here are artefactual . Many stratigraphically younger dinosaurs , especially non-maniraptorans , exhibit large body size and had slow macroevolutionary rates , possibly due to scaling of generation times ( e . g . , [71] , [72] ) . Scaling effects are observed across Dinosauria , but show substantial scatter ( non-significant; Figure 2C ) within Ornithischia and Sauropodomorpha , consistent with previous suggestions that scaling effects should be weak in dinosaurs because of the life history effects of oviparity [73] . Small dinosaurs ( 10–50 kg ) had the highest evolutionary rates , and rates attenuated only weakly , or not at all , at sizes below 10 kg ( Figure S3 ) . This might have been key to maniraptoran diversification from small-bodied ancestors , and also explains the origins of fundamentally new body plans and ecotypes from small-bodied ancestors later in ornithischian history ( Iguanodontia , Ceratopsidae; Figure 1 ) . Maniraptora includes Avialae , the only dinosaur clade to frequently break the lower body size limit around 1–3 kg seen in other dinosaurs . It is likely that more niches are available to birds ( and mammals ) around 100 g in mass [41] , [74] , so obtaining smaller body sizes might have contributed to the ecological radiation of Mesozoic birds ( e . g . , [27] , [75] ) . If the K/Pg extinction event was ecologically selective , vigorous ecological diversification may have given maniraptoran lineages a greater chance of survival: Avialae was the only dinosaurian clade to survive , perhaps because of the small body sizes of its members . Although the fossil record of birds is inadequate to test hypotheses of K/Pg extinction selectivity , it is clear that smaller-sized squamates and mammals selectively survived this event [76] , [77] . Therefore , our results suggest that rapid evolutionary rates within Maniraptora paved the way for a second great adaptive radiation of dinosaurs in the wake of the K/Pg extinction event: the diversification of neornithine birds [21] . Our findings complement recent studies of diversification rates in the avian crown group [3] , [21] , and suggest that birds , the most speciose class of tetrapods , arose from a long evolutionary history of continual ecological innovation . Our most striking finding is of sustained , rapid evolutionary rates on the line leading to birds ( i . e . , in maniraptorans ) for more than 150 Ma , from the origin of dinosaurs until at least the end of the Mesozoic . Rates of evolution declined through time in most dinosaurs . However , this early burst pattern , which characterises the niche-filling model of adaptive radiation [6] , [7] , does not adequately describe evolution on the avian stem lineage . The recovered pattern of sustained evolutionary rates , and the repeated generation of novel ecotypes , suggests a key role for the maintenance of evolvability , the capacity for organisms to evolve , in the evolutionary success of this lineage . Evolvability might have also played a central role in the evolution of other major groups such as crustaceans [78] and actinopterygians [15] , supporting its hypothesised importance in organismal evolution [79] . Rapid evolutionary rates observed during the early evolutionary history of Dinosauria , which decelerated through time in most subclades , indicate that much of the observed body size diversity of dinosaurs was generated by an early burst pattern of trait evolution . However , this pattern becomes difficult to detect when data from early dinosaurian history are not included in analyses ( Figure S1 ) , consistent with the observation that deep time data improve model inference in simulations [24] . The pruning of lineages by extinction might also overwrite the signals of ancient adaptive radiation in large neontological datasets . For example , Rabosky et al . [15] recovered slow evolutionary rates at the base of the actinopterygian tree , but the fossil record reveals substantial morphological and taxonomic diversity of extinct basal actinopterygian lineages [80] , [81] . Although it has not yet been tested quantitatively , this diversity might have resulted from early rapid rates across Actinopterygii , as observed here across Dinosauria . If our results can be generalised , they suggest that the unbalanced distribution of morphological and ecological diversity among clades results from the maintenance of rapid evolutionary rates over vast timescales in key lineages . These highly evolvable lineages may be more likely to lead to successful modern groups such as birds , whereas other lineages show declining evolutionary rates through time . Declining evolutionary rates in dinosaurian lineages off the line leading to birds indicate large-scale niche saturation . This might signal failure to keep pace with a deteriorating ( biotic ) environment ( the Red Queen hypothesis [82] , [83] ) , with fewer broad-scale ecological opportunities than those favouring the early radiation of dinosaurs . There is strong evidence for Red Queen effects on diversification patterns in Cenozoic terrestrial mammals [22] , and it is possible that a long-term failure to exploit new opportunities characterises the major extinct radiations of deep time ( and depauperate modern clades ) , whether or not it directly caused their extinctions .
We used phylogenetic comparative methods to analyse rates of dinosaur body mass evolution [6] , [14] , [38] , [39] ( Appendix S1 ) . Body mass , accompanied by qualitative observations ( Table 3 ) , was used as a general ecological descriptor . Body mass was estimated for all dinosaurs for which appropriate data were available ( 441 taxa; Dataset S1 ) using the empirical scaling relationship of limb robustness ( stylopodial circumference ) with body mass , derived from extant tetrapods [40] ( Appendix S1 ) . We analysed log10-transformed data ( excluding juveniles ) , which represent proportional changes in body mass . Stylopodial shaft circumferences are infrequently reported in the literature , so many were taken from our own measurements , or were calculated from shaft diameters ( Appendix S1 ) . Previous large datasets of dinosaurian masses were based on substantially less accurate methods , using the relationship between linear measurements ( e . g . , limb bone lengths ) and volumetric models of extinct dinosaurs ( [84]–[86]; reviewed by [40] ) . Quantitative macroevolutionary models were tested on composite trees compiled from recent , taxon-rich cladograms of major dinosaur groups ( Appendix S1; Figure S4 , Figure S5 , Figure S6 , Figure S7 ) . Phylogenetic uncertainty was reflected by analysing alternative topologies and randomly resolved polytomies ( Appendix S1 ) . Tip heights and branch durations were stratigraphically calibrated , and zero-length branches were “smoothed” using two methods: ( 1 ) by sharing duration equally with preceding non-zero length branches ( the “equal” method [87] ) ; and ( 2 ) by imposing a minimum branch length of 1 Ma ( the “mbl” method [88] ) . We used maximum-likelihood model comparison [6] , [38] and “node height” test [14] , [39] methods ( Appendix S1 ) to test the prediction of the niche-filling hypothesis: that rates of morphological evolution diminish exponentially through time after an adaptive radiation [1] , [2] , [4] . The node height test treats standardised independent contrasts [89] as nodal estimates of evolutionary rate [39] and tests for systematic deviations from a uniform rate Brownian model , using regression against log-transformed geological age ( robust regression [14] , [53] ) . We also regressed standardised contrasts against nodal body mass estimates ( a proxy for generation time and other biological processes that might influence evolutionary rates ) . As well as testing for a “background” model of declining evolutionary rates through time , robust regression identifies and down-weights single nodes deviating substantially from the overall pattern [14] , [53] . These nodes represent substantial , temporally rapid , niche-shift events [14] , following the macroecological principle that organisms in different body size classes inhabit different niches and have different energetic requirements [41] . We used lowess lines to visualise non-linear rate variation with time and body mass . Exponentially declining rates of evolution through time , predicted by the niche-filling model of adaptive radiation [1]–[3] , were also tested by comparing the fit of an early burst model [6] , [7] with other commonly used models: Brownian motion , directional evolution ( “trend” ) , the Ornstein–Uhlenbeck model of evolution attracted to an optimum value , and stasis ( “white noise” ) [38] , [56] , [90] ( Appendix S1 ) . Explicit mathematical models of trait evolution on our phylogenies were fitted using the R packages GEIGER version 1 . 99–3 [91] and OUwie version 1 . 33 [55] ( for Ornstein–Uhlenbeck ( OU ) models only ) , and compared using AICc [92] , [93] . Unlike GEIGER , OUwie allows estimation of a trait optimum ( θ ) that is distinct from the root value ( Z0 ) in OU models . Values from GEIGER and OUwie are directly comparable: identical log likelihood , AICc , and parameter estimates are obtained for test datasets when fitting models implemented in both packages ( Brownian motion in all instances; and OU models when θ = Z0 for ultrametric trees ) ; although note that comparable standard error values entered to the OUwie function of OUwie 1 . 33 are the square of those entered to the fitContinuous function of Geiger 1 . 99–3 . The algorithm used to fit OU models in GEIGER 1 . 99–3 is inappropriate for non-ultrametric trees ( personal communication , Graham Slater to R . Benson , December 2013 ) . This problem is specific to OU models implemented by GEIGER 1 . 99–3 , and does not affect the other models that we tested . GEIGER 1 . 99–3 fits models of trait evolution using independent contrasts , after rescaling the branch lengths of the phylogenetic tree according to the model considered [7] . For all models , except the OU model in the case of non-ultrametric trees , the covariance between two taxa i and j can be written as a function of the path length sij shared between the two taxa ( e . g . , [6] , [7] ) . The tree can thus easily be rescaled by applying this function to the height of each node before computing independent contrasts . In the case of the OU model , the covariance between two taxa i and j is a function of both the shared ( pre-divergence ) portion of their phylogenetic history and the non-shared ( post-divergence ) portion [54] . In the case of an ultrametric tree , the non-shared portion can also be written as a function of sij ( it is simply the total height T of the tree , minus sij [90] , [94] ) , and the corresponding scaling function can be applied to the tree ( this is what is performed in GEIGER 1 . 99 . 3 ) . However , in the case of a non-ultrametric tree , the post-divergence portion of the covariance cannot be written as a function of sij , so there is no straightforward scaling function to apply . Instead , it is necessary to fit the model by maximum likelihood after computing the variance–covariance matrix . This is what is implemented in OUwie , and now in GEIGER 2 . 0 ( personal communication , Josef Uyeda to R . Benson , January 2014 ) . Our data and analytical scripts are available at DRYAD [95] .
|
Animals display huge morphological and ecological diversity . One possible explanation of how this diversity evolved is the "niche filling" model of adaptive radiation—under which evolutionary rates are highest early in the evolution of a group , as lineages diversify to fill disparate ecological niches . We studied patterns of body size evolution in dinosaurs and birds to test this model , and to explore the links between modern day diversity and major extinct radiations . We found rapid evolutionary rates in early dinosaur evolution , beginning more than 200 million years ago , as dinosaur body sizes diversified rapidly to fill new ecological niches , including herbivory . High rates were maintained only on the evolutionary line leading to birds , which continued to produce new ecological diversity not seen in other dinosaurs . Small body size might have been key to maintaining evolutionary potential ( evolvability ) in birds , which broke the lower body size limit of about 1 kg seen in other dinosaurs . Our results suggest that the maintenance of evolvability in only some lineages explains the unbalanced distribution of morphological and ecological diversity seen among groups of animals , both extinct and extant . Important living groups such as birds might therefore result from sustained , rapid evolutionary rates over timescales of hundreds of millions of years .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"paleozoology",
"paleobiology",
"vertebrate",
"paleontology",
"earth",
"sciences",
"paleontology",
"biology",
"and",
"life",
"sciences",
"evolutionary",
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2014
|
Rates of Dinosaur Body Mass Evolution Indicate 170 Million Years of Sustained Ecological Innovation on the Avian Stem Lineage
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The proper assembly of the synaptonemal complex ( SC ) between homologs is critical to ensure accurate meiotic chromosome segregation . The SC is a meiotic tripartite structure present from yeast to humans , comprised of proteins assembled along the axes of the chromosomes and central region ( CR ) proteins that bridge the two chromosome axes . Here we identify SYP-4 as a novel structural component of the SC in Caenorhabditis elegans . SYP-4 interacts in a yeast two-hybrid assay with SYP-3 , one of components of the CR of the SC , and is localized at the interface between homologs during meiosis . SYP-4 is essential for the localization of SYP-1 , SYP-2 , and SYP-3 CR proteins onto chromosomes , thereby playing a crucial role in the stabilization of pairing interactions between homologous chromosomes . In the absence of SYP-4 , the levels of recombination intermediates , as indicated by RAD-51 foci , are elevated in mid-prophase nuclei , and crossover recombination events are significantly reduced . The lack of chiasmata observed in syp-4 mutants supports the elevated levels of chromosome nondisjunction manifested in high embryonic lethality . Altogether our findings place SYP-4 as a central player in SC formation and broaden our understanding of the structure of the SC and its assembly .
The synaptonemal complex ( SC ) is a proteinaceous structure formed between each pair of homologous chromosomes during meiotic prophase I . Meiotic recombination unfolds , and crossover events are completed , in the context of this structure . The formation of these crossover events is a prerequisite for accurate chromosome segregation at the first meiotic division since it ensures that homologous chromosomes will be held together until anaphase I . Subsequently , sister chromatids separate in the second meiotic division resulting in the formation of haploid gametes . Without a functional SC these events are impaired , resulting either in a meiotic arrest or in increased chromosome nondisjunction . The SC is composed of two main parts: the axes and the central region . The axes-associated proteins assemble along the chromosomes , followed by the central region proteins , which proceed to connect homologous chromosomes axes thereby completing synapsis . This temporal separation in the assembly of these two SC sub-structures is supported by the observation made in various organisms that the lack of axis assembly results in severe defects in the recruitment of central region proteins [1]–[3] . The central region of the SC is comprised of proteins that form transverse filaments ( TF ) connecting or bridging homologous chromosomes . These TF proteins share a common structure: a central coiled-coil domain flanked by globular domains [4] , [5] . However , central region components notoriously lack a significant level of sequence conservation throughout species , which has hindered sequence-based efforts in identifying novel components . Remarkably though , the SC is highly conserved at the ultrastructural level . Specifically , studies in mice , flies and yeast suggest that the central region of the SC is comprised of pairs of central region proteins arranged in a head-to-head orientation with their N-termini positioned at the center of the SC [6]–[8] . Therefore , the assembly of multiple units along the chromosomes results in a zipper-like organization . Since coiled-coil domains show a propensity to dimerize it has been proposed that central region proteins form parallel dimers through their coiled-coil domains [7] , [9] . Studies of the yeast TF protein Zip1 revealed dimers and high-order multimers forming in vitro [8] , further supporting this model . Detailed deletion studies in yeast revealed that TFs span the distance between homologous chromosomes , specifically demonstrating that the coiled-coil domain is the main determinant of the width of the SC [9] . Zip1 is the only TF protein reported thus far for yeast and is therefore the sole component of the central region in this organism [10] . Flies have two central region proteins: C ( 3 ) G and CONA , but only C ( 3 ) G was proposed to act as the Drosophila TF protein [11] , [12] . In C . elegans , three central region proteins have already been identified: SYP-1 , SYP-2 , and SYP-3 , but due to their relatively small size it is reasonable to speculate that these proteins act cooperatively as TFs [2] , [13] , [14] . Electron microscopy ( EM ) analyses performed in various model systems revealed an electron-dense linear structure , called the central element , located along the middle of the central region of the SC [4] . Until recently , it was unclear whether this structure resulted from the overlap between the globular domains of the central region proteins , or was formed by central element-specific proteins localizing only at the middle of the SC . However , studies performed in mice revealed the identity of several proteins specifically localizing to the central element and identified their function in promoting the assembly of the SC as a 3-dimensional structure [15] , [16] . Central element proteins may therefore not only act as “clamps” holding the transverse filaments of the SC , but may also have a role in promoting SC assembly in a particular direction , determining its height ( SYCE1 ) [16] , [17] or length ( SYCE2 and TEX12 ) [15] , [18] . Although a central element has not yet been observed by EM analysis in C . elegans , it is possible that some of the central region proteins in this system perform a similar function to central element proteins from mice ( K . P . S . and M . P . C . unpublished data ) . The formation of the SC is tightly coordinated with the progression of recombination . The SC starts to form at the entry to meiotic prophase I during leptotene . The first meiotic DNA double-strand breaks ( DSBs ) are observed at this stage , and in some organisms , such as in yeast , plants and mice , DSB formation is essential for SC assembly [19] , [20] . In contrast , both in D . melanogaster and C . elegans , recombination is dispensable for SC formation [21] , [22] . Throughout these various organisms , the mature SC is observed at the pachytene stage where crossover recombination is completed . Deletion of genes encoding SC central region proteins results in lack of synapsis and defects in the progression of recombination . In yeast mutants lacking the central region protein Zip1 , crossovers are reduced to 25% of the levels observed in wild-type [23] , while male mice lacking SYCP1 proteins exhibit a pachytene arrest accompanied by an accumulation of mid to late recombination markers [24] . In D . melanogaster and C . elegans , synapsis is also crucial for recombination , given that crossover formation is impaired and chiasmata are not observed in SC-deficient mutants [2] , [12]–[14] . To further investigate the structure of the SC in C . elegans , we performed a yeast two-hybrid screen utilizing the known SC central region proteins as baits . This has resulted in the identification of SYP-4 , a novel component of the central region of the SC , and the first example of the identification of a SC structural protein through the yeast two-hybrid approach . Here , we show that SYP-4 displays all the hallmark features of an SC component . Specifically , SYP-4 is essential for chromosome synapsis , and in its absence , chromosomes initiate pairing interactions that cannot be stabilized . These defects result in increased germ cell apoptosis due to impaired DSB repair progression . In addition , crossover frequencies are severely reduced in syp-4 mutants , resulting in increased chromosome nondisjuction . SYP-4 localizes at the interface between homologous chromosomes during meiosis and this localization requires axis morphogenesis and is interdependent with the SYP-1 , SYP-2 and SYP-3 proteins . Moreover , SYP-4 interacts with SYP-3 in a yeast two-hybrid assay , suggesting that the function of SYP-4 is executed through its role as a member of the CR of the SC . Our discovery of SYP-4 therefore sheds new light on the structure of the SC in C . elegans and the roles its proteins play in meiosis .
We applied a yeast two-hybrid approach to identify novel proteins functioning in the SC in C . elegans . Specifically , we screened a cDNA library prepared from mixed-stage worms , utilizing SYP-1 , SYP-2 and SYP-3 full-length constructs as well as N- and C-terminal truncations as baits ( see Materials and Methods ) . As a result , we identified SYP-4 ( encoded by open reading frame H27M09 . 3 ) as a protein that interacts with both the full-length and C-terminal truncation constructs of SYP-3 . Screens performed with SYP-1 and SYP-2 as baits failed to identify SYP-4 as an interacting protein . In addition , when directly tested for a yeast two-hybrid interaction , SYP-4 failed to interact with the full length , as well as the C- or N-terminal truncation constructs of SYP-1 and SYP-2 ( Figure 1 , Figure S1 ) . SYP-4 encodes for a 605 amino acid protein . It is predicted to contain three stretches of coiled-coil structure in the region between residues 115 and 410 , based on analysis using the COILS program [25] . SYP-4 lacks any other evident structural domains or shared homology with other proteins in C . elegans or other organisms ( Figure 2A ) . To further investigate the role of SYP-4 in meiosis we examined the phenotype of syp-4 ( tm2713 ) mutants . These mutants carry a 213 bp out-of-frame deletion in the N-terminus of syp-4 ( Figure 2A ) , predicted to result in the absence of a fully functional SYP-4 protein in these worms . In addition , genetic analysis indicates that tm2713 is a null allele of syp-4 ( see Materials and Methods ) . syp-4 ( tm2713 ) mutants exhibit high levels of embryonic lethality ( 97 . 5% , n = 1855 ) and a high percentage of males ( 40% ) among their surviving progeny compared to wild type ( 0% and 0 . 2% , respectively , n = 1798 ) , which are phenotypes suggestive of errors in meiotic chromosome segregation . Analysis of chromosome morphogenesis in syp-4 ( tm2713 ) mutant gonads revealed defects in the progression of meiotic prophase I . As observed in wild type , chromosomes clustered towards one side of the nuclei upon entering into prophase I in syp-4 ( tm2713 ) mutants , therefore acquiring the polarized configuration that is characteristic of transition zone ( leptotene/zygotene ) nuclei ( Figure 2B , 2C , and 2F ) . However , in contrast to wild type , chromosomes failed to redisperse throughout the nuclear periphery upon entrance into pachytene in syp-4 ( tm2713 ) mutants . Instead , they remained mostly clustered until late pachytene in an “extended transition zone” morphology characteristic of null mutants for genes encoding proteins that constitute the central region of the SC [2] , [13] , [14] ( Figure 2B , 2D , and 2G ) . Moreover , when chromosomes redispersed in late pachytene nuclei in syp-4 ( tm2713 ) mutants , the thick parallel DAPI-stained tracks indicative of synapsed chromosomes observed in wild type were not apparent , and instead , thin DAPI-stained tracks were present suggesting defects in synapsis . In addition , transmission electron microscopy ( TEM ) analysis revealed a lack of SC formation in syp-4 ( tm2713 ) mutants , suggesting that the defects in synapsis stem from an inability to form the SC structure in the absence of SYP-4 ( Figure S2 ) . As nuclei progressed into diakinesis , a complete lack of chiasmata was observed . Therefore , instead of the 6 DAPI-stained bodies present in wild type diakinesis oocytes , corresponding to the six pairs of attached homologous chromosomes , 11 . 9 DAPI-stained bodies ( n = 31 ) were observed in syp-4 mutants ( Figure 2E and 2H ) . Altogether , this analysis implicates SYP-4 in playing a crucial role in chromosome synapsis and chiasma formation . To examine the immunolocalization of SYP-4 on whole mounted germlines , an α-SYP-4 antibody was raised against the N-terminus ( first 27 amino acids ) of SYP-4 . The specificity of this antibody was confirmed by detecting the presence of SYP-4 signal on meiotic chromosomes in wild type nuclei ( Figure 3A–3C ) and not in syp-4 ( tm2713 ) mutants ( Figure 3D ) . In wild type , SYP-4 was first detected upon entrance into meiosis as foci or short tracks on chromosomes in transition zone nuclei ( Figure 3A ) . SYP-4 remained associated with chromosomes throughout pachytene where it was observed between synapsed chromosomes ( Figure 3B ) [2] , [13] , [14] , [26] , [27] . During the transition from late pachytene into diplotene , the SC starts to disassemble and chromosome remodeling unfolds around the crossover site [28]–[30] . At this transition , SYP-4 signal was greatly reduced throughout most of the length of the chromosomes , becoming mostly concentrated towards one end of each chromosome . This asymmetric localization pattern is similar to that observed for the other SYP proteins during this transition [28] . Given that a single crossover ( obligate crossover ) is formed between each pair of homologous chromosomes in C . elegans [31] and this crossover is off-center , chromosome remodeling then results in bivalents at diakinesis with a cross-shaped configuration composed of a long and a short axes intersecting at the chiasma [28] . By early diakinesis , SYP-4 localization was restricted to the short axes ( short arms; the region distal to the chiasma ) ( Figure 3C ) , and by the end of diakinesis it was no longer detectable on chromosomes . This distinct pattern of localization is unique to SC central region proteins , as lateral element proteins remain on both arms of the bivalent through the end of diakinesis [2] , [13] , [14] , [26] , [27] . To further examine if SYP-4 acts as a central region protein , we tested whether SYP-4 localizes to unsynapsed chromosomes in a scenario in which lateral element assembly is normal , but the central region is not formed . In him-8 mutants , all autosomes are synapsed and exhibit normal localization of central region proteins , while the X chromosome remains unsynapsed and shows no localization of central region proteins despite normal axis morphogenesis [32] . We did not detect any SYP-4 association with the unsynapsed pair of chromosomes in the him-8 mutants ( Figure 3E–3G ) . These results further support a role for SYP-4 as a central region protein of the SC . We next examined the requirements for SYP-4 localization . First , we determined whether SYP-4 localization is dependent on the lateral element protein HIM-3 [27] and on the SC central region proteins SYP-1 , SYP-2 and SYP-3 [2] , [13] , [14] . In the absence of HIM-3 , SYP-4 was observed only as a dot or short patch associated with chromosomes in pachytene nuclei ( Figure 3H ) , similarly to the localization of SYP-1 , SYP-2 and SYP-3 in him-3 ( RNAi ) or him-3 null mutants [2] , [13] , [14][33] . This suggests that SYP-4 depends on normal axis morphogenesis for its assembly onto chromosomes . Central region proteins were also essential for SYP-4 localization , given that SYP-4 localization was not observed in syp-1 , syp-2 or syp-3 null mutants ( Figure 3I–3K ) . In contrast , the localization of axis-associated proteins , such as HIM-3 [27] or HTP-3 [34] , was not affected in syp-4 ( tm2713 ) mutants ( Figure 4A–4D and data not shown ) , suggesting that SYP-4 acts downstream of axis morphogenesis . However , all three known central region components ( SYP-1 , SYP-2 and SYP-3 ) failed to localize to chromosomes in the syp-4 ( tm2713 ) mutants ( Figure 4E–4L and data not shown ) . This interdependency of SYP-4 with the other SYP proteins is in agreement with a role for SYP-4 in central region assembly , as all SYP proteins exhibit similar interdependencies [2] , [13] , [14] . In C . elegans , synapsis is crucial for the stabilization of chromosome paring interactions [2] , [13] , [14] . Since our immunolocalization studies place SYP-4 as a central region protein , we used fluorescence in situ hybridization ( FISH ) to monitor its role in chromosome pairing throughout prophase . We divided gonads from wild type and syp-4 ( tm2713 ) mutants into 7 zones and analyzed the percentage of nuclei carrying paired chromosomes in each one of these zones ( Figure 5A ) . Specifically , we monitored pairing at opposite ends ( the pairing center ( PC ) and non-PC ends ) of chromosomes I and X . Levels of homologous pairing progressively increased in wild type nuclei as they entered meiosis in zones 2 to 3 . In early pachytene ( zone 4 ) , chromosomes I and X were observed pairing with their homologous partners in approximately 100% of the nuclei examined and this level was maintained throughout late pachytene ( zone 7 ) ( Figure 5C ) . In contrast , although an increase in homologous pairing levels was detected in syp-4 ( tm2713 ) mutants initiating at the same time as in wild type , pairing levels failed to reach 100% and decreased as prophase progressed ( Figure 5C , and Table S2 , p<0 . 0001 for all loci in zones 6 and 7 ) . This inability to stabilize pairing interactions was more pronounced for chromosome I than for the X chromosome , as exemplified by the observation of homologous pairing at the PC end of chromosome I in only 74% of the nuclei examined in zone 4 , compared to 94% at the PC end of the X chromosome in the same zone ( Figure 5C , p<0 . 0001 ) . These differences probably reflect the yet unexplained propensity of the X chromosomes to pair more efficiently compared to the autosomes when SC formation is impaired , as seen in other mutants in C . elegans [35]–[37] . In addition , significantly higher levels of pairing were observed in the PC regions compared to the non-PC regions ( Figure 5C ) . Specifically , pairing levels were 25% and 56% higher at the PC end compared to the non-PC end of chromosomes I and X , respectively ( for Chromosome I , zone 3 , p = 0 . 0061; for the X chromosome , zone 4 , p<0 . 0001 ) . The higher levels of pairing observed at the PC ends most likely stem from the fact that the initiation of homologous pairing events occurs at the PCs in a SYP-independent manner [2] , [13] , [14] , [38] . Taken together , our observations indicate that unlike axis-associated proteins , SYP-4 is dispensable for the initiation of paring interactions , but as meiosis progresses it is crucial for the stabilization of pairing interactions . Our studies , therefore , further support a role for SYP-4 as a central region component of the SC . Interhomolog recombination resulting in crossover events is dependent on chromosome synapsis [2] , [13] , [14] . Therefore , we examined the progression of meiotic recombination in the syp-4 ( tm2713 ) mutants by immunostaining whole mounted germlines with an anti-RAD-51 antibody ( RAD-51 is required for strand invasion/exchange during double-strand break repair; [39] ) . Specifically , wild type and syp-4 ( tm2713 ) mutant germlines were divided into 7 zones and levels of RAD-51 foci/nucleus were quantitated for all nuclei in each zone ( Figure 6A ) . In wild type gonads , levels of RAD-51 foci started to increase as nuclei entered into meiotic prophase and peaked in early to mid pachytene ( Figure 6C , zone 5 , 3 . 7 foci/nucleus ) , after which they gradually declined ( Figure 6C , zone 7 , 0 . 5 foci/nucleus ) . As nuclei exited pachytene , RAD-51 foci were no longer observed . In syp-4 ( tm2713 ) mutants , the increase in the levels of RAD-51 foci was first observed with a similar timing to wild-type , suggesting that the initiation of meiotic recombination is not dependent on SYP-4 ( Figure 6C ) . However , levels of RAD-51 foci were significantly higher in mid-pachytene in syp-4 ( tm2713 ) mutants compared to wild type ( Figure 6B and 6C , zone 5 , 10 . 2 foci/nucleus; p<0 . 0001 , two-tailed Mann-Whitney test , 95% C . I . ) and remained elevated up to late pachytene ( Figure 6C , zone 7 , 2 . 1 foci/nucleus; p<0 . 0001 , two-tailed Mann-Whitney test , 95% C . I . ) . The defect in DSB repair progression observed in syp-4 ( tm2713 ) mutants probably stems from the lack of chromosome synapsis and therefore a lack of close and stable proximity to a homologous template for repair . However , DSB repair is eventually accomplished in syp-4 ( tm2713 ) mutants , as RAD-51 foci were absent in diplotene nuclei and chromosome fragments were not apparent in oocytes at diakinesis ( Figure 2H ) . This delayed repair may proceed in part through recombination with the sister chromatid , as previously demonstrated for syp-2 and syp-3 mutants [13] , [40] . Unrepaired meiotic DSBs that persist until late pachytene may activate a DNA damage checkpoint , resulting in an increase in germ cell apoptosis at that stage [41] . Therefore , we examined the levels of germ cell apoptosis in syp-4 ( tm2713 ) mutants compared to wild type , spo-11 mutants , that lack DSB formation , and syp-3 null mutants , which have significantly impaired DSB repair ( Table 1 ) . syp-4 ( tm2713 ) mutants ( n = 57 ) showed a significant increase in the levels of apoptosis compared to wild type ( n = 42 ) and spo-11 ( n = 17 ) worms ( p<0 . 0001 for both pairwise combinations , two-tailed Mann-Whitney test , 95% C . I . ) , but did not differ significantly from syp-3 ( ok758 ) ( n = 22 ) ( p = 0 . 8471 ) . The elevated germ cell apoptosis levels are dependent on meiotically induced DSBs , as in the absence of spo-11 , syp-4 ( RNAi ) failed to increase apoptosis to the levels observed for syp-4 ( RNAi ) in the spo-11/nT1 heterozygous background . Taken together , this analysis suggests that the defects in DSB repair progression observed by monitoring the levels of RAD-51 foci throughout prophase , are sufficient to activate a DNA damage checkpoint response , and that such a checkpoint is intact in the syp-4 ( tm2713 ) mutants . To examine whether SYP-4 is required for crossover recombination we measured crossover frequencies in syp-4 ( tm2713 ) mutants for intervals spanning ∼80% of the X chromosome ( Table 2 ) and ∼70% of chromosome V ( Table S3 ) , utilizing genetic markers and single-nucleotide polymorphism ( SNP ) markers , respectively . As expected , given the lack of chromosome synapsis , the lack of chiasmata , and the defects observed with the progression of meiotic recombination , crossover levels were significantly reduced for the genetic intervals examined on both chromosomes in the syp-4 ( tm2713 ) mutants compared to wild type ( p<0 . 0001 , respectively , by the two-tailed Fisher's Exact Test , 95% C . I . ) ( Table 2 , Table S3 ) . Altogether , these results suggest that the crucial role that SYP-4 plays in chromosome synapsis is essential for the normal progression of meiotic recombination and crossover formation .
Here we report the first example of the identification of a novel structural protein of the SC through the yeast two-hybrid approach . We succeeded in uncovering SYP-4 through its physical interaction with SYP-3 . We provide multiple lines of evidence suggesting that SYP-4 is a novel structural component of the SC participating in central region formation . The localization pattern of SYP-4 is distinct from that of meiosis-specific cohesin or lateral element components . Specifically , SYP-4 is only observed associating onto chromosomes upon entrance into meiosis and not earlier as observed for cohesin . Moreover , in contrast to axis-associated components , SYP-4 remains localized only to the short axes , instead of both long and short axes , of the bivalents following chromosome remodeling in late pachytene , and SYP-4 is no longer chromosome-associated by the end of diakinesis . Furthermore , analysis of the syp-4 ( tm2144 ) mutants supports a role for SYP-4 downstream of axis formation . First , SYP-4 localization to chromosomes depends on axis-associated proteins and is absent from unsynapsed chromosomes ( him-8 mutants ) , while axis-associated proteins still localize to the unsynapsed chromosomes of the syp-4 ( tm2144 ) or him-8 mutants . Second , our analysis of homologous pairing levels in the syp-4 ( tm2144 ) mutants clearly points to SYP-4 acting downstream of the establishment of pairing , which is dependent on axis-associated proteins . Third , syp-4 ( tm2713 ) mutants exhibit several phenotypes observed in null mutants for central region components such as the extended transition zone morphology and the accumulation of high levels of RAD-51 foci accompanied by increased apoptosis in late pachytene nuclei [2] , [13] , [14] . These phenotypes are clearly distinct from those of mutants in axis-associated proteins , which show a shortened transition zone and low levels of RAD-51 foci [33] , [34] . Altogether , these data render strong support for a role of SYP-4 as a central region protein of the SC , and point to its crucial function in promoting stable interactions between homologous chromosomes leading to crossover formation . The fact that SYP-4 is interdependent with SYP-1 , SYP-2 and SYP-3 , suggests that these proteins act in a complex . This is further supported by our identification of SYP-4 via a yeast two-hybrid interaction with SYP-3 . Our yeast two-hybrid analysis suggests that SYP-3 may interact with SYP-4 through its N-terminal domain , since a C-terminal truncated SYP-3 can still interact with SYP-4 , but a N-terminal truncation cannot . We were unable to detect any interaction between SYP-3 and any truncated version of SYP-4 , implying that the full length of the protein is likely required for this interaction . In addition , using the yeast two-hybrid system , we did not observe an interaction between SYP-4 and HIM-3 , an axis-associated component and yeast Hop1 homolog [27] , HTP-3 , an axis-associated component and HIM-3 paralog proposed to link DSB formation with homologous pairing and synapsis [34] , [42] , HTP-1 , a HIM-3 paralog implicated in coordinating the establishment of pairing and synapsis in early prophase and involved in the crossover-dependent chromosome remodeling process observed in late prophase [36] , [37] , and ZHP-3 , the ortholog of budding yeast Zip3 proposed to couple recombination with SC morphogenesis in C . elegans [43] ( data not shown ) . Furthermore , an interaction between SYP-4 and either SYP-1 or SYP-2 was not detected when assessing for these pairwise interactions via the yeast two-hybrid system . These interactions were also not detected when we used SYP-1 and SYP-2 as baits in yeast two-hybrid screens of both cDNA and ORFeome libraries [44] , nor reported by any other large genomic screen published in the literature . Therefore , these observations suggest that SYP-4 might interact exclusively with SYP-3 . However , taking the limitations of the yeast two-hybrid system into account , we cannot exclude the possibility that other interactions were missed by this approach and may be detected using other experimental techniques . In addition , pull-down assays revealed that SYP-1 interacts with SYP-2 ( K . S-P . and M . P . C unpublished results ) , but we were unable to test other pairwise combinations by this approach due to technical limitations . Taken together , these observations lead us to hypothesize that the central region of the SC in C . elegans may be comprised of at least two modules: one consisting of SYP-1 and SYP-2 , and the other formed by SYP-3 and SYP-4 . However , given that all SYP proteins are interdependent [13] , [14 , this study] , these two sub-complexes must be either directly or indirectly interconnected , forming the higher-order structure of the central region of the SC . Alternatively , it is possible that all proteins assemble into a single complex lacking any sub-modules . Future studies are required to conclude which of these models accurately describes the structure of the SC in C . elegans . Studies in various model organisms are revealing the identity of the proteins forming the central region of the SC: C ( 3 ) G and CONA in Drosophila [11] , [12] , Zip1 in S . cerevisiae [10] , SYCP1 , SYCE1 , SYCE2 and TEX12 in mouse [15] , [16] , [18] , [24] , and the duplicated ZYP1a and ZYP1b proteins in Arabidopsis [45] . In mice , specific functions have been assigned to each one of four known CR proteins based on their distinct mutant phenotypes . A null Sycp1 mutant results in a lack of CE formation and in the absence of any recruitment of CR proteins onto chromosomes , whereas foci for some of the CR components are still observed in Syce2 , Syce1 and Tex12 mutants [17] , [24] , [46] . This is further supported by the observation of partial CR structures by EM in the case of Syce2 and Tex12 mutants [17] , [24] , [46] . In addition , SYCP1 ( 993 amino acids ) is three to four times larger than any other CR protein . Thus , these studies , complemented by a detailed immuno-EM analysis , have led to the conclusion that SYCP1 acts as a transverse filament , contributing to most of the width of the CR structure , while SYCE1 , SYCE2 and TEX12 are central element proteins that play an essential role in the assembly of the SC , but do not contribute much to the width of the structure . The Drosophila CR proteins , C ( 3 ) G and CONA , are structurally distinct from each other . Specifically , CONA ( 207 amino acids ) is almost a quarter of the size of C ( 3 ) G and lacks any coiled-coil domain . Nevertheless , both c ( 3 ) G and cona mutants exhibit similar phenotypes [11] , [12] . Thus , extrapolating from the mouse data , C ( 3 ) G may be the fly TF protein , while CONA may be a non-TF CR protein . The studies of the SC structure in C . elegans result in a more complex picture in which it is still hard to distinguish which of the SYP proteins act as bona fide TF proteins and which , if any , are central element-like proteins . In C . elegans , the total length of the coiled coils is predicted to be higher in SYP-1 ( 34 . 16 nm ) compared to SYP-2 ( 5 . 05 nm ) , SYP-3 ( 13 . 51 nm ) and SYP-4 ( 12 . 92 nm ) . Interestingly , the total length of the coiled coils in SYP-1 is smaller than that predicted for C ( 3 ) G in D . melanogaster ( 67 . 86 nm ) , Zip1 in S . cerevisiae ( 68 . 16 nm ) and SYCP1 in M . musculus ( 87 . 76 nm ) [12] . However , the width of the SC is conserved across most species ( ∼100 nm ) , including C . elegans [4] , [35] , [47] , [48] . One possible scenario is that all the SYP proteins assemble into a single complex that can span the width of the SC . Therefore , the shorter lengths predicted for the coiled coils of each central region protein may be compensated by their additive value in C . elegans . This model suggests that in C . elegans multiple proteins may have evolved out of the need to conserve the width of the SC . An alternative , albeit not mutually exclusive model , is that in C . elegans different central region proteins play different roles in the 3-dimensional context of the SC , similarly to what is observed for central element proteins in mice . It is tempting to speculate that SYP-2 ( 213 amino acids ) and SYP-3 ( 224 amino acids ) , the smaller proteins of the central region in C . elegans , which are similar in size to the mammalian central element proteins , take over a similar role in SC assembly . However , unlike what is observed in synapsis-defective mutants in mice , syp null mutants do not show a separation of function phenotype . This would be consistent with the notion that instead of each SYP protein loading individually onto chromosomes , the two SYP protein subunits ( SYP-1/SYP-2 and SYP-3/SYP-4 ) preassemble into one complex and then load onto chromosomes as one unit composed of all four SYP proteins . Therefore , loss of either one of the two subunits will lead to defects in loading of the other SYP proteins , consequently leading to a complete perturbation of CR assembly . Either model would explain the interdependency between the various SYP proteins observed by our analysis , and both models predict that , albeit taking the yeast two-hybrid assay limitations into account , a yet non-identified additional component may exist linking SYP-1/SYP-2 and SYP-3/SYP-4 . In summary , our studies have identified SYP-4 as a novel component of the SC and revealed a key protein-protein interaction required between the central region proteins SYP-4 and SYP-3 to form the mature SC structure in C . elegans .
All C . elegans strains were cultured at 20°C under standard conditions [49] . Bristol N2 worms were utilized as the wild type background , while Hawaiian CB4856 wild type worms were used for assessing recombination frequencies when utilizing single-nucleotide polymorphism ( SNP ) markers . The following mutations and chromosome rearrangements were used ( [2] , [13] , [14] , [27] , [50]–[52]; this work ) : LGI: syp-3 ( ok758 ) , hDf8 , syp-4 ( tm2713 ) , ccIs4251 , hT2[bli-4 ( e937 ) qIs48] ( I;III ) LGIV: him-3 ( gk149 ) , spo-11 ( ok79 ) , nT1[unc- ? ( n754 ) let- ? ( m435 ) ] ( IV;V ) LGV: syp-2 ( ok307 ) , syp-1 ( me17 ) The tm2713 allele was generated by the C . elegans National Bioresource Project in Japan . It contains a 213 base pair out-of-frame deletion including exon 2 and extending halfway into exon 3 of open reading frame H27M09 . 3 . Identical cytological defects to those observed in syp-4 ( tm2713 ) homozygotes were observed in syp-4 ( RNAi ) worms , including an extended transition zone phenotype and up to 12 univalents at diakinesis . Moreover , trans-heterozygotes for tm2713 and hDf8 , a deficiency encompassing the syp-4 locus , were indistinguishable from tm2713 homozygotes , as determined by examining their DAPI-stained germlines and scoring for the embryonic lethality and the percent of males observed among their surviving progeny ( 98 . 6% embryonic lethality , p = 0 . 2713 , and 29% male progeny , p = 0 . 6006 , by the two-tailed Mann-Whitney test , 95% C . I . ; n = 1598 ) . Finally , SYP-4 signal was not detected upon immunostaining syp-4 mutant germlines with an N-terminal anti-SYP-4 antibody . Taken together , these results suggest that syp-4 ( tm2713 ) is a null . tm2713 is a recessive syp-4 allele . DAPI-stained germlines of syp-4/+ hermaphrodites were identical to wild type germlines . The levels of embryonic lethality and males observed among syp-4/+ progeny were not statistically significant when compared to those observed for wild type ( 3 . 9% embryonic lethality , p = 0 . 0789 , and 0 . 08% male progeny , p = 0 . 6439 , two-tailed Mann-Whitney test; n = 2521 ) . The presence of coiled-coil domains within CR proteins was predicted utilizing the COILS program [25] . This program was run using the MTIDK matrix with a 21-residue window and applying an unweighted scan . Protein regions were predicted to adopt a coiled-coil conformation if the amino acids within those regions had scores of 0 . 5 or higher . To estimate the physical length of the coiled-coil domain in nm , the number of amino acids identified by this analysis was multiplied by 0 . 1485 nm , the mean axial rise per residue in a coiled-coil [54] . Full-length cDNAs of the syp-1 , syp-2 and syp-3 open reading frames , as well as C- and N-terminal truncations that retain the coiled-coil domains , were amplified by PCR . The amplification was performed from a cDNA library generated from mixed-stage C . elegans using primers that contain Gateway compatible sequences and a gene specific sequence as in [55] and indicated in Table S1 . Gateway cloning , cDNA and ORFeome library screening , and X-Gal and 3AT assays for examining yeast two-hybrid interactions were performed as in [56] . RNAi-mediated depletion of syp-4 was performed at 20°C as described in [53] , except that 1 mM IPTG was utilized . SYP-4 cDNA was cloned into the pL4440 feeding vector . Control RNAi was performed by feeding worms with HT115 bacteria carrying the empty pL4440 vector . Wild type and syp-4 ( tm2713 ) adult hermaphrodites ( 20–24 hr post-L4 ) were prepared for high pressure freezing as described in [2] . 100 nm-thick longitudinal sections of three wild type worms and three syp-4 mutant worms were examined for the presence of SC in nuclei at the late pachytene region . Distances between electron-dense chromatin patches arranged in parallel were measured and the presence of an SC was only scored positively when distances were within the range observed in wild type ( 90 nm–125 nm; [35] ) for all points measured along a given pair of patches ( between 1 to 4 points were measured for each pair ) . SC stretches were observed in 64% of the wild type nuclei in late pachytene ( n = 70 ) . In contrast , dispersed patches of electron dense chromatin , indicating a lack of SC , were observed in 96 . 4% of the syp-4 ( tm2713 ) nuclei at this stage ( n = 83 ) . FISH probes were generated as in [21] from the following pooled cosmids obtained from the Sanger Center: D1037 , ZC535 , F21A9 ( I , left ) ; F14B11 , F32A7 ( I , right ) ; F28C10 , F57C12 , F13C5 , M6 , M02A10 , C02H7 , T04G9 , F25E2 , C03F1 , F56F10 , ZC13 ( X , left ) ; T23E7 , F20B4 , F15G10 , K09G11 ( X , right ) . Cosmids were labeled with either fluorescein-12-dCTP ( PerkinElmer ) or Digoxigenin-11-dUTP ( Roche ) . Homologous pairing was monitored quantitatively as in [57] , with FISH signals considered paired when separated by ≤0 . 75 µm . The average number of nuclei scored per zone ( n ) from three gonads each for wild type and syp-4 ( tm2713 ) are as follows: zone 1 ( n = 53 ) , zone 2 ( n = 67 ) , zone 3 ( n = 88 ) , zone 4 ( n = 96 ) , zone 5 ( n = 104 ) , zone 6 ( n = 94 ) , and zone 7 ( n = 78 ) . The rabbit α-SYP-4 N-terminal polyclonal antibody was generated using the following peptide antigen: MSFPTLQVRPNEKNPKVLRCHEFLRQS . Animals were immunized and bled by Sigma-Genosys , The Woodlands , TX . Affinity purification of this antibody was performed using SulfoLink® from Pierce following the manufacturers instructions . DAPI staining , immunostaining and analysis of stained meiotic nuclei were performed as in [13] . Primary antibodies were used at the following dilutions: rabbit α-SYP-1 ( 1∶100 ) , rabbit α-SYP-2 ( 1∶100 ) , rabbit α-SYP-3 ( 1∶100 ) , rabbit α-RAD-51 ( 1∶100 ) , rabbit α-HIM-3 ( 1∶100 ) , guinea pig α-HTP-3 ( 1∶500 ) , and mouse α-REC-8 ( 1∶100 ) . The secondary antibodies used were: Cy3 anti-rabbit , FITC anti-guinea pig and FITC anti-mouse ( Jackson Immunochemicals ) , each at 1∶100 . The images were acquired using the DeltaVision wide-field fluorescence microscope system ( Applied Precision ) with Olympus 40×/1 . 35 and 100×/1 . 40 lenses . Optical sections were collected at 0 . 20 µm increments with a coolSNAPHQ camera ( Photometrics ) and SoftWoRx 3 . 3 . 6 software ( Applied Precision ) , and deconvolved using SoftWoRx 3 . 3 . 6 software . Images are projections halfway through 3D data stacks of whole nuclei ( 15 to 30 0 . 2 µm slices/stack ) , except for diakinesis images , which encompass entire nuclei , prepared using SoftWoRx 3 . 3 . 6 and SoftWoRx Explorer 1 . 3 . 0 software ( Applied Precision ) . Quantitation of RAD-51 foci was performed for all seven zones composing the germline as in [13] . The average number of nuclei scored per zone ( n ) from three gonads each for wild type and syp-4 ( tm2713 ) were: zone 1 ( n = 172 ) , zone 2 ( n = 243 ) , zone 3 ( n = 238 ) , zone 4 ( n = 200 ) , zone 5 ( n = 166 ) , zone 6 ( n = 131 ) and zone 7 ( n = 119 ) . Meiotic crossover recombination frequencies for chromosome V were assayed utilizing single-nucleotide polymorphism ( SNP ) markers , with the pkP5076 and snp_Y17D7B DraI SNP primers as in [58] . syp-4 homozygous cross-progeny were detected by mating to the ccIs4251 strain as described in [42] .
|
Meiosis is a two-part cell division program that ensures the formation of haploid gametes ( e . g . eggs and sperm ) , which can then reconstitute a species' ploidy through fertilization . A critical step towards accomplishing this task is the accurate segregation of homologous chromosomes away from each other during meiosis I . This requires the formation of at least one obligatory crossover event ( genetic exchange ) between each pair of homologous chromosomes . In most organisms , the formation of all crossover events greatly relies on the synaptonemal complex ( SC ) . This “zipper-like” structure holds the pairs of homologous chromosomes together during meiotic prophase I , and crossover recombination is completed in the context of the fully formed SCs . Here , we identify SYP-4 as a novel structural component of the SC in the nematode C . elegans . In its absence , SCs fail to form , resulting in a lack of crossover formation and increased errors in chromosome segregation . SYP-4 interacts in a yeast two-hybrid assay with SYP-3 , one of the SC proteins , and its localization onto chromosomes is interdependent with SYP-1 , SYP-2 , and SYP-3 proteins . SYP-4 therefore plays a critical role during C . elegans meiosis in generating the ultrastructurally conserved SC that is ubiquitously present from yeast to humans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology/germ",
"cells",
"genetics",
"and",
"genomics/chromosome",
"biology",
"cell",
"biology/nuclear",
"structure",
"and",
"function"
] |
2009
|
A Yeast Two-Hybrid Screen for SYP-3 Interactors Identifies SYP-4, a Component Required for Synaptonemal Complex Assembly and Chiasma Formation in Caenorhabditis elegans Meiosis
|
The ability of innate immune cells to sense and respond to impending danger varies by anatomical location . The liver is considered tolerogenic but is still capable of mounting a successful immune response to clear various infections . To understand whether hepatic immune cells tune their response to different infectious challenges , we probed mononuclear cells purified from human healthy and diseased livers with distinct pathogen-associated molecules . We discovered that only the TLR8 agonist ssRNA40 selectively activated liver-resident innate immune cells to produce substantial quantities of IFN-γ . We identified CD161Bright mucosal-associated invariant T ( MAIT ) and CD56Bright NK cells as the responding liver-resident innate immune cells . Their activation was not directly induced by the TLR8 agonist but was dependent on IL-12 and IL-18 production by ssRNA40-activated intrahepatic monocytes . Importantly , the ssRNA40-induced cytokine-dependent activation of MAIT cells mirrored responses induced by bacteria , i . e . , generating a selective production of high levels of IFN-γ , without the concomitant production of TNF-α or IL-17A . The intrahepatic IFN-γ production could be detected not only in healthy livers , but also in HBV- or HCV-infected livers . In conclusion , the human liver harbors a network of immune cells able to modulate their immunological responses to different pathogen-associated molecules . Their ability to generate a strong production of IFN-γ upon stimulation with TLR8 agonist opens new therapeutic opportunities for the treatment of diverse liver pathologies .
The liver is an essential organ at the center of carbohydrate , lipid and protein metabolisms . It is crucial for clearing toxins and pathogens that reach the circulatory compartment from the gut . The liver is also home to abundant populations of innate immune cells ( monocytes , NK and NKT cells ) whose local activation needs to be tuned in order to avoid severe liver damage with life-threatening consequences [1] , [2] . For these reasons , the immunological environment of the liver has been primarily associated with tolerogenic features: abundance of immunosuppressive cytokines/ligands ( e . g . , IL-10 or PD-L1 ) , tolerance to LPS stimulation and production of inhibitory enzymes ( e . g . , arginase ) that can suppress immune responses [3] , [4] . The ability of pathogens like HBV , HCV and Plasmodium spp . to establish persistent infections in the liver can be facilitated by such immunotolerant features . The hypo-responsiveness of liver-resident immune cells is , however , not absolute and selective triggers are known to activate hepatic NK or CD56+ T cells: for example , liver-resident iNKT cells are activated in mice infected with Borrelia burgdorferi [5] . Using human immune cells purified from a donor liver for transplant , it was also shown that while Toll-like receptor 4/TLR4 and TLR2 agonists triggered a tolerogenic response and preferential IL-10 production , a TLR3 agonist activated hepatic NK cells through IL-18-mediated stimulation [6] . The difficulty in obtaining a substantial number of intrahepatic human cells has precluded a comprehensive analysis of the signals necessary to activate liver immunity . Furthermore , there is a general problem of translating murine findings to the human liver . The vast population of liver-resident T cells expressing NK markers are mostly composed by MR1-restricted mucosal-associated invariant T ( MAIT ) cells in humans and not by the classical CD1d-restricted NKT cells , which are abundant in mice [7] . Thus , studies on human hepatic immune cells are crucial . Utilizing intrasinusoidal samples obtained during the procedure preceding living-donor liver transplantation , we characterized the requirements for selective activation of human intrahepatic immune cells and the cellular subsets responsible for inducing a potent immune response in the human liver microenvironment .
To test whether liver intrasinusoidal cells can be differentially activated by various pathogen-associated molecules , mononuclear cells purified from healthy liver grafts preceding living-donor liver transplantations ( called liver-derived cells/LDCs ) were stimulated with TLR agonist 1/2 , 2 , 2/6 , 3 , 4 , 5 , 7 , 8 , or 9 ( respectively Pam3CSK4 , HKLM , FSL-1 , poly ( I:C ) , LPS , flagellin , imiquimod , ssRNA40 , or CpG ODN2216 ) or anti-CD3/CD28-coupled beads ( TCR ) as a control . We used TLR agonists at the concentration that triggered maximal activation in PBMCs ( not shown ) . After 18 hours of incubation , supernatants were collected and the concentrations of antiviral ( IFN-α , IFN- γ ) , pro-inflammatory ( IL-1β , IL-6 , IL-17A , TNF-α ) and immunosuppressive ( IL-10 ) cytokines were measured . The purity and cell composition of LDCs were recently described in detail [8] . For clarity , the differential composition of lymphocytes and of monocytes and dendritic cells/DCs obtained from the liver ( n = 6 ) or peripheral blood ( n = 7 ) of age-matched healthy subjects are shown again in Fig . 1A . Liver-derived lymphocytes are enriched in CD56Bright NK and T cells expressing NK markers CD56 and CD161 which are mainly mucosal-associated invariant T ( MAIT ) cells [8] . The frequency of different monocyte subsets ( CD14++CD16− , CD14+CD16+ and CD14dimCD16+ ) and of DCs were in contrast similar in LDCs and PBMCs . Fig . 1B shows the total production of IFN-α , IFN-γ , IL1β , IL-6 , IL-10 , TNF-α obtained in PBMCs of 5 healthy subjects and LDCs from 9 healthy liver donors ( matched for age ) . The tested TLR agonists activated higher production of cytokines in PBMCs than LDCs with the single notable exception of the TLR8 agonist ssRNA40 . Analysis of the single cytokines produced in ssRNA40-activated LDCs showed a very high quantity of IFN-γ , followed by TNF-α and IL-1β ( Fig . 1C ) . IFN-γ quantity produced by ssRNA40-activated LDCs ( ∼5000 pg/mL ) was higher than the IFN-γ triggered by anti-CD3/CD28-coupled beads ( ∼3000 pg/mL ) and by the other TLR agonists ( <500 pg/mL ) ( Fig . 1C and 1D ) . ssRNA40-activated LDCs also produced high quantities of IL-1β and TNF-α , but the differences between LDCs and PBMCs were not as dramatic as that observed for IFN-γ: on average 27 times higher in LDCs than PBMCs ( Fig . 1C and 1D ) . The TLR4 agonist LPS elicited also a high production of cytokines in LDCs ( Fig . 1B ) . The pro-inflammatory IL-1β , IL-6 and TNF-α and the immunoregulatory IL-10 cytokines were the most highly produced with levels similar between PBMCs and LDCs ( IL-6 , TNF-α , IL-10 ) or higher in PBMCs than LDCs ( IL-1β ) ( Fig . 1C ) . IFN-α was detectable only at low concentrations ( ∼63 pg/mL ) upon TLR9 activation with production higher in PBMCs than in LDCs ( not shown ) . TLR agonists did not induce production of IL-17A , which was only detectable at low levels in LDCs and PBMCs ( ∼57 and ∼124 pg/mL , respectively ) upon TCR stimulation ( not shown ) . We next characterized the cellular component responsible for the high IFN-γ , TNF-α and IL-1β production in LDCs after ssRNA40 stimulation . Visualization of cytokine-producing cells was performed by intracellular staining , adding the protein transport inhibitor brefeldin A either immediately or only in the last 5 hours of the stimulation . IFN-γ-producing cells within both the CD3+ and CD3− lymphocyte populations were visualized only with the addition of brefeldin A in the final 5 hours as the overnight presence of brefeldin A resulted in a diminished response ( not shown ) . Characterization of the cellular composition of IFN-γ producers based on NK- and T-cell subsets ( see tree diagram in Fig . 2A ) revealed that despite different frequencies in different individual samples ( pie charts in Fig . 2A ) , three lymphocyte populations were responsible for the majority of IFN-γ production upon ssRNA40 stimulation: CD3−CD56+CD16− ( CD56Bright NK or NKBright cells ) , CD3+γδ−CD4−CD161+Valpha 7 . 2+ ( MAIT cells ) and to a lesser extent , CD3+γδ+ cells ( γδ T cells ) . CD56Dim NK ( NKDim cells ) and conventional T cells were , in contrast , weakly activated ( Fig . 2A and 2B ) . Importantly , as shown in the dot plots of a representative sample in Fig . 2B , ssRNA40-activated lymphocytes produced only IFN-γ , while TNF-α and IL-1β were produced by activated monocytes ( Fig . 2B ) . The higher production of IFN-γ by ssRNA40-activated LDCs in comparison to PBMCs can be explained by the preferential liver compartmentalization of MAIT and NKBright cells ( Fig . 2C first panel ) . However we also tested whether liver-derived MAIT and NKBright cells were more efficiently activated than blood-derived cells . As shown in Fig . 2C , the frequency of IFN-γ-producing MAIT or NKBright subset within the total population present in liver or blood ( Fig . 2C second panel ) and their capacity to produce IFN-γ , measured by geometric MFI ( Fig . 2C third panel ) were not different in relation to their anatomical origin . Therefore , the higher production of IFN-γ in the supernatant of ssRNA40-stimulated LDCs in comparison to PBMCs is likely a consequence of the specific enrichment of MAIT and NKBright cells in the hepatic environment . We conducted a series of experiments to characterize the mechanism of ssRNA40-mediated activation of intrahepatic immune cells . We first blocked the generation of endolysosomes that are necessary for TLR8 signaling upon ssRNA40 stimulation [9] by using the endosomal acidification inhibitor chloroquine and the vacuolar-type H+ ATPase inhibitor bafilomycin A1 . The experiments demonstrated that inhibition of endolysosome acidification indeed blocked ssRNA40-mediated activation ( Fig . S1 ) , confirming the essential role of TLR8 in ssRNA40 stimulation . We also confirmed that the production of IFN-γ was dependent on uracil-rich ssRNA40 , but not seen with its control , adenine-rich ssRNA41 [10] , or its vehicle , Lyovec ( Fig . S2 ) . We next analyzed how ssRNA40 activates MAIT and NKBright cells . A previous study of hepatic NK-cell activation demonstrated that IL-18 and IL-12p70 ( hereby stated as IL-12 ) production by hepatic monocytes is necessary for their activation [6] . MAIT cells are instead known to be principally triggered by microbial riboflavin metabolites presented by MR1 molecules on APCs [11] , even though recent studies by others [12] and us [13] indicated that upon overnight co-culture with bacteria , circulating MAIT-cell activation was dependent on the MR1 recognition as well as on the presence of IL-12 and IL-18 . We therefore analyzed the role played by IL-12 and IL-18 in the ssRNA40-mediated activation of intrahepatic lymphocytes . LDCs from three different donors were stimulated with ssRNA40 in the presence or absence of anti-IL-12 or anti-IL-18 neutralizing antibodies . Anti-IL-12 or anti-IL-18 antibodies inhibited activation of hepatic MAIT and NK cells ( Fig . 3A ) . Furthermore , addition of recombinant IL-12 and IL-18 to LDCs induced IFN-γ production selectively in MAIT , NKBright and also γδ T cells , similarly to what observed in ssRNA40-mediated stimulation of LDCs ( Fig . 3B ) . The importance of IL-12 and IL-18 cytokines in the ssRNA40-mediated activation of intrahepatic immunity was further supported by the observation that across all tested TLR agonists , only ssRNA40 elicited a production of both IL-12 and IL-18 cytokines in LDCs ( Fig . 3C ) . More importantly , ssRNA40 was the only TLR agonist that stimulated a significantly large ( mean: 824 . 2 pg/mL ) production of IL-12 ( P<0 . 0001 ) . Human monocytes , particularly the CD14dimCD16+ population , have been shown to sense nucleic acids through TLR8 receptors [14] . We therefore directly tested whether hepatic monocytes produce IL-12 and IL-18 upon ssRNA40 stimulation . Hepatic monocytes were sorted from 2 different donors of LDCs and then stimulated with ssRNA40 for 18 hours . IL-12 and IL-18 production were detected both at mRNA and protein levels ( Fig . 3D ) . To directly test the ability of ssRNA40-activated monocytes to stimulate IFN-γ production by NK and MAIT cells , we sorted different populations of APC and lymphocytes ( NK and MAIT ) from one normal liver and one healthy peripheral blood . Regardless of their anatomical origin ( Fig . 3E ) , only monocytes were able to stimulate IFN-γ production in NK and MAIT cells . The ability of intrahepatic and circulating monocytes to activate NK and MAIT cells after ssRNA40 activation was further confirmed in depletion experiments . These were performed in cells purified in two distinct healthy liver and blood samples . Depletion of monocytes or total APC from bulk cell populations abolished the ssRNA40-mediated activation of MAIT and NK cells ( Fig . 3F ) . We recently demonstrated that overnight bacterial stimulation of healthy PBMCs activated MAIT cells to produce IFN-γ directly via MR1 as well as indirectly via IL-12- and IL-18-dependent mechanisms [13] . To test if this finding was true in LDCs , we stimulated bulk population with riboflavin- and non-riboflavin-synthesizing bacteria ( E . coli and E . faecalis respectively ) : only riboflavin-synthesizing bacteria can produce a ligand presented by MR1 [11] . The bacterial stimulation was performed for 20 hours in the presence or absence of blocking antibodies against MR1 or IL-12 and IL-18 . Importantly , we observed that upon overnight co-culture with riboflavin-synthesizing bacteria , hepatic MAIT cells were activated by both IL-12 and IL-18 cytokines and by MR1-restricted ligand ( Fig . 4A and 4B ) . In contrast , activation by non-riboflavin-synthesizing bacteria was entirely dependent upon IL-12 and IL-18 . Similar results were obtained using THP1 cells , a monocytic cell line , as APCs . Consistent with our findings with blood-derived MAIT cells [13] , early activation ( 5 hours ) of liver-derived MAIT cells with riboflavin-synthesizing bacteria was MR1-dependent , while later activation ( 20 hours ) was dependent upon both MR1 and IL-12 and IL-18 ( Fig . S3 ) . Similarly , experiments using non-riboflavin-synthesizing bacteria reinforced the important role of cytokines in MAIT-cell activation to produce IFN-γ . To further analyze whether TLR8-mediated activation mimics bacterial stimulation in the intra-hepatic environment , we pulsed LDCs with ssRNA40 , riboflavin-synthesizing bacteria ( P . aeruginosa ) , anti-CD3/CD28-coupled beads or PMA/Ionomycin and characterized cytokines produced by hepatic MAIT cells after overnight incubation . Fig . 5 shows that ssRNA40 elicited a cytokine profile in intrahepatic MAIT cells similar to that induced upon bacterial infection , but different to those obtained by anti-CD3/CD28-coupled beads or PMA/Ionomycin . MAIT cells exclusively produced IFN-γ without a concomitant major production of TNF-α ( Fig . 5 ) or IL-17A ( not shown ) when stimulated with ssRNA40 or bacteria . In contrast , significant proportions of TNF-α mono-producers and IFN-γ/TNF-α double producers were observed only when MAIT cells were stimulated with anti-CD3/CD28-coupled beads or PMA/Ionomycin . Taken together , ssRNA40 induced an intrahepatic immune response that resembles the one triggered by bacterial infection . The demonstration that a TLR8 agonist , through production of IL-12 and IL-18 from intrahepatic monocytes , stimulated a robust production of IFN-γ in liver resident lymphocytes could have therapeutic implications . In HBV transgenic mice , for example , IL-12 and IL-18 treatment causes an inhibition of HBV replication that was mediated by IFN-γ production from NK and NKT cells [15] , [16] . However , pathological processes can alter the cellular composition and the cytokine milieu of the liver microenvironment which can result in the inhibition of T- and NK-cell function [17] . A recent characterization of the immunological profile in chimpanzees treated with an oral TLR7 agonist has for example shown a reduced cytokine and chemokine production in chronic HBV-infected animals in comparison to the healthy ones [18] . We therefore tested whether liver pathological processes can alter the ability of TLR8 agonist to activate intrahepatic immunity . The limited number of cells obtained in diagnostic liver biopsies is insufficient to perform any functional analysis . Thus we purified hepatic immune cells from HBV- or HCV-associated liver explants due to the end-stage liver disease following the identical cold perfusion method applied for healthy livers . In all cases , the pathological processes were very advanced , i . e . , either cirrhosis or hepatocellular carcinoma . The functionality of the sorted whole monocytes from pathological liver explants to respond to ssRNA40 stimulation was first evaluated at protein ( 4 pathologic livers ) and mRNA ( 5 pathologic livers ) levels . Upon ssRNA40 overnight stimulation , pathological liver-derived monocytes were able to produce IL-12 and IL-18 cytokines . The cytokine concentrations in pathological livers were slightly higher than observed in healthy livers , even though they didn't reach statistical significance ( Fig . 6A ) . We next tested the ability of pathological LDCs to produce IFN-γ after ssRNA40 stimulation . Intracellular analysis of IFN-γ production confirmed that also in responding pathological livers , MAIT and NKBright cells were the major IFN-γ-producing innate immune cells ( Fig . 6B ) . In most of the LDCs of pathological livers ( 5 out of 7 ) , ssRNA40 stimulated IFN-γ production from viable intrahepatic lymphocytes even though to levels lower than those observed in healthy LDCs ( mean±SD: 1292±1957 vs 4975±3948 , P = 0 . 0311; Fig . 6C ) . Taken together , these data show that ssRNA40 can mediate activation of intrahepatic immunity also in livers with advanced pathological process . Since the lower IFN-γ quantity detected in the advanced pathological livers might be a direct consequence of the low intrahepatic frequencies of cells responding to ssRNA40 , we characterized the composition of liver-derived cells in samples from both end-stage liver diseases and in less advanced liver pathologies , such as chronic hepatitis B . In the latter , low cell numbers have severely limited our analysis only to the frequencies of MAIT cells . Among the major ssRNA40-responding cells ( MAIT , γδ T and NK cells ) , patients with end-stage liver disease have a significantly lower frequency of MAIT cells compared to healthy subjects ( Fig . 6D ) . In addition , a viable population of monocytes were clearly detectable in LDCs purified from pathological livers with the only difference being an increased frequency of CD16+ monocytes ( comprising both CD14+CD16+ and CD14dimCD16+ subsets ) in pathological compared to healthy livers ( 15 . 3% vs 5 . 5% of total CD45+HLA-DR+ cells; P = 0 . 05 ) , confirming previous results obtained in chronic hepatitis [19] . MAIT cells were , similar to NKBright cells [20] , enriched in the liver compartment of untreated CHB patients and , unlike in end-stage liver disease , hepatic MAIT-cell frequency remains similar to that found in healthy subjects ( Fig . 6E ) . Thus , the intrahepatic innate lymphocyte population is relatively conserved at least in chronic HBV-infected livers and might be potentially triggered by ssRNA40 stimulation .
We demonstrate here that the human liver environment is specifically equipped with a network of innate immune cells ( monocytes , MAIT and NKBright cells ) , which is not intrinsically tolerant or hyporesponsive . On the contrary , this cellular network can sense distinct pathogen-associated molecules and can generate a robust immune response . We show that substantial intrahepatic production of IFN-γ is preferentially activated in LDCs by the TLR8 agonist ssRNA40 and we identified CD161Bright MAIT and NKBright cells as the principal liver-resident IFN-γ-producing lymphocytes . We also show that their activation is mediated by IL-12 and IL-18 produced by the TLR8-activated hepatic monocytes . The demonstration that intrahepatic immune cells were strongly activated by ssRNA40 , but not by exposure to LPS , flagellin or other bacterial products , shows that the immune cells residing within the liver vascular system modulate their immunological responses to different pathogen-associated molecules [3] . In this context the TCR specificity of the MAIT cells is not critical and indeed the CD161Bright CD8+ T-cell population as a whole , of which the MAIT cells are the key subset , possesses the innate feature to preferentially respond to cytokine-mediated stimulation [13] . Even though TLR8-mediated recognition has been associated with viral infections , new findings link TLR8 sensing to viable bacterial infection [21] , [22] and unmodified RNAs , a hallmark of viable bacteria [23] , [24] . We might therefore hypothesize that the strong activation of intrahepatic immunity selectively triggered by TLR8 agonist reflects the ability of intrahepatic cells - in particular MAIT cells well known for their antimicrobial capability [25] - to control active bacterial infection while ignoring intestinal floral products that leak into the intrahepatic blood circulation . The strong activation of intrahepatic immunity observed in our experiments utilizing three different bacteria strains ( P . aeruginosa , E . coli and E . faecalis ) further support such an hypothesis . Nevertheless , our data might indicate that TLR8-mediated activation of intrahepatic immunity might also take place during other pathological conditions , e . g . , acute viral hepatitis and hepatic flares during chronic viral hepatitis . Unmodified RNAs , the possible trigger of TLR8-mediated immune response , are also highly enriched within mitochondria and as such can be released during flares when cells are damaged or die [23] . In this context , our observation that ssRNA40-mediated activation could be detected in pathologic liver-derived cells suggests that the ssRNA40-generated immune response can be induced during chronic viral infection even though the impact of hepatotropic viruses on the TLR8-mediated activation of intrahepatic immunity requires more in depth analysis . At the moment , our attempts to activate hepatic MAIT cells ( as well as NKBright ) by exposing LDCs to purified HBV virions ( unpublished results ) have been unsuccessful . Irrespective of their biological role during infections , the network of innate immune cells able to respond to TLR8 agonist within the liver sinusoids can open new therapeutic opportunities . TLR7 agonists are the most studied TLR agonists for the treatment of liver pathologies [18] , [26] due to their ability to suppress HCV replication in hepatocytes [27] and stimulate robust IFN-α production in plasmacytoid dendritic cells [28] . However , our data shows that a TLR8 agonist could be advantageous for liver-specific treatment as such an agonist preferentially activates innate immune cells within the liver compartment while TLR7 agonists do not display any preference and do not induce robust IL-12 and IFN-γ production . The selective liver-localized production of IFN-γ could exert an efficient antiviral effect , as HBV clearance was shown to be mediated by IFN-γ in infected chimpanzees [29] and HBV transgenic mice [30] . In this respect it is important to note that the recent demonstration of therapeutic efficacy in HBV-infected chimpanzees of a novel synthetic TLR agonist ( GS-9620; labeled as a TLR7 agonist ) was not only associated with serum detection of IFN-α , but also of increased IL-12 and CXCL10 levels [18] . Such a cytokine/chemokine profile is also consistent with a TLR8-mediated response and can be explained by the TLR7/TLR8 degeneracy of this synthetic compound at high doses [31] . In addition to its ability to activate NKBright and MAIT cells , IL-12 production following TLR8 activation might have other immunomodulatory effects , since IL-12 can induce partial functional recovery of exhausted HBV-specific CD8+ T cells [32] . This concept is also supported by data from a murine model of persistent HBV infection where single-stranded RNA could reverse immune tolerance [33] . On the other hand , the robust activation of intrahepatic cellular immunity via TLR8 needs to be tightly controlled , both during natural infection or if a TLR8 agonist is to be exploited for therapy of chronic viral infections . IFN-γ is not only an antiviral cytokine but also further induces production of inflammatory chemokines , e . g . , CXCL10 , by hepatocytes [34] . CXCL10 can induce rapid recruitment of circulating memory T cells into solid organs [35] but can also aggravate inflammatory events that may lead to severe liver damage [36] . In this respect , the balance between protective or damaging effects of intrahepatic innate immune-cell activation will have to be carefully evaluated in vivo . In conclusion , we demonstrate that a network of hepatic innate immune cells responds to TLR8-mediated activation by inducing a potent immune response . The strategic positioning of such cells in the liver can constitute an advantage to immediately sense and control the presence of viable bacteria in the gastrointestinal circulation and might also open new therapeutic options for the treatment of different liver diseases .
Collection of healthy human LDCs ( n = 24 ) and PBMCs ( n = 7 ) was performed as previously reported [8] . Due to ethical considerations , immune cells derived from healthy liver donors were instead compared to those of age-matched healthy donors of PBMCs [8] . Liver biopsies were performed on untreated chronic HBV-infected/CHB patients ( n = 13 ) . These biopsies were cultured in medium for 24 hours to gently release mononuclear cells , ( ‘walk-out’ method ) [37] . Cell viability collected using this method exceeded 80% . Among these CHB patients , 10 subjects donated blood as well , from which PBMCs were collected . Mononuclear cells were also collected from HBV/HCV-related liver explants due to end-stage liver disease through perfusion using cold buffer solution ( n = 6 ) . All participants in this study are adults . The study was approved by the local ethical boards of the Asian American Liver Centre and the Royal London Hospital and all participants gave written informed consent . Monoclonal antibodies of anti-human-CD3-eFluor605NC ( clone OKT3 ) or –FITC or PE-Cy7 ( UCHT1 ) , anti-CD4-eFluor650NC ( RPA-T4 ) , anti-CD7-FITC ( 4H9 ) , anti-CD161-PerCP-Cy5 . 5 ( HP-3G10 ) , anti-HLA-DR-AlexaFluor700 ( LN3 ) were obtained from eBioscience ( San Diego , CA ) . Anti-CD8-V500 ( RPA-T8 ) , anti-CD14-PE-Cy7 ( M5E2 ) , anti-CD45-V500 ( HI30 ) , anti-CD56-PE-Cy7 ( B159 ) , anti-IFN-γ-V450 ( B27 ) , anti-TNF-α-APC ( 6401 . 1111 ) antibodies were obtained from Becton Dickinson ( BD , San Jose , CA ) . Anti-CD16-APC-Cy7 ( 3G8 ) , anti-CD19-FITC ( HIB19 ) , anti-CD20-FITC ( 2H7 ) , anti-CD56-FITC or APC-Cy7 ( HCD56 ) , anti-Vα7 . 2 TCR-PE or -APC ( 3C10 ) antibodies were obtained from Biolegend ( San Diego , CA ) . Anti-CD161-APC ( 191B8 ) and IFN-γ-FITC ( 45-15 ) antibodies were obtained from Miltenyi Biotec ( UK ) . Anti-TCRγδ-FITC or -PE ( 5A6 . E9 ) antibody and Live/Dead Fixable Dead Cell Stain Kit ( yellow ) were obtained from Invitrogen ( Carlsbad , CA ) . Agonists for human TLR1/2 ( Pam3CSK4; 1 µg/mL ) , TLR2 ( HKLM; 108cells/mL ) , TLR2/6 ( FSL-1; 1 µg/mL ) , TLR3 ( Poly ( I:C ) ; 10 µg/mL ) , TLR4 ( E . coli K12 LPS; 1 µg/mL ) , TLR5 ( S . typhimurium flagellin; 1 µg/mL ) , TLR7 ( Imiquimod; 5 µg/mL ) , TLR8 ( ssRNA40; 1 µg/mL or otherwise stated ) and TLR9 ( CpG ODN2216; 5 µM ) were obtained from Invivogen ( San Diego , CA ) . Anti-CD3/CD28-coupled beads were obtained from Invitrogen ( 1∶1 bead-to-cell ratio ) . Brefeldin A ( 2 µg/mL for overnight incubation or otherwise stated ) , bafilomycin A1 ( 10 nM ) , chloroquine diphosphate ( 5 µg/mL ) , PMA ( 2 ng/mL ) and Ionomycin ( 1 µg/mL ) were obtained from Sigma-Aldrich ( Saint Louis , Missouri ) . LDCs or PBMCs were plated at 100 , 000 cells per well in 96-well plates and stimulated with TLR agonists or anti-CD3/CD28-coupled beads for 18 hours . Sorted monocytes were plated at 150 , 000 cells per well in 96-well plates and stimulated with ssRNA40 for 18 hours . Supernatant was collected subsequently and cytokine concentrations were assessed via cytokine multiplex bead-based assay according to the manufacturer's instructions ( Luminex , Austin , TX ) . ELISA of IL-18 was performed according to the manufacturer's instructions ( R&D Systems ) . The experiment was performed as previously reported [8] . Data was analysed using FACSDiva ( BD ) , FlowJo ( Tree Star ) or Kaluza ( Beckman Coulter ) software . Hepatic MAIT cells were sorted as previously reported [8] . Subsets of hepatic lineage positive ( CD3+CD7+CD56+ ) and antigen-presenting cells ( CD3−CD7−CD56−HLA-DR+ ) were sorted using a BD FACS Aria . Subsets of hepatic APCs were subsequently sorted based on the expression of CD14++ and CD16+ ( monocytes ) , CD20+ ( B cells ) , CD11c+ ( mDCs ) or CD123+ ( pDCs ) . Pan monocytes were depleted from the bulk population using the pan monocyte isolation kit ( Miltenyi Biotec ) . Sorted hepatic lineage positive cells were co-cultured with sorted APCs at an effector-to-target ( E∶T ) ratio of 1∶4 in the presence of ssRNA40 overnight with addition of brefeldin A ( 10 µg/mL ) in the final 5 hours . The intracellular production of IFN-γ was assessed by staining and FACS as above . LDCs or PBMCs were stimulated with ssRNA40 overnight in the presence of anti-MR1 monoclonal antibody at 10 µg/mL ( clone 26 . 5 , kindly provided by Ted . H . Hansen ) . Alternatively , the stimulated cells were cultured in the presence of anti-IL-12p70 monoclonal antibody ( clone 24910 ) or rhIL-18 binding protein ( clone 136007 ) at concentration 20 µg/mL ( R&D Systems ) . Brefeldin A ( 10 µg/mL ) was added in the final 5 hours . The intracellular production of IFN-γ was determined by flow cytometry . The bulk population of liver-derived cells was stimulated overnight with two groups of bacteria , i . e . , the ones that synthesize riboflavin ( Escherichia coli or Pseudomonas aeruginosa ) and the one that does not ( Enterococcus faecalis ) . Two million paraformaldehyde-fixed E . coli or E . faecalis were added for 20 hours in the presence or absence of blocking antibodies against IL-12p70 ( R&D Systems ) and IL-18 ( MBL International , USA ) at concentration 5 µg/mL or against MR1 ( 10 µg/mL ) as indicated . Alternatively , to characterize the early and late activation of MAIT cells , THP1 cell line was used as APCs . To assess the cytokine produced by MAIT cells upon infection with riboflavin-synthesizing bacteria , viable P . aeruginosa was added overnight at MOI 10 per monocyte . Brefeldin A ( 10 µg/mL ) was added for the final 4–5 hours of culture . Intracellular cytokine staining was performed to detect production of IFN-γ and TNF-α by MAIT cells . The nonparametric Mann-Whitney U test was used to determine the statistical significance of differences .
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The ability of human pathogens , like HBV , HCV or Plasmodium spp . to infect the liver might be influenced by its tolerogenic features . However , hepatic tolerance is not absolute since protective immunity can be triggered . Our goal was to define how to deliberately elicit an intrahepatic protective immune response . To achieve this , we purified immune cells residing in the vascular bed of human livers and we probed their reactivity against different pathogen-associated molecules , mimicking signature components of viruses or bacteria . We found that robust production of anti-viral cytokine IFN-γ was induced only by the TLR8 agonist ssRNA40 . Mechanistically , ssRNA40 triggered hepatic monocytes to produce IL-12 and IL-18 cytokines , which stimulated IFN-γ production by liver-resident CD161Bright MAIT and CD56Bright NK cells . We also demonstrated that ssRNA40-mediated activation could occur in pathologic ( HBV- or HCV-chronically infected ) livers and that a similar cytokine-mediated activation of intrahepatic cells could also be triggered upon bacterial infection . Thus , we showed that the liver immune cells can respond vigorously to specific pathogen-associated molecules . The selective production of IFN-γ by liver-resident cells could have therapeutic implications for the treatment of chronic liver infections .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
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[
"infectious",
"diseases",
"immunology",
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2014
|
Toll-Like Receptor 8 Agonist and Bacteria Trigger Potent Activation of Innate Immune Cells in Human Liver
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Vector arthropods control arbovirus replication and spread through antiviral innate immune responses including RNA interference ( RNAi ) pathways . Arbovirus infections have been shown to induce the exogenous small interfering RNA ( siRNA ) and Piwi-interacting RNA ( piRNA ) pathways , but direct antiviral activity by these host responses in mosquito cells has only been demonstrated against a limited number of positive-strand RNA arboviruses . For bunyaviruses in general , the relative contribution of small RNA pathways in antiviral defences is unknown . The genus Orthobunyavirus in the Bunyaviridae family harbours a diverse range of mosquito- , midge- and tick-borne arboviruses . We hypothesized that differences in the antiviral RNAi response in vector versus non-vector cells may exist and that could influence viral host range . Using Aedes aegypti-derived mosquito cells , mosquito-borne orthobunyaviruses and midge-borne orthobunyaviruses we showed that bunyavirus infection commonly induced the production of small RNAs and the effects of the small RNA pathways on individual viruses differ in specific vector-arbovirus interactions . These findings have important implications for our understanding of antiviral RNAi pathways and orthobunyavirus-vector interactions and tropism .
Orthobunyaviruses are endemic in tropical and subtropical regions worldwide and are transmitted by mosquitoes , midges , ticks or other arthropods . The Orthobunyavirus genus within the Bunyaviridae family comprises at least 30 viruses that can cause disease in humans , including Oropouche virus ( OROV; febrile illness ) , La Crosse virus ( LACV; encephalitis ) and Ngari virus ( haemorrhagic fever ) [1] . In addition , infection by orthobunyaviruses such as Cache Valley virus ( CVV ) and Schmallenberg virus ( SBV ) can lead to disease in animals [2] . Bunyamwera virus ( BUNV ) is the prototype virus of both the Orthobunyavirus genus and the family . Like most viruses in the genus , the BUNV genome possesses a tripartite , single-stranded negative sense RNA genome , in which the small ( S ) segment encodes the nucleocapsid ( N ) protein and the nonstructural protein NSs in overlapping reading frames , the medium ( M ) segment encodes a viral glycoprotein precursor ( in the order Gn-NSm-Gc ) for two envelope glycoproteins Gn , Gc and a nonstructural protein NSm , and the large ( L ) segment encodes the RNA-dependent RNA polymerase . This genome structure is generally reflected by most orthobunyaviruses with some differences for example in the presence or length of NSs [1] . BUNV was originally isolated from Aedes spec . mosquitoes in the Semliki Forest in Uganda in 1943 [3] but has since also been found in Culex spec . and Mansonia spec . ( see [4] and references on BUNV therein ) and Ochlerotatus spec . [5] . BUNV infections cause febrile illness and ( rarely ) encephalitis in humans in Sub-Saharan Africa , in particular in Nigeria and the Central African Republic , with wild rodents likely to be serving as amplifying reservoir [6] . Cache Valley virus ( CVV ) belongs to the Bunyamwera serogroup and is enzootic throughout North and South America [7 , 8] . It was first isolated from Culiseta inornata mosquitoes in the Cache Valley in Utah , United States of America in 1956 [8] , and has since been shown to be transmitted by mosquitoes of the Culiseta , Anopheles , Aedes , Culex and Ochleratatus genera [9 , 10] . A small number of CVV infections in humans have been reported [11–13] , where infection rarely leads to serious disease . In ruminants , including sheep and cattle , CVV causes spontaneous abortions and multiple congenital malformations [14–16] . Large mammals including deer , horses and sheep are known to serve as amplifying hosts [6] . In addition to mosquito-borne orthobunyaviruses some members of the genus are exclusively transmitted by biting midges ( e . g . SBV , OROV and Sathuperi virus [SATV] ) [17–22] , ticks ( Tete group viruses Bahig and Matruh ) [23] or are mosquito/insect-specific [24 , 25] . SBV and SATV are closely related orthobunyaviruses of the Simbu serogroup . SBV was first discovered in 2011 in Germany and the Netherlands [26] and infections have since been reported in many European countries [27] . Infections can lead to reduced milk yield , fever , fetal malformations and abortions in ruminants ( primarily sheep , goats and cattle ) [26 , 28]; human infections have not been reported . SATV was isolated from mosquitoes in India in 1957 [29] , and was later detected in cattle and biting midges in Nigeria [30 , 31] . More recently , SATV was detected in Japan in 1999 [32] . To date little information is available on its pathogenicity in ruminants [22] . Both SBV and SATV are transmitted by Culicoides sp . biting midges [22 , 31 , 33–35] . The exogenous small interfering ( exo-si ) RNA and Piwi-interacting ( pi ) RNA pathways have previously been described as important mosquito antiviral responses limiting the replication of positive-strand RNA flaviviruses and togaviruses [36 , 37] . The activity of the exo-siRNA pathway is mediated by two key proteins , the endoribonuclease Dicer-2 ( Dcr2 ) and Argonaute-2 ( Ago2 ) . Dcr2 cleaves long virus-derived double-stranded RNAs ( dsRNAs ) into 21 nucleotides ( nt ) long small interfering RNA ( viRNA ) duplexes . These viRNAs are then incorporated into the multiprotein RNA-induced silencing complex ( RISC ) , where presumably one strand is retained ( guide strand ) by Ago2 to detect , bind and catalyze the degradation of complementary single-stranded ( ss ) RNA such as viral mRNA . Indeed , 21 nt viRNAs were produced in mosquitoes or mosquito-derived cell lines upon infection with several arboviruses , for example flaviviruses ( dengue virus , DENV; West Nile virus , WNV ) , alphaviruses of the Togaviridae family ( chikungunya virus , CHIKV; Semliki Forest virus , SFV; Sindbis virus , SINV; o’nyong’nyong virus , ONNV ) , bunyaviruses ( Rift Valley fever virus , RVFV; and SBV ) as well as reoviruses ( bluetongue virus , BTV ) [38–42] . Silencing experiments in mosquitoes have confirmed the antiviral activity of the exo-siRNA pathway against DENV , SINV , CHIKV and ONNV in vivo [42–46] . In addition to the exo-siRNA pathway , the piRNA pathway has been shown to be a contributor to antiviral immunity in mosquitoes or derived cells [38 , 47] . In Drosophila , the piRNA pathway is involved in the epigenetic regulation of the expression of transposable elements ( TE ) in the germline , and thus preserves the genome integrity [48–51] . However , PIWI proteins have also been detected in somatic cells [49 , 52–54] . The production of transposon-specific piRNAs is complex and relies on proteins of PIWI clade , in particular Piwi , Aubergine ( Aub ) and Argonaute-3 ( Ago3 ) . piRNAs are believed to be generated via a primary processing pathway and a secondary ping-pong amplification loop [51 , 52] . Primary piRNAs have a 5’ uridine ( U1 ) bias . Secondary piRNAs have a 10 nt overlap with primary piRNAs and contain an adenine at position 10 ( A10 bias ) . Mature piRNAs are generally 26–32 nt in length . The piRNA pathway is functional in mosquito germline and somatic cells [38 , 39 , 47 , 55–57] . Interestingly , in mosquitoes a loss of Aub and a diversification of Piwi proteins has occurred [58] . This gene diversification has been linked with a gain of function of the piRNA pathway and a new role in antiviral immunity since the pathway has also been found to target a number of mosquito-borne viruses including DENV , CHIKV , SFV and RVFV [38 , 39 , 47] . Further , in the Ae . aegypti-derived Aag2 cell line an antiviral effect of Piwi4 against SFV has been directly demonstrated [47] . Recently it was shown that Ago3 and Piwi5 ( and Piwi6 to a lesser extent ) are needed for the generation of SINV and DENV specific piRNAs in Aag2 cells [59 , 60]; however , the viral RNA substrate that induces this pathway is unknown . Importantly , the respective contribution of the two small RNA pathways to immune defenses against negative-sense RNA arboviruses has not been studied . In short , little is known about the interactions of viruses and vectors , and antiviral responses of vectors , which may govern viral infection , dissemination and transmission . In this study we investigated the antiviral activities of the mosquito exo-siRNA and piRNA pathway against two mosquito- and three midge-borne orthobunyaviruses . Using reporter BUNV and SBV that express Nano luciferase we compared these responses in vector-virus and non-vector-virus interactions . We performed small RNA sequencing and showed that mosquito as well as midge-borne viruses produce virus-specific siRNAs and piRNAs in mosquito cells . Interestingly we found that silencing of Ago2 and Piwi4 in Aag2 cells led to increased viral replication of mosquito-borne orthobunyaviruses , in contrast to midge-borne orthobunyaviruses where only Ago2 silencing increased virus replication . Additionally , silencing of other piRNA pathway members ( Piwi5 , 6 and Ago3 ) affected virus replication differently depending on whether the virus was mosquito- or midge borne . These findings indicate that RNAi pathways play a crucial role in the control of orthobunyavirus replication; however , the piRNA pathway in Aag2 cells seems to be adapted to specific virus-vector combinations and may have important consequences for arbovirus tropism and vector specificity .
Our previous work has shown that small RNAs with viRNA and piRNA characteristics are produced in non-vector mosquito cells infected with the midge-borne SBV [40] ( S1A Fig ) . To determine if this is similar for a mosquito-borne virus , Ae . aegypti-derived Aag2 cells were infected with BUNV ( Fig 1A and 1B ) and small RNAs were isolated , sequenced and mapped to the virus genome and antigenome . As shown in Figs 1C and 2A , 21 nt long small RNAs were produced from all three segments which mapped along the genome and antigenome in a cold and hot spot pattern . For the L segment , these 21 nts viRNAs were the major small RNA species produced . Moreover , small RNAs of 24–30 nts with piRNA-specific features ( A10 , U1 bias ) , which mapped across the genome and antigenome in a hot and cold spot pattern ( Fig 2B ) , were produced for all three segments ( Fig 2C ) ; however , they were the major small RNA species only for M and S segments with a bias for small RNAs mapping to the antigenome . In contrast , 24–30 nt small RNAs mapping to the L segment had a bias for the genome ( Fig 1C ) . Similar results were obtained in BUNV-infected Ae . albopictus-derived U4 . 4 cells ( S2 Fig ) . To determine if similar small RNAs are produced for BUNV and SBV in midge cells; experiments were repeated with infected C . sonorensis KC cells . As previously reported no piRNA-like molecules could be detected for SBV in KC cells , in contrast , 21 nt small RNAs were mapped to the genome and antigenome of all three segments [40] ( S1B Fig ) . Similar results were observed for BUNV small RNAs in infected KC cells ( Fig 1D ) . Although small RNAs of 24–28 nts could be detected in KC cells , they lacked the piRNA-specific features ( A10 , U1 bias and 10 nts overlap of sense and antisense small RNAs ) ( Fig 1D ) . Overall , our data showed that BUNV infection induced small RNA patterns comparable with SBV [40] in mosquito-derived cells as well as Culicoides-derived cells . In addition , differences in small RNA patterns were found between mosquito and midge cells . Previously , luciferase expressing alphaviruses were employed to investigate the antiviral RNAi response in arthropod cells [47 , 61 , 62] . To obtain similar tools for bunyaviruses , Nano luciferase ( NL ) expressing BUNV ( BUNV-NL ) and SBV ( SBV-NL ) were constructed in the course of this study . BUNV NSm protein , which is dispensable in viral replication in tissue culture , consists of the ectodomain , transmembrane , cytoplasmic domain and type-II transmembrane domain that also serves as internal Gc signal peptide [63 , 64] . For BUNV-NL , the 62 residues of the NSm cytoplasmic domain ( residues 395 to 455 ) was replaced by the NL coding region between residues 394 and 456 , resulting in a chimeric NSm-NL fusion protein ( S3A Fig ) . NSm-NL has a similar molecular weight to that of N protein ( 28 . 67 versus 26 . 67 kDa ) and was indistinguishable from the N protein band in the protein profile of the reporter virus ( S3B Fig ) . BUNV-NL exhibited smaller plaque size than the wildtype virus ( S3C Fig ) . The reporter virus could readily infect Aag2 cells , similar to wildtype virus ( S3D Fig ) . The SBV-NL reporter virus was constructed in the same way as BUNV-NL and successful infection of Aag2 cells , comparable to wildtype SBV , was verified by immunostaining ( S3D Fig ) . To assess the antiviral role of the small RNAi pathways in BUNV and SBV infected Aag2 mosquito cells , knockdown experiments were performed . Transcripts of the different RNAi pathway key effectors ( Ago2 , exo-siRNA pathway; Ago1 , miRNA pathway; Piwi4-6 and Ago3 , piRNA pathway ) were silenced by transfection of sequence-specific dsRNA . The effect of the silencing was evaluated by BUNV-NL or SBV-NL infection at 24 hours post-transfection ( p . t . ) at low MOI ( 0 . 01 ) and subsequent luciferase detection at 48 hours p . i . ( Figs 3A , 3B , 4A and 4B ) ; dsRNA specific to eGFP was used as negative control . Silencing of Ago2 led to an increase in luciferase expression for both BUNV-NL and SBV-NL ( Fig 3A and 3B ) . In contrast , silencing of Piwi4 had no effect on SBV-NL , but resulted in an increase of BUNV-NL replication . Interestingly , silencing expression of other piRNA pathway related genes resulted in a significant decrease of BUNV-NL ( Piwi6 and Ago3 knockdowns ) and SBV-NL ( Piwi5 knockdown ) replication ( Fig 4A and 4B ) . A decrease of luciferase expression was also observed for BUNV-NL following Ago1 silencing , compared to an increase of luciferase expression for SBV-NL ( Fig 3A and 3B ) . These results suggested differences in the ability of the piRNA and miRNA pathway to interact with BUNV and SBV . To determine if this was virus-specific or due to vector versus non-vector arbovirus interaction , silencing experiments were repeated with the mosquito-borne Cache Valley orthobunyavirus ( CVV ) as well as the midge-borne Sathuperi ( SATV ) orthobunyaviruses after their ability to grow in Aag2 cells was verified ( S4A Fig ) . Similar to BUNV , silencing of Ago2 or Piwi4 promoted CVV replication , whereas Ago1 knockdown reduced CVV replication ( Fig 3C ) . Moreover , CVV-infected , Piwi4-silenced Aag2 cells showed cytopathic effects not observed for any of the other knockdown experiments with this virus ( S4B Fig ) . For midge-borne SATV , an increase in replication was observed in cells silenced for Ago2 and Ago1 . However , no significant effect on SATV replication was observed in cells treated with Piwi4 dsRNA ( Fig 3C ) . Successful silencing of transcripts by sequence specific dsRNAs was verified by quantitative RT-PCR ( Figs 3D and 4C ) . In short , Ago2 silencing led to a consistent increase in virus replication for mosquito and midge-borne orthobunyaviruses , supporting an antiviral activity of the exo-siRNA pathway . Similar effects were observed for Piwi4 silencing in the case of mosquito-borne viruses , but not midge-borne viruses . Silencing of Ago1 resulted in a decrease of replication of the tested mosquito-borne viruses , suggesting an importance of the miRNA pathway for a successful infection in Aag2 cells for these viruses . In contrast , silencing of Ago1 resulted in an increase of SBV and SATV , albeit only slightly significant .
The RNAi response is a major antiviral response in arthropods against arbovirus infection . The activated exo-siRNA pathway plays a role in a variety of organisms , including mosquitoes and the model insect D . melanogaster . In contrast , the antiviral activity of the piRNA pathway and the production of viral specific piRNA molecules have been restricted to mosquitoes , especially Aedes spp . Besides , interactions between the miRNA pathway and viruses have been reported in several organisms [65 , 66] , acting either pro- or antiviral . This can be by expression changes of vector/host miRNAs or viral encoded miRNAs which can either directly target the virus or have host/vector targets , resulting in changes of the cell environment . Previous research has often used D . melanogaster to investigate the interaction between the insect RNAi response and different viruses including arboviruses; however little is known about the specificity of the antiviral response in vector- or non-vector arbovirus interactions . Knockdown experiments of key proteins of the exo-siRNA , miRNA and piRNA pathway in mosquito cells and orthobunyavirus infection , either mosquito- or midge-borne , supports the broad antiviral activity of the exo-siRNA pathway , based on an observed increase in virus replication upon Ago2 silencing for all viruses used . In contrast , the miRNA pathway seemed to be only important for virus replication in the case of a virus-vector match . miRNA expression is often species or even tissue specific and some miRNA-arbovirus interactions have been reported [67] . Whether a similar interaction is important for BUNV and CVV infection in mosquitoes has to be investigated in the future . Infections with both mosquito-borne and midge-borne viruses were able to induce viral specific piRNAs in the used mosquito cell line . However , the antiviral activity of the piRNA pathway was only confirmed for the mosquito-borne viruses in the Piwi4 knockdown cells . Interestingly knockdown of other piRNA pathway members indicated that they may have pro-viral activities , knockdown of Piwi6 and Ago3 had replication suppressive effects on the mosquito-borne BUNV while knockdown of these proteins had no effect on the midge-borne SBV , whereas knockdown of Piwi5 reduced SBV replication but not BUNV replication . The meaning and biological relevance of these observations is not yet clear . Co-silencing/infection experiments and sequencing of virus-derived small RNAs from such cells may give clues to how different Piwi proteins interact , their role in piRNA-like small RNA production and potentially a hierarchy of protein effector and regulatory functions within this pathway . Perhaps , similar to other RNA binding proteins some of these proteins are important for promoting virus replication though this would require in depth analysis of RNA protein interactions and/or protein-protein interaction studies . Little is known about the piRNA pathway in mosquitoes , the production of virus specific piRNAs and how/if they target the virus . Recently , it has been shown that Piwi5 ( and to a lesser extent Piwi6 ) and Ago3 are needed for virus-derived piRNA production [59 , 60]; however , no antiviral activity has previously been linked to these transcripts . In contrast , Piwi4 seems not to be involved in virus-derived piRNA production [59]; however knockdown of Piwi4 can result in antiviral activity for mosquito-borne viruses , like SFV [47] . Similar antiviral activity of Piwi4 is observed for the mosquito-borne orthobunyaviruses: CVV and BUNV , but not for midge-borne orthobunyaviruses ( SBV and SATV ) . This is especially striking as both the mosquito-borne BUNV as well as midge-borne SBV produce similar amounts of virus-specific piRNA molecules in infected Aag2 cells . This would suggest that the antiviral activity of Piwi4 is species specific and either acts as an effector protein downstream of the virus specific-piRNA production or through a different as yet unidentified pathway . Interestingly , no viral specific piRNAs have been reported in midges so far; although this has only been investigated in the C . sonorensis-derived KC cell line and not whole midges [40] . Interestingly , it has been shown that infection of mosquitoes and mosquito-derived cultured cells with arboviruses can lead to generation of virus derived cDNA forms , which are important for mosquito tolerance to virus infection and survival [68] . These DNA forms have the potential to become the template for small RNA production and could therefore determine the action of RNAi pathways on acute arbovirus infection . If the generation of cDNA forms in mosquitoes and derived cells is mosquito-borne virus specific ( not occurring with e . g . midge borne viruses ) is not known , but could explain the observed species specific antiviral activity of Piwi4 . Overall , these results show the importance to investigate the antiviral RNAi response in vector cells to understand the complex interaction between virus-vector interplay and its effect on tropism .
BSR-T7/5 cells [derived from the BSR clone of baby hamster kidney cells-21 [BHK-21] and stably expressing T7 RNA polymerase [69]; a kind gift of Dr . K . K . Conzelmann , Max-von-Pettenkofer Institut , Munich , Germany] were maintained in Glasgow minimal essential medium ( GMEM ) supplemented with 10% tryptose phosphate broth ( TPB ) , 10% fetal calf serum ( FCS ) , and 1 mg/mL geneticin . BHK-21 cells were maintained in GMEM supplemented with 10% TPB , 10% newborn calf serum ( NCS ) and 1% penicillin/streptomycin ( P/S ) . Sheep choroid plexus cells ( CPT-Tert ) [70] were grown in Iscove's modified Dulbecco's media ( IMDM ) supplemented with 10% FCS and 1% P/S . Vero E6 cells were grown in Dulbecco’s modified Eagle’s medium ( DMEM ) and 10% FCS . BSR-T7/5 , BHK-21 , CPT-Tert and Vero E6 cells were grown at 37°C and 5% CO2 . Ae . aegypti-derived Aag2 cells were maintained in L-15 medium supplemented with 10% TBP , 10% FCS and 1% P/S at 28°C . Plasmids to generate full-length BUNV antigenome RNA transcripts [pT7riboBUNL ( + ) , pTVT7RBUNM ( + ) , and pT7riboBUNS ( + ) ] have been described previously [71 , 72] . pTVT7RBUNM-NL was generated by PCR-directed internal deletion to replace the coding region of the NSm cytoplasmic tail ( residues 395 to 456 ) with that of Nano luciferase ( NL ) . A five amino acid linker , GASGA , was inserted between the NSm transmembrane domain ( TMD ) and the N-terminus of NL . To facilitate the cloning of NL , a unique KpnI restriction enzyme site was introduced at nt 1419 in the BUNV M segment cDNA ( S3A Fig ) . The plasmids , pUCSBVST7 , pUCSBVMT7 and pUCSBVLT7 , used to rescue SBV have been described previously [73] . pUCSBVT7-NL was generated through the introduction of two unique restriction sites , MIuI and XhoI , in the SBV M segment ( provided by M . Varela; University of Glasgow ) . The restrictions sites were used to delete 90 nt of NSm ( corresponding to nt 1235–1325 in JX853180 . 1 ) and NL was subsequently cloned in the deletion site . NL is a small luciferase subunit ( 19 kDa ) from the deep sea shrimp Oplophorus gracilirostris with significantly increased luminescence expression and signal half-life as well as specific activity in mammalian cells compared to both Firefly and Renilla luciferases . NL uses a novel imidazopyrazinone substrate ( furimazine ) [74] . Rescue experiments were performed as essentially described previously , with a modification [75] . Briefly , BSR-T7/5 cells were transfected with a mixture of plasmids comprising 0 . 5 μg each of pT7riboBUNL ( + ) , pT7riboBUNS ( + ) and either TVT7RBUNM ( + ) or TVT7RBUNM-NLuc cDNA . At 4 hours p . t . , 2 ml growth medium was added and incubation continued for 5–11 days at 33°C until cytopathic effect ( CPE ) was evident . Virus titre was determined by plaque assay on BSR-T7/5 cells . Cells were fixed in 4% formaldehyde and plaques stained in 0 . 01% toluidine blue . The rescue of SBV-NL was performed using pUCSBVST7 , pUCSBVMT7-NL and pUCSBVLT7 as described for BUNV but with the exception that 1 μg of each plasmid was used and that the plaque assay was performed using BHK-21 cells . BUNV , BUNV-NL , SBV , SBV-NL , CVV and SATV stocks were grown in BHK-21 cells . Cells were infected with viruses at a multiplicity of infection ( MOI ) of 0 . 01 PFU/cell and incubated at 33°C . Virus-containing cell supernatant was harvested when CPE was evident ( usually 2–4 days p . i . ) , cleared by centrifugation and stored at -80°C . Virus titres of BUNV and CVV were determined by plaque assay on BHK-21 cells and those of SBV and SATV on CPT-Tert cells . Procedures for metabolic radiolabelling and immunoprecipitation of BUNV proteins were described previously [76] . Briefly , at 24 hours p . i . , BSR-T7/5 cells were labelled with [35S]methionine ( 50 Ci ) for 2 hours and then lysed , on ice , in 300 μl of non-denaturing RIPA buffer ( 50 mM Tris-HCl [pH7 . 4] , 1% Triton X-100 , 300 mM NaCl , 5 mM EDTA ) containing a cocktail of protease inhibitors ( Roche ) . For immunoprecipitation assays , BUNV viral proteins were immunoprecipitated with anti-BUNV antibody , a rabbit antisera raised against purified BUNV [77] that had been conjugated to magnetic Protein A-Dynabeads ( Life Technologies ) . Viral proteins were analysed by SDS-PAGE under reducing conditions . The plaque phenotype of BUNV and BUNV-NL in BSR cells ( routinely grown in GMEM supplemented with 10% foetal calf serum at 37°C in a 5% CO2 incubator ) was investigated by plaque assay . Cells were seeded into 12-well plates at a density of 1 . 2 x 105 cells/well and left to adhere overnight . Cells were infected with BUNV or BUNV-NL at a MOI of 0 . 05 and cells were fixed with 4% formaldehyde-PBS and stained with 0 . 1% crystal violet blue solution at 3 days post infection . Growth of BUNV , SBV , CVV and SATV in Aag2 cells was assessed . Briefly , Aag2 cells were seeded into 24-well plates at a density of 1 . 7 x 105 cells/well and left to adhere overnight . Cells were then infected with viruses at MOI 1 ( BUNV , CVV ) or MOI 0 . 01 ( SBV , SATV ) and culture supernatant was harvested at different time points p . i . Viral titres were determined by plaque assays on BHK-21 cells ( BUNV , CVV ) or CPT-Tert cells ( SBV , SATV ) . Aag2 cells were seeded into 24-well glass bottom plates at a density of 1 . 7 x 105 cells/well and infected with BUNV , BUNV-NL , SBV or SBV-NL at a MOI of 0 . 01 for 48 hours . Cells were fixed and stained with anti-BUNV N or–SBV N antibody [78 , 79] . Goat anti-rabbit Alexa Fluor 488 antibody ( Molecular Probes ) was used to detect primary antibodies and cells were mounted using Vectashield mounting media containing DAPI ( Vectorlabs ) . Images were taken using the EVOS FL Cell Imaging System . Small RNA sequencing of BUNV-infected Aag2 and U4 . 4 cells was carried out by Edinburgh Genomics ( University of Edinburgh ) using the Illumina HighSeq 2000 platform , as previously described [40] . In short , 2 . 6 x 106 Aag2 cells/well were seeded into a 6-well plate and left to adhere overnight . Cells were infected with BUNV at a MOI of 10 . At 24 hours p . i . , RNA was isolated from individual wells using 1 ml TRIzol ( Life Technologies ) followed by purification , sequencing , analysis and mapping of virus specific small RNAs using viRome [80] . Small RNA sequences were submitted to the European Nucleotide Archive ( accession number PRJEB15203 ) . Data for SBV are referenced under [40] . The actual number of reads for each experiment is shown S1 Table . Silencing of Ae . aegypti Ago2 , Piwi4 , and Ago1 was performed in Aag2 cells using dsRNAs as previously described [81] . dsRNA targeting eGFP was used as control . Silencing of transcripts was confirmed by qRT-PCR using Fast SYBR Green Master Mix ( Applied Biosystems ) and the corresponding primers [S2 Table; [81]]; on an ABI 7500 Fast real-time PCR instrument . Ae . aegypti S7 ribosomal transcript was used as housekeeping gene for relative quantification as previously described [81] . At 24 hours p . t . , cells were infected with BUNV-NL , SBV-NL , CVV or SATV at a MOI of 0 . 01 . At 48 hours p . i . cells were either lysed in passive lysis buffer and NL luminescence was measured ( BUNV-NL and SBV-NL ) or RNA was isolated using TRIzol ( CVV and SATV ) , followed by cDNA production and qRT-PCR analysis . cDNA synthesis was performed using random hexamer primers ( Promega ) and SuperScript III reverse transcriptase ( Life Technologies ) . qRT-PCR was performed using Fast SYBR Green Master Mix using corresponding primers ( S2 Table ) . Ae . aegypti S7 ribosomal transcript was used as housekeeping gene .
|
A number of orthobunyaviruses such as Oropouche virus , La Crosse virus and Schmallenberg virus are important global human or animal pathogens transmitted by arthropod vectors . Further understanding of the antiviral control mechanisms in arthropod vectors is key to developing novel prevention strategies based on preventing transmission . Antiviral small RNA pathways such as the exogenous siRNA and piRNA pathways have been shown to mediate antiviral activity against positive-strand RNA arboviruses , but information about their activities against negative-strand RNA arboviruses is critically lacking . Here we show that in Aedes aegypti-derived mosquito cells , the antiviral responses to mosquito-borne orthobunyaviruses is largely mediated by both siRNA and piRNA pathways , whereas the piRNA pathway plays only a minor role in controlling midge-borne orthobunyaviruses . This suggests that vector specificity is in part controlled by antiviral responses that depend on the host species . These findings contribute significantly to our understanding of arbovirus-vector interactions .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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] |
2017
|
The Antiviral RNAi Response in Vector and Non-vector Cells against Orthobunyaviruses
|
Species often encounter , and adapt to , many patches of similar environmental conditions across their range . Such adaptation can occur through convergent evolution if different alleles arise in different patches , or through the spread of shared alleles by migration acting to synchronize adaptation across the species . The tension between the two reflects the constraint imposed on evolution by the underlying genetic architecture versus how effectively selection and geographic isolation act to inhibit the geographic spread of locally adapted alleles . This paper studies the balance between these two routes to adaptation in a model of continuous environments with patchy selection pressures . We address the following questions: How long does it take for a novel allele to appear in a patch where it is locally adapted through mutation ? Or , through migration from another , already adapted patch ? Which is more likely to occur , as a function of distance between the patches ? What population genetic signal is left by the spread of migrant alleles ? To answer these questions we examine the family structure underlying migration–selection equilibrium surrounding an already adapted patch , treating those rare families that reach new patches as spatial branching processes . A main result is that patches further apart than a critical distance will likely evolve independent locally adapted alleles; this distance is proportional to the spatial scale of selection ( σ / s m , where σ is the dispersal distance and sm is the selective disadvantage of these alleles between patches ) , and depends linearly on log ( sm/μ ) , where μ is the mutation rate . This provides a way to understand the role of geographic separation between patches in promoting convergent adaptation and the genomic signals it leaves behind . We illustrate these ideas using the convergent evolution of cryptic coloration in the rock pocket mouse , Chaetodipus intermedius , as an empirical example .
The convergent evolution of similar phenotypes in response to similar selection pressures is a testament to the power that selection has to sculpt phenotypic variation . In some cases this convergence extends to the molecular level , with disparate taxa converging to the same phenotype through parallel genetic changes in the same pathway , genes , or even by precisely the same genetic changes [1–3] . Convergent adaptation also occurs within species , if different individuals adapt to the same environment through different genetic changes . There are a growing number of examples of this in a range of well studied organisms and phenotypes [4] . Such evolution of convergent phenotypes is favored by higher mutational input , i . e . , higher total mutational rate and/or population size [5] . The geographic distribution of populations can also affect the probability of parallel mutation within a species: a widespread species is more likely to adapt by multiple , parallel mutations if dispersal is geographically limited , since subpopulations will adapt via new mutations before the adaptive allele arrives via migration [6] . Standing variation for a trait can also increase the probability of convergence , as this increases the probability that the selective sweep will be soft ( beginning from a base of multiple copies ) , which leads to genetic patterns similar to convergent adaptation [7 , 8] whether or not the copies derive from the same mutation , Intuitively , convergence is also more likely when geographically separated populations adapt to ecologically similar conditions . The probability that convergent adaptations arise independently before adaptations spread between the populations by migration will be larger if these adaptive alleles are maladapted in intervening environments , since such adverse conditions can strongly restrict the spread of locally adapted alleles [9] . One elegant set of such examples is provided by the assortment of plant species that have repeatedly adapted to patches of soil with high concentrations of heavy metals ( e . g . , serpentine outcrops and mine tailings ) [10–13]; the alleles conferring heavy metal tolerance are thought to be locally restricted because they incur a cost off of these patches . Similarly , across the American Southwest , a variety of species of animals have developed locally adaptive cryptic coloration to particular substrates , e . g . , dark rock outcrops or white sand dunes [14] . One of the best-known examples is the rock pocket mouse ( Chaetodipus intermedius ) , which on a number of black lava flows has much darker pelage than on intervening areas of lighter rock [15] . Strong predator-mediated selection appears to favour such crypsis [16] , and , perhaps as a result of this strong selection against migrants , at least two distinct genetic changes are responsible for the dark pigmentation found on different outcrops [17] . Similar situations have been demonstrated in other small rodent systems [18–20] and in lizards [21] . In this paper , we study this situation , namely , when a set of alleles provide an adaptive benefit in geographically localized patches , separated by inhabited regions where the alleles are deleterious . The main questions are: Under what conditions is it likely that each patch adapts in parallel , i . e . , convergently through new mutation , and when is it likely that migration carries these alleles between patches ? How easy will it be to detect adaptive alleles that are shared by migration , i . e . , over what genomic scale will a population genetic signal be visible ? We work in a model of continuous geography , using a variety of known results and new methods . In the section Establishment of a locally adaptive allele due to mutational influx we develop a simple approximation , Eq ( 2 ) , for the rate at which patches become established by new mutations . The most important conceptual work of the paper occurs in the section Establishment of a locally adaptive allele due to migrational influx , where we develop an understanding of the process by which alleles move from an existing patch to colonize a novel patch , culminating in Eq ( 11 ) for the rate at which this happens . We combine these two results in the section The probability of parallel adaptation between patches to discuss the probability of parallel adaptation , Eq ( 12 ) . To understand the genomic signal left by adaptations shared by migration , in the section Length of the hitchhiking haplotype , we study the time it will take for an allele to transit between patches , ( Eq ( 18 ) ) , and thus the length of haplotype that hitchhikes with it ( Eq ( 19 ) ) . Finally , in the section Applications we apply this work to understand the geographic scale of convergent evolution in Chaetodipus intermedius .
Consider a population spread across a landscape to which it is generally well adapted , but within which are patches of habitat to which individuals are ( initially ) poorly adapted . ( When we refer to “patches” it is to these pieces of poor habitat . ) Suppose it takes only a single mutational change to create an allele ( B ) that adapts an individual to the poor habitat type . The required change could be as specific as a single base change , or as general as a knockout of a gene or one of a set of genes . This change occurs at a ( low ) rate of μ per chromosome per generation , and has fitness advantage sp relative to the unmutated type ( b ) in these “poor” habitat patches . Outside of these patches the new allele has a fitness disadvantage of sm in the intervening areas , with sp and sm both positive . ( Here we define “fitness” to be the intrinsic growth rate of the type when rare . ) We assume that the disadvantage sm is sufficiently large that , on the relevant timescale , the allele is very unlikely to fix locally in these intervening regions . ( The case where sm = 0 requires a different approach , which we do not treat here . ) We are interested in the establishment of mutations in the “poor” patches by either migration or mutation , which depends on whether the allele can escape initial loss by drift when rare . Therefore , we need not specify the fitness of the homozygote; only that the dynamics of the allele when rare are determined by the fitness of the heterozygote . More general dominance will only make small corrections to the dynamics until initial fixation , with the exception of the recessive case , which we omit . In other words , we follow the literature in treating the diploid model as essentially haploid . We also assume population density ρ is constant across the range ( even in the “poor” patches ) . The variance in offspring number is ξ2 , and that the mean squared distance between parent and child is σ2 ( i . e . , σ is the dispersal distance ) . We will deal with migration by immediately appealing to the central limit theorem , treating the total distance traveled across t dispersal events as Gaussian with variance tσ2 , and do not discuss the ( interesting ) cases where rare , long-distance dispersal events are more important ( see e . g . , [6 , 22 , 23] for discussion ) . We first compute the time scale on which a new B mutations will appear and fix in a single , isolated poor habitat patch in which no B allele has yet become established . As we are interested in patches where local adaptation can occur , we will assume that the patch is larger than the cutoff for local establishment mentioned above . Let p ( x ) be the probability that a new mutant B allele that arises at location x relative to the center of the patch fixes within the poor habitat patch . Under various assumptions , precise expressions can be found for p ( x ) [29] , but results will be more interpretable if we proceed with a simple approximation . The total influx per generation of mutations that successfully establish is the product of population density ρ , mutation rate μ , and the integral of p ( x ) over the entire species range: λ mut = ρ μ ∫ p ( x ) d x . ( 1 ) This depends in a complicated way on the patch geometry and selection coefficients , but still scales linearly with the mutational influx density ρμ . If we consider a patch of area A , whose width is large relative to σ / 2 s m , then a reasonable approximation is to ignore migration , so that p ( x ) = pe ≈ 2sp/ξ2 within the patch , and p ( x ) = 0 otherwise . This approximates the integrand p ( x ) in Eq ( 1 ) by a step function , which will be a good approximation if the patch is large relative the scale over which p ( x ) goes from 0 to pe , or if p ( x ) is close to pe at some point and is symmetric about the edge of the patch . We examine this more generally via exact calculation of p ( x ) in the section Numerical calculation of the probability of establishment . The rate at which mutations arise and colonize a patch of area A is then λ mut ≈ 2 s p ρ A μ / ξ 2 , ( 2 ) i . e . , the product of mutational influx in the patch ( ρAμ ) and the probability of establishment ( pe ≈ 2sp/ξ2 ) . If this rate is low , then the time ( in generations ) until a mutation arises that will become locally established within the patch is exponentially distributed with mean 1/λmut . Assuming that once a mutation becomes established it quickly reaches its equilibrium frequency across the patch , the time scale on which new patches become colonized by the B allele from new mutation is therefore approximately 1/λmut . Now suppose that there are two patches separated by distance R ( i . e . , the shortest distance between the two is R ) . If the B allele has arisen and become established in the first patch , but has not yet appeared in the second , we would like to know the time scale on which copies of B move between patches by migration ( supposing that no B allele arises independently by mutation in the meantime ) . To determine this , we study the fine-scale genealogy of alleles that transit between patches , obtaining along the way other useful information about the genealogy of B alleles . Doing this we arrive at Eq ( 11 ) for the rate at which an allele established in one patch spreads to a neighboring one by migration . Migration–selection balance ensures that there will be some B alleles present in the regions where they are disadvantageous , but only rarely , far away from the patch where B is established . Denote the expected frequency of allele B at displacement x relative to the patch by q ( x ) , and assume that the shape of the patch is at least roughly circular . Following [24] or [9] , one can write down a differential equation to which q ( x ) is the solution , and show that q ( x ) decays exponentially for large ∣x∣ , with a square-root correction in two dimensions: q ( x ) ≈ C ( | x | 2 s m / σ ) − ( d − 1 ) / 2 exp ( − | x | 2 s m / σ ) for large | x | , ( 3 ) where d is the dimension ( d = 1 or 2 ) , and C is a constant depending on the geographic shape of the populations and the selection coefficients . In applications we fit C to the data; to get concrete numbers from theory we take C = 1 if necessary . As J . Hermisson pointed out in comments on an earlier draft , this functional form has a simple intuition: local migration leads to the exponential decay , since if each migrant at distance x produces an average of c descendants that make it to distance x + 1 , then in one dimension , the number of migrants must decay as exp ( −cx ) ; and the square-root term in two dimensions enters because the available area grows with x . The “renewal” aspect of this same argument suggests that for Eq ( 3 ) to hold , ∣x∣ must be larger than a few multiples of σ . These asymptotics fit simulations quite well , as shown in Fig 1 . To be clear about the assumptions implicit here , below we provide a derivation of Eq ( 3 ) in section The equilibrium frequency , as well as justification for the asymptotics below in section Asymptotic solution for the equilibrium frequency . In one dimension , the equation can be solved to give q ( x ) in terms of Jacobi elliptic functions [37]; see the Supporting Information . This expected frequency q ( x ) is the time-averaged occupation frequency , i . e . , the total number of B alleles found across T generations near location x , per unit area , divided by T . If , for instance , q ( x ) = . 01 and the population density is ρ = 10 individuals per unit area , then in a survey tract of unit area around x we expect to find one individual every 10 generations—or , perhaps more likely , 10 individuals every 100 generations . This points to an important fact: close to the original patch , the “equilibrium frequency” q ( x ) describes well the density of the B allele at most times , but far away from the patch , the equilibrium is composed of rare excursions of families of B alleles , interspersed by long periods of absence . An example of one of these rare excursions is shown in Fig 2 . The relevant time scale on which B alleles migrate between patches is given by the rate of appearance of such families . This suggests decomposing the genealogy of B alleles into families in the following way . First , consider the genealogy of all B alleles that were alive at any time outside of the original patch . This is a collection of trees , each rooted at an allele living outside the patch whose parent was born inside the patch . Next , fix some intermediate distance r0 from the established patch , and erase from the genealogy every allele that has no ancestors living further away than r0 to the patch . This fragments the original set of trees into a collection of smaller trees that relate to each other all B alleles living outside of r0 at any time , and some living inside of r0; we refer to these trees as “families” . If r0 is large enough that there are not too many families and interactions between family members are weak , then these families can be approximated by a collection of independent spatial branching processes whose members are ignored if they enter the original patch , illustrated in Fig 3 . ( This can be made formal in the limit of large population density , also taking r0 large enough that the chance of reaching the original patch is small . ) The opportunity for adaptation depends on how often these families of B alleles encounter the new patch . Suppose that the area occupied by the new patch is S . We can learn about the rate of arrival of families at S from the time-averaged number of B alleles expected in S were it not a patch ( i . e . , if B was still maladaptive in S ) , which from Eq ( 3 ) is ρ q ( S ) = ( outflux of families ) × ( mean occupation in new patch per family ) , ( 4 ) where q ( S ) = ∫S q ( y ) dy . The quantity we wish to compute , the effective rate at which families of migrant B alleles establish in the new patch , is λ mig ( S ) = ( outflux of families ) × ( probability a family establishes in new patch ) . ( 5 ) The quantities on the right-hand side depend implicitly on r0 , but their product does not . Now that we have expressions for the mean rates of adaptation by new mutation , Eq ( 2 ) , and by migration from an already colonized patch , Eq ( 11 ) , it seems helpful to step back and review the assumptions underlying the asymptotic results we have used , or will use below . Our results should hold exactly in the limit of large , circular patches sufficiently far apart , large population density , and small selection coefficients of equal magnitude . To be more precise , the mutation rate results most obviously apply if sm ≈ sp , if the patch diameter is large relative to σ / min ( s m , s p ) , and the patch is not too far from circular . If sm ≪ sp then a strip of width σ / s m should be added to the outside of the patch in computing A for Eq ( 2 ) , and if sm ≫ sp , a strip of width σ / s p should be subtracted from the patch . As for the migration rate , we assume that each patch is large enough to support a stable population of B alleles ( A 1 / d > ( σ / 2 s p ) tan - 1 ( s m / s p ) ) . The geometry and size of the patch will also affect the approximation of Eq ( 48 ) . Next , in using Eq ( 3 ) , we assume that the inter-patch distance is large relative to the characteristic length ( R > σ / s m ) , and that local drift is not so strong that the equilibrium frequency is at least approximately attained ( using Wright’s effective neighborhood size , sm ≫ 1/ ( ρσd ) ) . The last requirement is necessary because if the B allele fixes in large neighborhoods where it is deleterious , we cannot approximate its dynamics via a branching process . We also neglect the time for migration-selection equilibrium to be reached . As discussed above , we also assume that migration to a new patch takes place over a number of generations; if there are sufficient rare , long-distance migration events that would move between patches in a single hop , this would require a different analysis . To test the robustness of our results to the various approximations we used , we used individual-based simulations on a one-dimensional lattice of 501 demes , with ρ haploid individuals per deme , dispersal to nearby demes with σ = 0 . 95 , and run for 25 , 000 generations . More details are given below in section Simulation methods , and the number of simulations used and parameter values are given in supplementary S1 and S2 Tables . To estimate the mean time until adaptation by mutation , we used one centrally located patch of 99 demes and a mutation rate of μ = 10−5 , and to estimate the mean time until adaptation by migration , we used two centrally located patches of 99 demes separated by a varying number of demes , with one patch initialized to have the B allele at frequency 0 . 8 . In each case , we measured the “time to adaptation” as the amount of time until at least 100 B alleles were present in the previously unadapted patch . Fig 4 summarizes how the results compare to theory , excluding parameter combinations that violate the assumptions discussed above , or where a majority of simulations did not adapt by 25 , 000 generations . S1 and S2 Figs show all times , and depictions of typical simulation runs are shown in S3 , S4 , S5 , S6 , S7 , S8 , S9 and S10 Figs . The agreement is reasonable for both . The theoretical value 1/λmut underestimates the mean time to mutation by a factor of around 2 that increases slightly with sm . This is to be expected for two reasons: First , we compute the time to reach 100 B alleles , while theory predicts the time until the progenitor of those 100 B alleles arose . Second , the expression for λmut neglects effects near the boundary of the patch , and the larger sm is , the harder it is for mutations that arise near the edge of the patch to establish . The theoretical values 1/λmig again has a margin of error of a factor of about 2 . S10 Fig shows simulations at more parameter values . We now turn to the question of whether the separated patches adapt by parallel genetic changes or by the spread of a migrant allele between the patches . As only a single mutation is required for individuals to adapt to the poor habitat patch , subsequent mutations that arise after an allele becomes established in the patch gain no selective benefit . Similarly , an allele introduced into a patch by migration will not be favored by selection to spread , if the patch has already been colonized . Therefore , mutations selectively exclude each other from patches , over short time scales , and will only slowly mix together over longer time scales by drift and migration , an assumption we also made in [6] . In reality , different mutations will likely not be truly selectively equivalent , in which case this mixing occurs over a time-scale dictated by the difference in selection coefficients . We assume that once a B allele is introduced ( by migration or mutation ) it becomes established in the poor habitat patch rapidly if it escapes loss by demographic stochasticity . Under this approximation , and the “selective exclusion” just discussed , after one of the patches becomes colonized by a single B allele , the other patch will become colonized by migration or mutation , but not by both . As such , the question of how the second patch adapts simply comes down to whether a new mutation or a migrant allele is the first to become established in the second patch . To work out how adaptation is likely to proceed , we can compare Expressions ( 2 ) and ( 11 ) above for the effective rates of adaptation by new mutation and by migration . We work in one dimension , as the square root term appearing for two dimensions is relatively unimportant . We first consider the order of magnitude that our parameters need to be on in order for adaptation via mutation or migration to dominate . Let γ = min ( 1 , sm/pe ) and w = A/A′—since A is the area of the not-yet-adapted patch and A′ is the area of its closest σ / 2 s m strip , w is approximately the width of the patch in units of σ / 2 s m . Effective migration and mutation rates will be on the same order if A μ ≈ 2 A ′ γ s m exp ( - R 2 s m / σ ) , where R is the distance between the patches . In other words , the migrational analogue of “mutational influx” ( μρA ) is 2 ρ A ′ γ s m exp ( - R 2 s m / σ ) , which depends fairly strongly on the selective disadvantage sm between patches . Equivalently , the rates are roughly equal if R / σ = log ( 2 γ s m / ( w μ ) ) / 2 s m , which gives the critical gap size past which adaptation will be mostly parallel in terms of selection coefficients , patch width , and mutation rate . If we take μ = 10−5 , the patch width to be ten times the length scale σ / 2 s m so w = 10 ( and −log ( wμ ) ≈ 9 . 2 ) , and sm > pe so that γ = 1 , then adaptation is mostly parallel for patches separated by gaps larger than R / σ > ( 9 . 2 + log ( 2 s m ) ) / 2 s m . If the selective pressure is fairly strong ( say , sm = . 05 ) , then for convergence to be likely , the distance between patches must be at least 21 . 8 times the dispersal distance ( i . e . , R/σ > 21 . 8 ) . If sm is much smaller , say sm = . 001 , the required distance increases to R/σ > 67 . If the mutation rate is higher—say , μ = 10−3—the required separation between patches is only reduced to R/σ > 7 . 3 with sm = . 05 . If sm = . 001 , then with this higher mutational influx this calculation predicts that mutation will always be faster than migration—however , this should be taken with caution , since as discussed above , this model does not hold if R is of the same order as σ or if sm is small enough that local drift is more important . We can go beyond these rough calculations to find the probability of parallel adaptation if we are willing to take our approximations at face value . Doing so , the time until the first of the two patches is colonized by the B allele will be approximately exponentially distributed with mean 1/ ( 2λmut ) . Following this , the time until the second patch is subsequently colonized ( via either migration or new mutation ) will be exponentially distributed with mean 1/ ( λmut + λmig ) . Both these rates scale linearly with population density ( ρ ) and the probability of establishment of a rare allele ( pe ≈ 2sb/ξ2 , for pe > sm ) , so that the overall time to adaptation is robustly predicted to increase with offspring variance ( ξ2 ) and decrease with population density and beneficial selection coefficient . Furthermore , the probability that the second adaptation is a new mutation , i . e . , the probability of parallel adaptation , with γ = min ( 1 , sm/pe ) , is λ mut λ mut + λ mig ( R ) = w μ / ( 2 s m ) w μ / ( 2 s m ) + C γ ( R 2 s m / σ ) − ( d − 1 ) / 2 exp ( − 2 s m R / σ ) , ( 12 ) so that the probability of parallel mutation should increase approximately logistically with the distance between the patches , on the spatial scale σ / 2 s m . We tested this using the same simulations as Fig 4 , by using the empirical distributions of the respective times to adaptation to estimate the probability , for each parameter combination , that a new , successful mutation appears before a successful migrant arrives from another , already adapted patch . The results are compared to Eq ( 12 ) in Fig 5 . If a patch adapts through new mutation or a rare migrant lineage , the genomic region surrounding the selected site will hitchhike along with it [42] , so that at least initially , all adapted individuals within a patch share a fairly long stretch of haplotype . Pairs of adapted individuals within a patch will initially share a haplotype of mean genetic length of about log ( ρAsp ) /sp around the selected site , as long as the patch is reasonably well mixed by dispersal ( otherwise see [43] ) . This association gets slowly whittled down by recombination during interbreeding at the edge of the patch , but there will always be longer LD nearby to the selected site [44] . When an already adapted patch colonizes another through migration , the newly colonized patch will inherit a long piece of haplotype around the selected site from the originating patch . The genetic length of this haplotype is roughly inversely proportional to the number of generations the allele spends “in transit” , because while very rare , the allele will be mostly in heterozygotes , and so each generation provides another opportunity for a different haplotype to recombine closer to the transiting B allele . A large , linked haplotype may still arrive and fix in the new patch , in which case the haplotype has literally hitchhiked across geography ! Fig 6 shows a simulation of such an event , including the lineage that founds an adaptive population on a second patch , and the length of the haplotype shared between this lineage and one in the original patch . The time that the lineage is outside the region where the B allele is common ( dark red in Fig 6 ) , the haplotype that accompanies it is broken down rapidly . After the lineage establishes on the patch , the rate of decay of the haplotype is slowed significantly , since most others with which it recombines have similar haplotypic backgrounds . Above we argued that a good model for this transit is a single Brownian trunk lineage surrounded by a cloud of close relatives of random size K whose chance of surviving until t generations is 1 − ke ( t ) . Consider a single such family , and let τ be the ( random ) time at which it first hits the new patch , with τ = ∞ if it never does . We assume that the length of the hitchhiking segment depends only on the time spent by the trunk lineage of the first successful migrant “in transit” between the patches , i . e . , τ conditioned on τ < ∞ . In so doing , we neglect recombination between B alleles ( since they are at low density in transit ) , and the possibility that more than one successful migrant family is in transit at once ( so that faster migrants would be more likely to have arrived first ) . Since each generation spent in transit provides an opportunity for recombination , if recombination is Poisson , the length of the haplotype ( in Morgans ) initially shared between populations on each side of the selected locus is exponentially distributed with mean τ . Therefore , if L is the length of hitchhiking segment on , say , the right of the selected locus , then P { L > ℓ } = E [ e - ℓ τ | τ < ∞ ] . ( 15 ) Computing this depends on 1 − ke ( t ) , the probability a family survives until t . Since 1 - k e ( t ) ≃ e - s m t / E [ K ] , we approximate the lifetime distribution of a family by an exponential with mean 1/sm . We can then use standard results on hitting times of d-dimensional Brownian motion that is killed at rate sm ( see [45] 2 . 2 . 0 . 1 and 4 . 2 . 0 . 1 ) . In particular , if the patch is circular with radius w and lies at distance R from the already adapted patch , then E [ e - ℓ τ ] = e - R 2 ( s m + ℓ ) / σ d = 1 K 0 ( ( R + w ) 2 ( s m + ℓ ) / σ ) K 0 ( w 2 ( s m + ℓ ) / σ ) d = 2 , ( 16 ) where K0 is a modified Bessel function of the second kind . We are interested in lineages that manage to reach the patch before being killed , i . e . , having τ < ∞ , which occurs with probability P { τ < ∞ } = lim ℓ → 0 E [ exp ( - ℓ τ ) ] . To keep the expressions simple , in the remainder of this section we only compute quantities for one dimension . By Bayes’ rule , P { L > ℓ } = exp - R σ 2 ( ℓ + s m ) - 2 s m . ( 17 ) This can be differentiated to find that the expected transit time is E [ τ ∣ τ < ∞ ] = ( R 2 s m / σ ) × 1 / ( 2 s m ) ( 18 ) and that Var [ τ ∣ τ < ∞ ] = ( R 2 s m / σ ) × 1 / ( 2 s m ) 2 . ( A saddle point approximation provides an alternative route to these expressions . ) The form of Eq ( 17 ) implies that if Y is an exponential random variable with rate R 2 / σ , then L has the same distribution as ( Y + s m ) 2 - s m . Furthermore , the expected length of shared hitchhiking haplotype is E [ L ] = σ 2 s m / R + σ 2 / R 2 ( 19 ) and Var [ L ] = 2 s m σ 2 / R 2 + 4 σ 3 2 s m / R 3 + 5 σ 4 / R 4 . For two dimensions , asymptotics of Bessel functions show that L has the same tail behavior , but how other properties might change is unclear . As a rule of thumb , Eq ( 18 ) means that families who successfully establish move at speed 2 s m σ towards the new patch—if sm , the strength of the selection against them , is smaller , the need to move quickly is less imperative . Then , Eq ( 19 ) means that the haplotype length is roughly the length one would expect given the mean transit time , so more weakly deleterious transiting alleles arrive with them shorter haplotypes . Note that we have become used to seeing σ divided by 2 s m , rather than multiplied; it appears here because σ / 2 s m is a length scale , and to convert it to a speed this is divided by 1/2sm . Coat color in the rock pocket mouse Chaetodipus intermedius is a classic example of local adaptation [14 , 15] . These mice live on rocky outcrops throughout the southwestern United States and northern Mexico , and generally have a light pelage similar to the predominant rock color . However , in some regions these mice live on darker substrates ( e . g . , old lava flows ) , and in these patches have much darker pigmentation , presumably to avoid visual predators . Some of the largest patches of dark rock range from 10km to 100km wide and lie 50–400km from each other , and dark-colored populations of C . intermedius have been found on many of these . ( However , patches of all sizes occur across all scales in a heterogeneous manner . ) [46] demonstrated that on one of these flows ( Pinacate ) , much of the change in coloration is due to an allele at the MC1R locus . This dark allele differs from the light allele by four amino acid changes , and has a dominant or partially dominant effect depending on the measure of coat color . The Pinacate allele is not present in a number of other populations with dark pelage , suggesting these populations have adapted in parallel [17 , 46] . However , [47] reasoned that , elsewhere in the range , multiple close dark outcrops may share a dark phenotype whose genetic basis has been spread by migration despite intervening light habitat . A key parameter above was the dispersal distance divided by the square root of strength of selection against the focal allele between patches , σ / s m . [48] studied the frequency of the dark MC1R allele and coat color phenotypes , at sites across the ( dark ) Pinacate lava flow and at two nearby sites in light-colored rock . On the lava flow the dark MC1R allele is at 86% frequency , while at a site with light substrate 12km to the west ( Tule ) , the frequency is 3% . The dark allele was entirely absent from Christmas pass , a site with light substrate 7km north of Tule , and 3km further from the lava flow . In the other direction , the dark MC1R allele was at 34% at a site with light substrate 10km to the east of the flow ( O’Neill ) . Note that such apparent asymmetry is expected , since as noted above the migration–selection equilibrium can be highly stochastic . These numbers give us a sense of a plausible range of the parameters . Assuming the dark allele is at 50% frequency at the edge of the lava flow , we can fit Formula ( 3 ) to these frequencies ( similar to [48] ) . Doing this for Tule we obtain σ / s m ≈ 3 km , and for O’Neill , σ / s m ≈ 30 km , giving us a range of cline widths . We also need estimate of sm . Using σ ≈ 1km [49 , 50] , these cline widths imply that sm = 1/9 and 1/900 are reasonable values . The mutational target size μ for the trait is unclear . While the Pinacate dark haplotype differs from the light haplotype at four amino acid residues , it is likely that not all of these changes are needed for a population to begin to adapt . Also , there a number of genes besides MC1R at which adaptive changes affecting pigmentation have been identified in closely related species and more broadly across vertebrates [51] . To span a range of plausible values , we use a low mutation rate of μ = 10−8 ( a single base pair ) , and a high mutation rate μ = 10−5 ( a kilobase ) . Finally , we set A = 100km2 ( roughly the size of the Pinacate patch ) . In Fig 7 we show the dependence of the probability of parallel mutation on the distance between lava flow patches using these parameters , showing that parallel mutation should become likely over a scale of tens to a few hundred kilometers between patches . Given the large selection coefficient associated with the dark allele on the dark substrate , we expect the initial haplotype associated with either a new mutation or migrant allele to be large . Fig 7 also shows how long the founding haplotype shared between populations is expected to be , from Eq ( 19 ) . The initial length can be quite long between geographically close patches ( tens of kilometers ) . However , for the wider cline width ( σ / s m = 30 km ) , adaptation by migration can still be likely for patches 100km apart , but the shared basis may be hard to detect , as the length of shared haplotype can be quite short .
We have focused on relative rates of adaptation , since in applications where adaptation has occurred , the question is whether adaptations in distinct patches have appeared independently or not . However , any adaptation that does occur may have to make use of standing variation , if mutation rates are low . The case of a panmictic population was studied by [8] , and we study the case of a continuous , spatial population in [54] . If parallelism in local adaptation of the sort we study here is due to standing variation rather than new mutation , then the dynamics of adaptation should not depend strongly on migration patterns ( but the initial spatial distribution of standing variation may ) . We have mostly ignored the issue of dominance by dealing with essentially haploid models , and appealing to the fact that the dynamics we study occur where the mutation is rare , and hence mostly present only in heterozygotes . Our results should hold as a good approximation to dominant and partially dominant alleles ( with sm the selection against heterozygotes ) . If , however , the mutation is recessive , then it is essentially neutral where rare , and so would encounter much less resistance to spreading between patches . The shape of the cline obtained is given by [24] . This is counteracted , however , by the increased difficulty with which the mutation would establish once arriving in a new patch , if the beneficial effect is also recessive . As such it is not clear what our intuition should be about the contribution of recessive alleles to adaptation via migration . Further work is needed to put empirical observations of local adaptation by recessive alleles in a theoretical context . It is similarly unclear how the model should extend to a polygenic trait . To provide context for the results on shared haplotype length in section Length of the hitchhiking haplotype it is important to also understand the process by which haplotypes are whittled down within patches . The initial haplotype that sweeps within a patch will be dispersed over time by recombination . Likewise , the haplotype that is shared between patches coadapted by migration will also break down ( Eq 19 ) . However , a long time after the initial sweep , we may still expect to find individuals within the patch sharing longer haplotypes around the selected locus than with individuals elsewhere , since selection against migrants decreases mean coalescence times within the patch near the selected locus . The literature on clines ( e . g . , [44] ) has important information , but more work is needed to provide robust estimates for these processes . Questions about the genomic length-scale of signals of sweeps shared by migration have also been addressed in discrete population settings [55 , 56] , reviewed in [57] . This work has shown that the length of the shared swept haplotype is often significantly shorter than the sweep within each patch , resulting in a pattern of shoulders of elevated FST between adapted populations some distance away from the shared selected allele . It would be of interest to see how similar patterns can arise in a continuous population setting , as a way of uniting these results . We have also ignored the possibility of very long distance migration , instead focusing on local dispersal ( hence Gaussian by the central limit theorem ) . However , dispersal distributions can be very heavy tailed , with a small fraction of individuals moving very long distances indeed [22 , 58] . In addition , over long time-scales , very rare chance events ( mice carried off by hurricanes and the like; [59 , 60] ) could play a role in spreading migrant alleles if adaptation by other means is sufficiently unlikely . Such tail events could greatly increase the probability of shared adaptation above that predicted by our model . Furthermore , if adaptive alleles do move between distant patches via rare , long distance migration then they will be associated with a much longer shared haplotype than predicted by Eq ( 19 ) . As such , we view our results as a null model by which the contribution of long distribution migrants to adaptation could be empirically judged . We have studied circular patches of habitat at long distances from each other . Real habitat geometry can be much more complex , e . g . , with archipelagos of patches of varying sizes , or patches connected by long , skinny corridors , for instance . The work of [61] comes closest to a general theory of balanced polymorphisms in such habitats . It is possible that our techniques could be applied in their much more general setting , as both are based , fundamentally , on branching process approximations . It is also interesting to think about the probability of convergent adaptation to continuously varying environments , e . g . replicated environmental clines . The falling cost of population genomic sequencing means that we will soon have the opportunity to study the interplay of adaptation with geography and ecology across many populations within a species . Our work suggests that even quite geographically close populations may be forced to locally adapt by repeated , convergent , de novo mutation when migration is geographically limited and selective pressures are divergent . Thus , systems where populations have been repeatedly subject to strong local selection pressures may offer the opportunity to study highly replicated convergent adaptation within a similar genetic background [1] . Such empirical work will also strongly inform our understanding of the ability of gene flow to keep ecologically similar populations evolving in concert [62] . Our results suggest that adaptation to shared environments is certainly no guarantee of a shared genetic basis to adaptation , suggesting that rapid adaptation to a shared environment could potentially drive speciation if the alleles that spread in each population fail to work well together [63] .
First we briefly describe the simulations we used for illustration and validation ( the R code used is provided in S1 Scripts and at http://github . com/petrelharp/spatial_selection ) . We simulated forward-time dynamics of the number of alleles of each type in a rectangular grid ( either one- or two-dimensional ) of demes with fixed size N . Each generation , each individual independently chose to reproduce or not with a probability r depending on her type and location in the grid; locally beneficial alleles were more likely to reproduce . Each extant individual then either remained in the same location with probability 1 − m or else migrated a random number of steps in a uniformly chosen cardinal direction; for most simulations m = 0 . 2 and the probability of migrating k steps was proportional to 2−k for 1 ≤ k ≤ 5 . In 2D , diagonal steps were also used . This gave us values of σ = 0 . 95 deme spacings in 1D and σ = 0 . 74 in 2D . Once migrants were distributed , each deme was uniformly resampled back down to N individuals . ( Although we described the simulation in terms of individuals , we kept track only of total numbers in an equivalent way . ) The base probability of reproduction in each generation in simulations for type b alleles was r = 0 . 3; this was then multiplied by 1 + s to get the probability of reproduction for type B , where the value of s is either sm or sp depending on the individual’s location . This determines the values of sm and sp reported in the figures , and do not depend on the basic rate of reproduction . However , to obtain values for sp and sm when comparing theory to simulation , we computed the rate of intrinsic growth , i . e . , the s so that the numbers of B alleles when rare would change by est after t generations in the absence of migration . ( The resulting values are close to the first notion of s , but give better agreement with theory , which uses the second definition . ) To sample lineages , we first simulated the population dynamics forwards in time , then sampled lineages back through time by , in each generation , moving each lineage to a new deme with probability proportional to the reverse migration probability weighted by the number of B alleles in that deme in the previous time step . If more than one lineage was found in a deme with n alleles of type B , then each lineage picked a label uniformly from 1 … n , and those picking the same label coalesced . Since reproduction is Poisson , this correctly samples from the distribution of lineages given the population dynamics . When rare , copies of a new mutant allele are approximately independent and experience a uniform selective benefit; and can therefore be treated as a branching process . Furthermore , whether or not a new , beneficial mutation establishes or is lost to demographic stochasticity is determined by this initial phase where it is rare . Fortunately , the probability that a branching process dies out can be found as a fixed point of the generating function of the process [38] . Therefore , we calculated explicitly the generating function for a spatial branching process with nearest-neighbor migration on a one-dimensional lattice and a Poisson number of offspring with mean 1 + s , where s could vary by location , and iterated this forward to convergence to obtain 1 − p ( x ) , the probability a single mutation appearing at x would fail to establish . We considered two situations: where s is a step function , and where it has a linear transition . These solutions are shown , and parameters described , in Fig 8 . Here ( and at other parameter choices ) we see that the probability of establishment p ( x ) goes to the equilibrium value ( approximately pe = 2s/ξ2 ) within the patch; the transition is fairly symmetrical about the edge of the patch , even if the edge of the patch is not sharp . Additional experimentation indicated that the fit remains equally good for other parameter values , even if migration can move further than one deme and offspring numbers are not Poisson . This lends credence to our approximation that the integral of p ( x ) over the entire range is close to pe multiplied by the area of the patch . For completeness , and clarity as to the scalings on the relevant parameters , here we provide a derivation of the differential equations referred to above Eq ( 3 ) , and establish the asymptotics given in that equation . One route to the “equilibrium frequency” of the allele outside the range where it is advantageous is as follows; see [9] or [29] ( or [64] or [65] or [24] ) for other arguments in equivalent models , and see [66] and/or [67] for a general framework for the stochastic processes below . Suppose that the population is composed of a finite number of small demes of equal size N arranged in a regular grid , and that selection ( for or against ) the allele is given by the function s ( x ) , with x denoting the spatial location . Each individual at location x reproduces at random , exponentially distributed intervals , producing a random number of offspring with distribution given by X who then all migrate to a new location chosen randomly from the distribution given by x + R , where they replace randomly chosen individuals . If x + R is outside of the range , then they perish . Each individual’s time until reproduction is exponentially distributed: the reproduction rate is 1 if it carries the original allele , or is 1 + s ( x ) if it carries the mutant allele . Suppose that the number of offspring X has mean μ; the variance of X will not enter into the formula ( but assume X is well-behaved ) . Also suppose that the migration displacement R has mean zero and variance σ2; in more than one dimension , we mean that the components of the dispersal distance are uncorrelated and each have variance σ2 . Let Φ t N ( x ) be the proportion of mutant alleles present at location x at time t , and Φt ( x ) the process obtained by taking N → ∞ ( which we assume exists ) . Denote by δx a single unit at location x , so that e . g . Φ t N + δ x / N is the configuration after a mutant allele has been added to location x . For 0 ≤ ϕ ≤ 1 , we also denote by X ¯ ϕ the random number of mutant alleles added if X new offspring carrying mutant alleles replace randomly chosen individuals in a deme where the mutant allele is at frequency ϕ ( i . e . hypergeometric with parameters ( X , Nϕ , N ( 1 − ϕ ) ) ) ; similarly , X ˜ ϕ is the number lost if the new offspring do not carry the allele ( i . e . hypergeometric with parameters ( X , N ( 1 − ϕ ) , Nϕ ) ) . ( We like to think of Φ t N as a measure , but it does not hurt to think of ΦN as a vector; we aren’t providing the rigorous justification here . ) Then the above description implies that for any sufficiently nice function f ( Φ ) that ∂ ∂ t E f ( Φ t N ) = N ∑ x E ( 1 + s ( x + R ) ) Φ t N ( x + R ) f Φ t N + X ¯ Φ t ( x + R ) N δ x - f ( Φ t N ) + E 1 - Φ t N ( x + R ) f Φ t N - X ˜ Φ t ( x + R ) N δ x - f ( Φ t N ) ( 20 ) = μ ∑ x E ∂ ϕ ( x ) f ( Φ t ) Φ t ( x + R ) - Φ t ( x ) + s ( x + R ) Φ t ( x + R ) ( 1 - Φ t ( x ) ) + O 1 N . ( 21 ) In the final expectation , R and Φ are independent . This follows by taking first-order terms in 1/N in the Taylor series for f , and the fact that E [ X ¯ ϕ ] = ϕ μ and E [ X ˜ ϕ ] = ( 1 - ϕ ) μ . We can see two things from this: First , since this is a first-order differential operator , the limiting stochastic process Φ obtained as N → ∞ is in fact deterministic ( check by applying to f ( Φ ) = Φ ( x ) 2 to find the variance ) . Second , if we want to rescale space as well to get the usual differential equation , we need to choose Var[R] = σ2 and s ( x ) to be of the same , small , order; this is another way of seeing that σ / s is the relevant length scale ( as noted by [9] ) . More concretely , suppose that the grid size is ϵ → 0 , that Var[R] = ( σϵ ) 2 , and that the strength of selection is s ( x ) ϵ , suppose that Φt ( x ) is deterministic and twice differentiable , and let ξ ( t , x ) = Φt/ϵ ( x ) ; then the previous equation with f ( Φ ) = Φ ( x ) converges to the familiar form: ∂ t ξ ( t , x ) = μ σ 2 2 ∑ k = 1 d ∂ x k 2 ξ ( t , x ) + s ( x ) ξ ( t , x ) ( 1 - ξ ( t , x ) ) . ( 22 ) Here we have taken first the population size N → ∞ and then the grid size ϵ → 0; we could alternatively take both limits together , but not if ϵ goes to zero too much faster than N grows . One reason for this is that at finite N , the process Φt is an irreducible finite-state Markov chain with absorbing states at 0 and 1; therefore , the inevitable outcome is extinction of one type or another , which is not the regime we want to study . In one dimension , we are done ( and discuss exact solutions in S1 Text ) ; in higher dimensions , we are more interested in the mean frequency at a given distance r from a patch . If we take a radially symmetric patch centered at the origin ( so s only depends on r ) , and let ξ ( t , r ) denote the mean occupation frequency at distance r , then the polar form of the Laplacian in d dimensions gives us that Eq ( 22 ) is ∂ t ξ ( t , r ) = μ σ 2 2 ∂ r 2 ξ ( t , r ) + σ 2 d - 1 2 r ∂ r ξ ( t , r ) + s ( r ) ξ ( t , r ) ( 1 - ξ ( t , r ) ) . ( 23 ) A radially symmetric equilibrium frequency ξ ( t , x ) = q ( ∣x∣ ) , with s ( r ) = −s < 0 for all r > r0 , solves for r0 < r < ∞ , ∂ r 2 q ( r ) + d - 1 r ∂ r q ( r ) - 2 s σ 2 q ( r ) ( 1 - q ( r ) ) = 0 ( 24 ) lim r → ∞ q ( r ) = lim r → ∞ ∂ r q ( r ) = 0 0 < q ( r ) < 1 ( 25 ) Since q ( r ) → 0 as r → ∞ , so q ( r ) ( 1 − q ( r ) ) ≈ q ( r ) , it can be shown that the true equilibrium frequency q is close , for large r , to the solution to ∂ r 2 u ( r ) + d - 1 r ∂ r u ( r ) - 2 s σ 2 u ( r ) = 0 ( 26 ) lim r → ∞ u ( r ) = lim r → ∞ ∂ r u ( r ) = 0 . ( 27 ) This has general solution given by a modified Bessel function: using [68] 8 . 494 . 9 , the general solution is u ( r ) = C ′ ( r - r 1 ) ( 2 - d ) / 2 K ( 2 - d ) / 2 ( r - r 1 ) 2 s / σ , ( 28 ) where C′ and r1 are chosen to match boundary conditions . Asymptotics of Bessel functions ( [68] , 8 . 451 . 6 ) then imply that q ( r ) ≈ C r ( 1 - d ) / 2 exp - r 2 s / σ + O ( 1 / r ) , ( 29 ) where C is a different constant . Here we make a more precise argument to back up Expression ( 11 ) for the migration rate . The argument made above in section Heuristics applies to general migration mechanisms , since it relies only on a decomposition of the migrant families upon hitting the new patch; but it is also imprecise in subtle ways that are difficult to formalize . Here we take a somewhat different tack , supposing that it suffices to model the spatial movement of a migrant family by following only the motion of the “trunk” ( i . e . , the red line in Fig 3 ) , and supposing this motion is Brownian , with variance σ2 per generation . ( Recall σ is the dispersal distance . ) We then use facts about Brownian motion and branching processes , to compute more precise versions of Eqs ( 4 ) and ( 5 ) . We are approximating the dynamics of the focal allele in the region further away than r0 from the patch as the sum of independent migrant families , each of whose dynamics are given by a spatial branching process ( as depicted in Fig 3 ) . Call B ( r0 ) the region closer than r0 to the patch , and ∂B ( r0 ) its boundary . Denote by γ ( x ) the mean rate of outflux of migrant families from a point x ∈ ∂B ( r0 ) , i . e . , the time-averaged density of individuals near a point x in ∂B ( r0 ) that are the founders of new migrant families . Expressions ( 4 ) and ( 5 ) are a simple product of the “outflux of families” , when in fact they should be an integral of γ ( x ) over possible locations . However , it will turn out that the integrand of Eq ( 5 ) is well-approximated by a constant multiple of the integrand of Eq ( 4 ) . First consider Eq ( 4 ) for the equilibrium frequency . Suppose that Z is one such spatial branching process as above in section The genealogy of migrant families , started at time 0 with a single individual at x , and write S for the new patch . The mean occupation measure of Z in the region S , which we denote u ( x , S ) , can be thought of informally as the expected total number of offspring of a family beginning at x that ever live in S . Let q ( S ) = ∫S q ( y ) dy denote the total equilibrium frequency in S . This is decomposed in Expression ( 4 ) as the sum of mean occupation densities of a constant outflux of branching processes from ∂B ( r0 ) : ρ q ( S ) = ∫ ∂ B ( r 0 ) γ ( x ) u ( x , S ) d x . ( 30 ) Now we will decompose u ( x , S ) under the assumption that the marginal distribution of the spatial motion of a single lineage is Brownian . Let Bt be a Brownian motion with variance σ2 , and τ† an independent Exponential ( sm ) time . The mean occupation time of Z spent in a region S is , u ( x , S ) = E ∫ 0 ∞ Z t ( S ) d t ( 31 ) = ∫ 0 ∞ E [ Z t ] p t ( x , S ) d t ( 32 ) = ∫ 0 ∞ e - s m t p t ( x , S ) d t ( 33 ) = ∫ 0 ∞ P x { τ † > t & B t ∈ S } d t ( 34 ) = E x ∫ 0 τ † 1 S ( B t ) d t , ( 35 ) where P x gives probabilities for Brownian motion begun at x ( i . e . , B0 = x ) , and likewise E x . If we define τS to be the hitting time of S by the Brownian motion B , and μS ( x ) to be the hitting distribution of ∂S by BτS conditioned on τS < τ† , by the strong Markov property this is equal to u ( x , S ) = P x { τ S < τ † } E μ S ( x ) ∫ 0 τ † 1 S ( B t ) d t ( 36 ) = P x { τ S < τ † } g ( A ) , ( 37 ) where now E μ denotes expectations for Brownian motion for which the distribution of B0 is μ , and g ( A ) is defined to be the latter expectation , which does not depend on x if S is circular ( with area A ) . This form we can now compare to the Expression ( 5 ) for the outflux of successful migrants . Consider the probability that a migrant family beginning with a single individual at x will ever establish in the new patch . It would be possible to analyze this probability directly , as in [29]; but here we take a simpler route , approximating this by the chance that the trunk hits the new patch , multiplied by the chance that at least one member of the family escapes demographic stochasticity and successfully establishes in the new patch . Write h ( x , S ) for the probability that the Brownian trunk hits the patch S before the family dies out , and f ( S ) for the chance that the family manages to establish in the new patch , given that it successfully arrives . ( This is approximately independent of x . ) As for q ( S ) above , Expression ( 5 ) is properly an integral against the outflux γ ( x ) : λ mig ( S ) = ∫ ∂ B ( r 0 ) γ ( x ) h ( x , S ) f ( S ) d x . ( 38 ) If we make the approximation that Z hits the new patch only if the trunk of Z does , and recall that 1 − ke ( t ) is the chance that Z survives for t generations , then h ( x , S ) ≈ ∫ 0 ∞ ( 1 - k e ( t ) ) P x { τ S ∈ d t } ( 39 ) = ∫ 0 ∞ e s m t ( 1 - k e ( t ) ) e - s m t P x { τ S ∈ d t } ( 40 ) = ∫ 0 ∞ e s m t ( 1 - k e ( t ) ) P x { τ S ∈ d t & t < τ † } ( 41 ) ≈ 1 E [ K ] ∫ 0 ∞ P x { τ S ∈ d t & t < τ † } ( 42 ) = 1 E [ K ] P x [ τ S < τ † ] . ( 43 ) Therefore , we have that h ( x , S ) E [ K ] ≈ P x [ τ S < τ † ] = u ( x , S ) / g ( A ) . ( 44 ) Since the integrands in Expressions ( 30 ) and ( 38 ) only differ by a factor that ( at least asymptotically ) does not depend on the distance between x and S , we can obtain the migration rate by multiplying the equilibrium frequency by this factor . This factor will not depend on A , because although f ( A ) and g ( A ) in principle depend on the patch size ( and geometry ) , the dependence is very weak . For instance , the width of a circular patch only very weakly affects the chance of establishment of a new migrant that appears on its edge , as long as the patch is large enough . By Eqs ( 30 ) and ( 38 ) , ρ q ( S ) = ∫ ∂ B ( r 0 ) γ ( x ) u ( x , S ) d x ( 45 ) ≈ g ( A ) E [ K ] ∫ ∂ B ( r 0 ) γ ( x ) h ( x , S ) d x ( 46 ) = g ( A ) E [ K ] f ( A ) λ mig ( S ) . ( 47 ) Once we show that g ( A ) ≈ 1/ ( 2sm ) , we will have arrived at the result , Eq ( 10 ) . The function g ( A ) is the expected amount of time that a Brownian motion begun on the edge of a disk of area A is expected to spend inside the disk before τ† . This is integral of the Green function for the Bessel process of the appropriate order , so using [45] , and letting w be the width of the patch , in d = 1 , g ( A ) = ∫ 0 2 w / σ e - y 2 s m / σ 2 s m d y = ( 1 - e - 2 w 2 s m / σ ) / ( 2 s m ) ( 48 ) and in d = 2 , by [68] 5 . 56 . 2 , g ( A ) = ∫ 0 2 w / σ 2 y K 0 ( y 2 s m ) d y ( 49 ) = 1 s m ∫ 0 2 w 2 s m / σ y K 0 ( y ) d y ( 50 ) = 1 2 s m 1 - 2 w 2 s m σ K 1 ( 2 w 2 s m / σ ) , ( 51 ) where K0 and K1 are modified Bessel functions of the second kind . In either case , g ( A ) ≈ 1/ ( 2sm ) , which is the approximation we use in the main text . In the development above we need to approximate the integral of Eq ( 3 ) across the area occupied by the new patch , q ( S ) = ∫S q ( x ) dx . The precise answer depends on the shape and orientation of the patches; but we aim for a usable approximation , primarily in terms of the shortest distance from the old patch to the new patch , denoted R , and assuming R is large enough we can take Eq ( 3 ) as an equality . Since q ( R ) decreases as ∣x∣ increases , q ( S ) ≤ A q ( R ) ( 52 ) where A is the area of S . This will be a good approximation if S is small . In one dimension , if S has length ℓ , q ( S ) = ∫ R R + ℓ C e - u 2 s m / σ d u ( 53 ) = C e - R 2 s m / σ σ 2 s m 1 - e - ℓ 2 s m / σ ( 54 ) ≤ C e - R 2 s m / σ σ 2 s m . ( 55 ) In two dimensions , suppose that T is a rectangle enclosing S with one axis aligned towards the original patch and transverse width w . Then q ( S ) ≤ ∫ T q ( x ) d x ( 56 ) ≤ w ∫ R ∞ q ( r ) r d r ( 57 ) = w C ∫ R ∞ ( r 2 s m σ ) − 1 / 2 e − r 2 s m / σ d r ( 58 ) ≤ w σ 2 s m C π ( R 2 s m σ ) − 1 / 2 e − R 2 s m / σ d r . ( 59 ) If we absorb π into the constant C , this is of the form ( width ) × ( σ / 2 s m ) × q ( R ) , i . e . , roughly the area , after replacing the length by σ / 2 s m . In both cases , the upper bound is q ( S ) ≤ A′ q ( x ) , where A′ is the area of the parts of S that are no more than R + σ / 2 s m away from the original patch . Lower bounds could be obtained along similar lines .
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Often , a large species range will include patches where the species differs because it has adapted to locally differing conditions . For instance , rock pocket mice are often found with a coat color that matches the rocks they live in , these color differences are controlled genetically , and mice that don’t match the local rock color are more likely to be eaten by predators . Sometimes , similar genetic changes have occurred independently in different patches , suggesting that there were few accessible ways to evolve the locally adaptive form . However , the genetic basis could also be shared if migrants carry the locally beneficial genotypes between nearby patches , despite being at a disadvantage between the patches . We use a mathematical model of random migration to determine how quickly adaptation is expected to occur through new mutation and through migration from other patches , and study in more detail what we would expect successful migrations between patches to look like . The results are useful for determining whether similar adaptations in different locations are likely to have the same genetic basis or not , and more generally in understanding how species adapt to patchy , heterogeneous landscapes .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"&",
"Methods"
] |
[] |
2015
|
Convergent Evolution During Local Adaptation to Patchy Landscapes
|
Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments . Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth , cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels . To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth , as provided by genome-scale reconstructions of microbial organisms . Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp . PCC 6803 . Building upon several recent reconstructions of cyanobacterial metabolism , unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed . The updated model integrates novel results with respect to the cyanobacterial TCA cycle , an alleged glyoxylate shunt , and the role of photorespiration in cellular growth . Going beyond conventional flux-balance analysis , we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism .
Almost all life on Earth ultimately depends on oxygenic photosynthesis to capture solar energy and convert atmospheric carbon into organic compounds that serve as nutrients for heterotrophic organisms . Photosynthesis and the assimilation of inorganic carbon are evolutionarily old processes , with signatures RuBisCO activity , the major enzyme of carbon fixation , tracing back more than 3 billion years [1] . The presence of molecular oxygen ( ) in today's atmosphere is believed to be a consequence of the appearance of cyanobacteria , ubiquitous photosynthetic microorganisms that led to the great oxygenation event , one of the major transitions in the evolution and history of life on this planet [1] . Today , cyanobacteria are the only known prokaryotes capable of oxygen-evolving photosynthesis and remain to have major impact on almost all geochemical cycles , including the global carbon cycle , global oxygen recycling and nitrogen fixation . From a metabolic perspective , cyanobacteria are highly versatile organisms and occupy diverse ecological niches where light is available . Renewed attention on cyanobacterial metabolism was triggered by the prospect to utilize their light-driven capability of fixation for the production of high-value products [2] , [3] and third generation biofuels [4]–[9] . However , to harness solar energy using cyanobacteria often requires targeted modifications of the metabolic network – a task that would greatly benefit from an in-depth understanding of metabolic interconversions taking place during phototrophic growth . A first step towards such an increased understanding is often provided by detailed and validated genome-scale reconstructions of the metabolic networks of the respective organisms . Recently , a number of metabolic reconstructions of cyanobacteria , most notably for the strain Synechocystis sp . PCC 6803 , became available [10]–[18] . While these reconstructions differ significantly in reliability , size and scope , each led too useful insight into the metabolic organization of the model organism . In particular , analysis of different reconstructions allows us to pinpoint open questions in the representation of the metabolic network of Synechocystis sp . PCC 6803 . In this work , we present and interrogate an updated representation of the metabolic network of Synechocystis sp . PCC 6803 . The updated model integrates novel results with respect to the cyanobacterial TCA cycle , an alleged glyoxylate shunt , the role of photorespiration in cellular growth , as well peculiarities of photosynthetic reactions such as light-dependent oxidative stress . In this paper , we seek to explore the implications of alternative network topologies for phototrophic flux patterns and optimal growth . Closing the iterative cycle of Systems Biology , our computational analysis is supplemented with specifically acquired experimental data to validate unclear reaction steps and growth conditions . Furthermore , we seek to improve the applicability of FBA on phototrophic conditions by implementing a full diurnal cycle , guided by the diurnal transcription of key enzymes . The manuscript is organized as follows: First , we provide a brief overview on the current status of our reconstruction of the metabolic network of the cyanobacterium Synechocystis sp . PCC 6803 , including several computational validation steps . Subsequently , we discuss a reference condition for phototrophic growth that allows us to compare different network topologies with respect to optimal biomass yield using flux-balance analysis . Based on this reference condition , details of alternative flux solutions with respect to the TCA cycle , the glyoxylate bypass , the RuBisCO oxygenase and photorespiration are explored . In the final section , we discuss the diurnal cycles of phototrophic metabolism using a time-varying objective function .
The starting point of our analysis is an extended and updated metabolic reconstruction of the cyanobacterium Synechocystis sp . PCC 6803 . The reconstruction is based on a previously published network of the organism [13] , and takes into account knowledge from several complementing recent reconstructions [10]–[12] , [14]–[18] . As compared to our previous reconstruction of Synechocystis sp . PCC 6803 , the network was extended to include lipid and fatty-acid metabolism , biosynthesis of peptidoglycan , chlorophylls , carotenoids , terpenoids , quinones and tocopheroles , thiamine-diphosphates , as well as the synthesis of several co-factors , vitamins and several stress related-pathways . The description of photosynthetic light reactions and transport processes was significantly improved . The reconstruction process itself followed standard procedures described in the literature [19] and is detailed in Materials and Methods . In order to avoid an inflation of network size by poorly validated reactions , we distinguish between a core network used for further computational analysis and an augmented network including all remaining annotated enzymes with putative metabolic function . The core network encompasses a connected set of all known metabolic pathways for the synthesis of main biomass and co-factors known to occur within the cyanobacterium Synechocystis sp . PCC 6803 . During the reconstruction process , completeness of all synthesis pathways was validated with respect to biomass components , co-factors and dilution of metabolites by growth . The core reconstruction encompasses 677 genes , that encode for 495 enzymes or enzyme-complexes . The annotated enzymes give rise to 759 metabolic reactions among 601 metabolites . In addition , the core reconstruction contains 6 spontaneous interconversions and 61 transport reactions , including diffusion . The reaction network distinguishes between six cellular compartments , the cytosol , the thylakoid membrane , the thylakoid lumen , the plasma membrane , the periplasmic space , carboxysomes , as well as the extracellular space . Several key properties of the network are summarized in Figure 1 . The reconstruction is provided as an annotated SBML file ( System Biology Markup Language , [20] ) and as an Excel sheet , Supplementary Dataset S1 and Supplementary Table S1 , respectively . Annotated enzymes that are not part of the core network are provided as Supplementary Table S2 . To enhance usability of the reconstructed network , a detailed graphical overview of the metabolic network was prepared and is provided as Figure S1 . Large scale metabolic reconstructions offer the possibility to investigate physiological properties of the respective organism using constraint-based analysis [21] . While flux-balance analysis was already employed with great success for a variety of heterotrophic unicellular organisms , the application on phototrophic growth is still in its infancy [22] . In particular , phototrophic growth gives rise to additional computational and conceptual challenges , such as diurnal patterns of light availability . However , before such more complex scenarios can be considered , we need to establish a reference solution for phototrophic growth under constant light . Following the practice of conventional FBA , we assume that intracellular fluxes are organized such that they realize a given cellular objective function , namely maximal biomass yield , for given constraints and exchange fluxes . The biomass objective function ( BOF ) was adapted and modified from Nogales et al . [17] and consists of proteins , DNA , RNA , cell wall components , lipids , soluble metabolites , inorganic ions and pigments . Growth-dependent ATP utilization is included to account for energy requirement of protein synthesis and growth . In addition to cellular growth , the reference solution must accommodate additional processes known to affect cyanobacterial metabolism . We assume a basal growth-independent ATP utlilization for cellular maintenance . Likewise , cyanobacteria are assumed to exhibit residual respiratory activity also during light [23] , [24] . A number of further processes are directly dependent on light input , such as creation of reactive oxygen species ( ROS ) , photoinhibition and photodamage . Scavenging of ROS may result in increased demand of NADPH , whereas photodamage results in high growth-independent turnover of cellular components , most notably the D1 protein of photosystem II [25] . Recently , also the existence of a Mehler-like reaction , differing from its counterpart in higher plants in producing no reactive oxygen species , has been demonstrated [26] , [27] . Unfortunately , quantitative data on these processes are scarce and their interdependencies with growth are only incompletely understood . In order to constrain and parametrize the flux-balance solution under constant light , we assume an average doubling time of 24 h under constant illumination , corresponding to an average growth rate of . Carbon uptake is not constrained and taken up only as bicarbonate ( ) . We only consider net exchange fluxes and do not explicitely account for cycling of inorganic carbon [28] . In addition to growth-related ATP utilization , a basal maintenance of is included . To account for basal respiratory activity in the light , we assume nonzero activity of the terminal oxidase , as well as of the Mehler-like reaction that converts NADPH and to and NADP . Both processes are assumed to take up 10% of evolution of photosystem II , respectively . However , we note that estimates of the respective activities strongly vary in the literature and seem to be highly dependent on the specific growth conditions . For the activity of the terminal oxidase , we follow the values of Helman et al . [29] , who assessed the extent of electron flow via cytochrome oxidase in the light and concluded that consumption by respiratory activity in the light was about 6% that of production . The Mehler-like reaction was recently studied by Allahverdiyeva et al . [27] , who report that under air level conditions approximately 20% of electrons originating from water splitting are targeted to – mainly due to the Mehler-like reactions . We note that the precise values used here may need revision in future studies , but do not qualitatively affect the properties of the optimized flux solution . To account for oxidative stress , superoxide ( ) is created in photosystem II ( PSII ) , and at photosystem I ( PSI ) by the plant-type Mehler reaction . Both processes are assumed to be very low and are assumed to correspond only to 0 . 5% of the respective electron flow . Nitrogen is taken up as nitrate ( ) using an ABC transporter . In the following , unless stated otherwise , light is considered to be the growth limiting factor . Other nutrients , including nitrogen , phosphorus , and sulfur , are not considered limiting . We do not consider uptake of complex molecules , such as glucose or amino acids . Given the constraints and conditions defined above , a solution for the flux-optimization problem was obtained using the COBRA toolbox [30] and verified using FASIMU [31] , both giving identical results . The reference solution under constant light is not unique . A graphical overview is given in Figure 2 . Overall , the solution is in good agreement with previous studies [16] , [17] . As expected , autotrophic growth is based on assimilation of carbon dioxide by the ribulose-1 , 5-bisphosphate carboxylase/oxygenase ( RuBisCO , EC 4 . 1 . 1 . 39 ) . RuBisCO converts one molecule of ribulose-1 , 5-bisphosphate ( RuBP ) and to two molecules of glycerate-3-phosphate ( PG3 ) . To ensure sustained growth , PG3 is then utilized to regenerate RuBP via the Calvin-Benson-Bassham ( CBB ) cycle , resulting in a surplus of one molecule of PG3 for each 6 molecules of PG3 generated by the cycle . Energy ( ATP ) and reducing power ( NADPH ) are provided by the photosynthetic light reactions . Beyond the CBB cycle , flux towards biomass synthesis drops considerably in terms of absolute magnitude . During phototrophic growth , flux through the tricarboxylic acid ( TCA ) cycle is non-cyclic and acts as a hinge to distribute metabolic precursors for growth . Within the TCA cycle fumarate that originates as a by-product of purine synthesis is re-channeled into metabolism . In addition to maximizing the biomass objective function , the flux solution for phototrophic growth also incorporates synthesis of storage compounds that are then re-utlized during periods of darkness . For simplicity , within our computational analysis , we consider glycogen as the only storage compound . The corresponding flux is . We observe no qualitative changes in flux distribution for varying light intensity , as long as light remains the growth limiting factor . To discuss the validity of the computational flux-optimization , a comparison with experimentally obtained flux values is crucial . To this end , Young et al . [32] recently presented a photoautotrophic flux map based on dynamic isotope labeling measurements . The experimental flux distribution is in good agreement with results obtained with FBA , notwithstanding several interesting differences . When optimizing for maximal biomass yield , the computational solution consistently assigns non-zero flux through the phosphoketolase ( PHK , EC 4 . 1 . 2 . 22 ) that converts either xylose 5-phosphate ( X5P ) to acetyl phosphate ( AceP ) and glyceraldehyde 3-phosphate ( GAP ) or fructose 6-phosphate ( F6P ) to acetyl phosphate and erythrose 4-phosphate ( E4P ) . The phosphoketolase therefore effectively acts as a shortcut from the CBB cycle to acetyl coenzyme A ( acetyl-CoA ) and as a bypass of the releasing reaction catalyzed by the pyruvate dehydrogenase complex ( PDH ) . Interestingly , the phosphoketolase pathway was previously discussed as an innovative solution for pentose catabolism in Saccharomyces cerevisiae [33] and L-glutamate production in Corynebacterium glutamicum [34] . The presence of the phosphoketolase also has a significant influence on the PGK/PGM branchpoint that diverts flux from the CBB cycle . With non-zero flux through PHK , the PGK/PGM ratio is approximately , whereas in the absence of the PHK , the ratio is , in agreement with previous studies [13] , [17] . The value determined experimentally by Young et al . [32] is approximately , allowing no definite conclusions on the validity of either solution . A further discrepancy between the two flux maps is the role of the malic enzyme ( ME ) during phototrophic growth . In accordance with earlier studies on heterotrophic growth [35] , Young et al . [32] report a non-zero flux through ME , suggesting that the enzyme might be involved in concentrating intracellular analogous to its role in C4 plants . However , since such a cycle dissipates energy , FBA will not select for a non-zero flux in the absence of further constraints . Rather surprisingly , the computational study of Nogales et al . [17] nonetheless report such a non-zero flux through the ME . However , this study considered carbon limited growth in the excess of light – resulting in various energy dissipating cycles . Indeed , flux variability analysis of the model of Nogales et al . [17] shows that absence of flux through the ME reaction is likewise compatible with optimal growth . Finally , the experimental study of Young et al . [32] reveals an unexpected residual flux through the oxidative pentose ( OPP ) pathway . As during phototrophic growth , NADPH is excessively produced by the light-driven electron transport chain ( ETC ) , the flux through OPP pathway fulfills no obvious cellular requirements and only results in small but significant loss of fixed carbon . As argued by Young et al . [32] , the detected flux may therefore represent an incomplete suppression of OPP pathway during night/day transitions . Again , such a suboptimal flux state is not recovered by FBA in the absence of additional constraints . However , it demonstrates the utility of FBA to identify suboptimal solutions and the necessity to also consider sub-optimal states in network analysis . Indeed , it has been shown previously that cellular metabolism can maintain a ‘standby mode’ in anticipation of changing environmental conditions at the expense of optimal growth [36] , [37] . This trade-off between flexibility and efficiency requires the investment of additional resources and is likely to affect diurnal growth of photosynthetic organisms . In addition to phototrophic growth , Synechocystis sp . PCC 6803 has to survive extended periods of darkness , usually relying on endogenous storage compounds that are accumulated when light is available . Unfortunately , data on experimental flux patterns under prolonged darkness are scarce . To nonetheless approximate metabolic flux under periods of darkness , we assume that , unlike for some diazotrophic cyanobacteria , dark metabolism in Synechocystis sp . PCC 6803 is dominated by a low level of cellular maintenance and hence utilization of ATP . Growth is assumed to be minimal , which is in good agreement with experimental observations . As a constraint for the flux-optimization problem , we therefore allow for a maximal rate of glycogen consumption that only slightly exceeds the requirements for respiratory metabolism and the demand of ATP . In addition to ATP utilization , we assume residual growth , or , equivalently , a residual cellular turnover that is likewise approximated by the biomass objective function [13] . The dark optimization problem therefore seeks to maximize the BOF under conditions of limited glycogen utilization that is only slightly above the requirement for cellular maintenance . We note that , while glycogen is a major respiratory substrate during periods of darkness , Synechocystis sp . PCC 6803 is likely to utilize other substrates as well . As shown recently , mutants impaired in glycogen synthesis have strongly reduced viability in dark-night cycles – however the reduction in viability is not strong enough to confirm glycogen as only storage compound [38] . In the following , the contributions of alternative storage compounds are not considered . The resulting optimal flux patterns shows considerable variability in dependence of detailed assumptions about enzyme specificity and directionality . When the annotated transhydrogenase reaction ( slr1239 and slr1434 , EC 1 . 6 . 1 . 2 ) is assumed to be active and allowed to carry reversible flux , redox potential ( NADH ) for respiration is generated via cyclic flux through the TCA cycle and subsequently converted into NADPH . NADPH is mainly fed into the NADPH dehydrogenase complexes ( NDH-1 ) . NDH-1 was reported to be specific for NADPH [23] . A different flux pattern emerges , if the transhydrogenase is assumed to be either absent or only unidirectionally converting NADPH into NADH . In this case , the computational solution suggests that redox potential for respiration ( NADPH ) is predominantly generated by the OPP pathway , with no cyclic flux through the TCA cycle . Utilization of the OPP would be in good agreement with the observation that during heterotrophic growth , a large fraction of the consumed glucose was reported to be oxidized via the OPP pathway [35] . However , if we allow the respiratory complex NDH-1 to utilize both , NADH and NADPH , as substrates then again cyclic flux through the TCA is predicted by the model . The use of the cyclic TCA cycle is also supported by the observation that the succinate dehydrogenase reaction is the main respiratory electron transfer pathway into the PQ pool [23] . The actual flux pattern during periods of darkness cannot be resolved based on the presently available data . We favor a scenario with cyclic flux through the TCA cycle . The estimated flux distributions for dark metabolism are provided as Supplemental Table S3 . Continuing with the analysis under conditions of constant illumination , we seek to discuss specific features of phototrophic metabolism in more detail . In particular , under current atmospheric conditions , photosynthetic productivity is significantly impaired by the fact that RuBisCO exhibits a nonzero affinity for molecular oxygen , instead of , as an alternative substrate . Mostly regarded as an evolutionary relic of the -rich atmosphere in which RuBisCO first evolved , photorespiration is a seemingly wasteful process and effectively withdraws carbon from the CBB cycle . However , recently , also alternative hypotheses have emerged that suggest an essential role for photorespiration in energy-dissipation [39] and other processes [40] . As a rather surprising result , our previous reconstruction has shown that optimization for maximal biomass yield gives rise to a nonzero rate of photorespiration , roughly matching reported experimental values [13] . The reason for this apparent non-optimal route was the absence of stoichiometrically more efficient pathways for the synthesis of the amino acids serine , glycine and cysteine . In particular , as yet , the enzymes phosphoserine transaminase ( EC 2 . 6 . 1 . 52 ) and phosphoserine phosphatase ( EC 3 . 1 . 3 . 3 ) have no known homologues in the genome of Synechocystis sp . PCC 6803 . While a candidate for the latter was recently suggested [41] , and possible candidates for the former are available [M . Hagemann , personal communication] , the pathway must currently still be regarded as incomplete in Synechocystis sp . PCC 6803 . In the absence of an annotated phosphoserine pathway in the metabolic reconstruction , glycine is produced from glyoxylate as a by-product of photorespiration . The situation is similar within the current reconstruction . Optimization with respect to biomass yield again results in a nonzero rate of photorespiration . We therefore investigated three scenarios related to the possible production of the amino acids glycine and serine . The first scenario assumes that the current annotation is not incomplete and the enzymatic activity of a phosphoserine transaminase and phosphoserine phosphatase are indeed absent in Synechocystis sp . PCC 6803 . In this case , glycine , serine , and cysteine are synthesized from glyoxylate that itself is a product of glycolate and hence 2-phosphoglycolate ( 2PG ) , the product of photorespiration . A non-zero rate of photorespiration therefore emerges as a result of the flux optimization problem . See Figure 3 for a pathway map . Quantitatively , the optimal rate of photorespiration is approximately 5% , well within current estimates of photorespiration [42] . However , we note that Young et al . [32] observed a considerably lower rate of photorespiration in similar experimental conditions . Reverting to a simulation of dark metabolism , as defined above , photorespiratory flux is no longer part of the optimal solution . Instead , the residual demand for glycine , serine and cysteine is met by degradation of proline , resulting in a slightly higher yield than photorespiration under non-phototrophic conditions . As argued previously [13] , this switch also shows that stoichiometric efficiency is not a property of an isolated pathway , but must be considered in the context of the flux solution as a whole . We note that this solution is different from the computational results of Nogales et al . [17] , who suggest that RuBisCO oxygenase , but not carboxylase , is active under heterotrophic conditions . As our second scenario , we assumed that as yet unidentified genes encode for a phosphoserine transaminase and phosphoserine phosphatase . When the respective enzymatic steps are introduced into the reconstruction , photorespiration ceases under phototrophic conditions and the RuBisCO oxygenase is no longer part of the optimized solution . Instead serine , and subsequently glycine and cysteine , are produced by the newly introduced phosphoserine pathway . This solution is also in agreement with the results of Young et al . [32] and Huege et al . [42] who both observed that 13C-enrichment of serine was substantially higher than glycine during transient labeling . Within the third scenario , taking into account that photorespiration is presumably an inevitable process under current atmospheric conditions , we investigated the optimal flux distribution in the presence of the phosphoserine pathway while simultaneously forcing a non-zero flux of the RuBisCO oxygenase . Specifically , the flux through RuBisCO oxygenase is constrained with a lower bound of 3% of the carboxylase flux . In this case , flux variability analysis shows that several equivalent flux solutions exists . The photorespiratory intermediate glyoxylate may either be used for the synthesis of glycine , or , stoichiometrically equivalent in terms of biomass yield , may be recycled into the CBB cycle via glycerate . In the latter case , serine is synthesized via the phosphoserine pathway . The corresponding pathway maps are shown in Figure 3 . Overall , the question of the possible activity of the phosphoserine pathway still represents an evolutionary conundrum . Given that photorespiration is considered an inevitable side process under current atmospheric conditions , it seems advantageous to use its products in the stoichiometrically most efficient way . In particular , there seems little incentive to establish or maintain an alternative pathway that results in a stoichiometrically identical yield . Interestingly , the absence of such an alternative pathway would make cellular metabolism dependent on a wasteful side product , which in turn might impede further optimization of the RuBisCO reaction: an evolutionary deadlock . Nonetheless , there is also indirect evidence for the phosphoserine pathway , in particular from transient labeling experiments [32] . Furthermore , one step of the phosphoserine pathway , a 3-phosphoglycerate dehydrogenase ( EC 1 . 1 . 1 . 95 ) , is annotated in the genome of Synechocystis sp . PCC 6803 . The gene was recognized as a hydroxypyruvate reductase ( EC 1 . 1 . 1 . 81 ) in the work of Eisenhut et al . [43] . However , recent evidence indicates that the gene indeed encodes a 3-phosphoglycerate dehydrogenase and an alternative candidate for the hydroxypyruvate reductase has been identified [Martin Hagemann , personal communication] . As a preliminary conclusion , we therefore favor a scenario where the phosphoserine pathway is present , albeit encoded with as yet unidentified genes . In the following , all simulations correspond to the third scenario studied above , with RuBisCO oxygenase activity forced as 3% of it carboxylase activity . The phosphoserine pathway then functions as an auxiliary supply of serine that allows to cope with varying levels of photorespiration . Several recent reconstructions of the metabolic network of Synechocystis sp . PCC 6803 include the metabolic reactions isocitrate lyase ( ICL , EC 4 . 1 . 3 . 1 ) and malate synthase ( EC 2 . 3 . 3 . 9 ) , encoding a bacterial glyoxylate shunt [10] , [12] , [14] . Isocitrate lyase , the first enzyme of the glyoxylate shunt , splits isocitrate to succinate and glyoxylate . While the corresponding genes are not annotated within the genome , the decision to include the glyoxylate shunt was partly motivated by reports that the respective enzymatic activities have been detected experimentally [35] , [44] , [45] . Also , genes for a functioning glyoxylate shunt were recently identified in the genome of Cyanothece strains [46] , albeit with no homologues in Synechocystis sp . PCC 6803 . However , as argued previously [13] , the experimental reports are not conclusive . To resolve the discrepancy and to test the functional implications of a glyoxylate shunt in phototrophic metabolism , we therefore performed experimental validation of the enzymatic steps and investigated different scenarios using constraint optimization . When the isocitrate lyase is introduced into the current reconstruction , the enzymatic step is indeed utilized within the optimized flux distribution . In this case , isocitrate lyase is used to synthesize glyoxylate , providing a precursor for the amino acids glycine , serine , and cysteine . Correspondingly , under these conditions , the photorespiratory flux within the optimized computational flux solution is zero . However , if the phospho-serine pathway is assumed to be present , no flux through either the full glyoxylate shunt or the isocitrate lyase is obtained . Likewise , flux through the isocitrate lyase is obtained for dark metabolism , providing glyoxylate for cellular turnover . It is noted that for the reconstruction of Shastri and Morgan [10] , the glyoxylate shunt was introduced to close the otherwise incomplete cyanobacterial TCA cycle . Indeed , under some conditions , the isocitrate lyase has a predicted non-zero flux in dark metabolism , beyond the synthesis of glyoxylate , that is discussed in more detail below . To experimentally resolve the possibility of enzymatic activity of the isocitrate lyase in Synechocystis sp . PCC 6803 , we applied a refined methodology to determinate isocitrate lyase activity in cell-free extracts of Synechocystis sp . PCC 6803 . The method was adopted from Dixon and Kornberg [47] , a standard procedure for detection and quantification of glyoxylate in soluble extracts . Glyoxylate reacts with phenylhydrazine to a phenylhydrazone that can be measured by absorption at . However , other metabolites with reactive keto or aldehyde groups lead to the same reaction . Particular attention must therefore be paid to 2-oxoglutarate , likewise a product of isocitrate , resulting from decarboxylation catalyzed by the isocitrate dehydrogenase in presence of the co-substrate NADP . Measurements were therefore performed with crude extracts of Synechocystis cells , passed over a PD-10 gel filtration column to remove small reactive metabolites and NADP . There is no significant isocitrate lyase activity detectable in filtered crude extracts of Synechocystis sp . PCC 6803 ( Figure 4 , Trace B ) . Positive control for ICL activity is provided in Supplemental Text S1 and Figure S4 . If the filtration step was omitted , a small increase in A324 nm was measured even in the absence of isocitrate by reactive metabolites in the crude extract . After addition of isocitrate a much higher increase in A324 nm is present ( Figure 4 , Trace A ) , similar to measurements of other authors [44] , [45] . These results demonstrate that the measured increase of A324 nm in crude extracts that were not passed over the gel filtration column primarily results from the formation of 2-oxoglutarate , analyzed by an alternative enzymatic test for 2-oxoglutarate quantification ( not shown ) . The residual activity is due to the presence of small amounts of NADP in the unfiltered crude cell extract that , after reduction to NADPH , can be reoxidized by unspecific oxydases resulting in the cyclic formation of NADP , the co-substrate of isocitrate-dehydrogenase . Therefore , we conclude that the presumed isocitrate lyase activity observed by other authors is most likely the result of the isocitrate dehydrogenase reaction . Further evidence for the absence of a glyoxylate shunt is provided by the fact that Synechocystis sp . PCC 6803 lacks the capability to utilize acetate as sole carbon source in the presence of the photosystem II inhibitor DCMU ( photoheterotrophic growth ) . Model simulations clearly show that in such a case , in the presence of the glyoxylate , photoheterotrophic growth is possible . Experimental results are shown in Figure 4B . As one of the most iconic pathways in central metabolism , the TCA cycle has a dual role of oxidizing respiratory substrates for ATP synthesis and providing precursor metabolites , such as oxaloacetate and 2-oxoglutarate , for biosynthesis [48] . Until recently , it was widely assumed that cyanobacteria have an incomplete TCA cycle and lack the genes encoding for the 2-oxoglutarate dehydrogenase ( OGDH ) complex . Correspondingly , almost all published reconstructions to date incorporate only an incomplete TCA cycle and rely on auxiliary reactions to allow for cyclic flux . For example , the analysis of Shastri and Morgan [10] assumed the presence of a glyoxylate shunt to close the cycle . Within the reconstruction of Knoop et al . [13] flux is channeled through the GABA shunt , constituting a bypass from 2-oxoglutarate , via glutamate , -aminobutyrate ( GABA ) and succinate semialdehyde , to succinate . However , recently , the misconception about the incompleteness of the cyanobacterial TCA cycle was corrected [49] . Many cyanobacteria , including Synechocystis sp . PCC 6803 , have genes encoding for two enzymes that replace the lacking OGDH complex: A 2-oxoglutarate decarboxylase ( sll1981 , EC 4 . 1 . 1 . 71 ) and a succinate semialdehyde dehydrogenase ( slr0370 , EC 1 . 2 . 1 . 16 ) . Together these two enzymes constitute a shortcut that closes the incomplete TCA cycle . See Figure 5 for a corresponding pathway map . The recent discovery of a closed cyanobacterial TCA cycle calls to reconsider the role of the cycle under different growth conditions . As outlined above , the photoautotrophic flux map obtained from FBA shows that during phototrophic growth the TCA cycle carries non-cyclic flux . In this case , the TCA cycle mainly provides oxaloacetate and 2-oxoglutarate for growth and incorporates fumarate originating in purine synthesis back into metabolism . According to the computational flux map , the two newly discovered enzymes carry no flux – consistent with the autotrophic lifestyle and highlighting the fact that the conventional closed TCA cycle is only one way how that flux through the component reactions can be organized [48] , [50] . However , absence of flux through the TCA shortcut during phototrophic growth is not fully concordant with the reported experimental observation that , at least for Synechococcus sp . PCC 7002 , mutants lacking the OGDH complex and SSADH grow at a slower rate than the wildtype also during constant illumination , as well as in light-dark cycles [49] . The predicted optimal flux pattern changes in periods of darkness . Apart from special conditions , respiratory metabolism in the dark phase typically requires cyclic flux through the TCA cycle to drive ATP synthesis . To evaluate the role of cyclic flux during periods of darkness , we distinguish between four putative scenarios to close the cyanobacterial TCA cycle: ( i ) a conventional bacterial TCA cycle , involving an OGDH complex that is not annotated in cyanobacteria; ( ii ) the actual cyanobacterial TCA cycle using a 2-oxoglutarate decarboxylase and a succinic semialdehyde dehydrogenase; ( iii ) an incomplete TCA cycle that is closed by the GABA shunt to establish cyclic flux through the cycle; as well as ( iv ) a glyoxylate shunt , via isocitrate lyase activity , to establish cyclic flux through the cycle . Within our previous reconstruction [13] , flux through the GABA shunt was used during respiratory metabolism . The respective sequence of reactions , shown in Figure 5D , is stoichometrically identical to the newly discovered shortcut of Zhang and Bryant [49] , shown in Figure 5B . Therefore , in the context of FBA , both cycles result in identical yield . Interestingly , this yield is below the yield of the conventional cycle using a OGDH complex , therefore representing a seemingly sub-optimal solution for respiratory metabolism . To solve this evolutionary conundrum , Nogales et al . [17] argue that the GABA shunt may nonetheless be an evolutionary favorable solution – based on the finding that the flux forced through the GABA shunt during phototrophic growth results in no reduction of growth , as compared to flux forced through the OGDH complex . However , this difference cannot be recovered using the TCA bypass identified by Zhang and Bryant [49] . An explanation of this discrepancy is provided in Materials and Methods . Rather , the explanation for the stoichiometric inefficiency of the cyanobacterial TCA bypass using a 2-oxoglutarate decarboxylase and a succinic semialdehyde dehydrogenase , instead of the conventional OGDH complex , might be the difference in protein synthesis requirements of both pathways . The OGDH complex is a highly sophisticated multiprotein machine , analogous to the pyruvate dehydrogenase ( PDHC ) complex , and is encoded by three subunits . In E . coli , the complex consists of a 24-mer core of its E2 component , encoded by the gene sucB , with an as yet unclear stoichiometry of its two other components [51] , [52] . In contrast , both , the bypass of Zhang and Bryant [49] , as well as the GABA shunt are encoded by comparatively simple enzymes . An overview of amino acid requirements is provided in Supplemental Table S5 . Given the complexity of the OGDH complex and the relative unimportance of cyclic flux through the TCA cycle for phototrophic growth , such a difference in enzyme investment may result in a trade-off between enzymatic efficiency and enzyme synthesis costs . Indeed , it is increasingly recognized that maximization of molar yield is not necessarily a universal principle of metabolism [53] , [54] . Interestingly , also the isocitrate lyase , if inserted into the model , results in a slightly higher biomass yield during dark metabolism . In this case , succinate is utilized by the succinate dehydrogenase ( SDH ) to close the flux through the TCA cycle . In most natural habitats cyanobacterial metabolism is subject to a diurnal cycle of light availability , resulting in significant change and re-organization within the metabolic network . Correspondingly , cyanobacteria are the only known prokaryotes with an endogenous circadian clock that acts as an intracellular zeitgeber [55] . Considerable effort has been invested to elucidate the cyclic behavior of cyanobacterial metabolism using high-throughput data [22] , [56]–[60] . However , all current large-scale reconstructions exclusively focus on heterotrophic growth or phototrophic growth under constant illumination . Here , we seek to augment the picture by an analysis of the temporal coordination of cyanobacterial metabolism , by simulating the diurnal cycle of phototrophic metabolism . To incorporate circadian changes into a large-scale model of metabolism is not trivial . In general two approaches are available: Following a bottom-up approach , large-scale data on transcript or protein expression may be used to constrain the availability of certain enzymatic interconversions . However , transcript or protein abundance must not necessarily correspond to metabolic flux and often contradicting expression values for single pathways or isoenzymes are observed . Indeed , a recent analysis of paired mRNA-protein abundance in light-dark synchronized cultures of the cyanobacterium Prochlorococcus MED4 showed only poor correlation between mRNA and protein abundance [60] . Also , only small changes in relative enzyme abundance over the entire time-course were observed [60] . We therefore conjecture that a straightforward integration of transcriptomic data to constrain metabolic flux , as already applied for heterotrophic bacteria [61] , is not a suitable strategy to describe the periodic diurnal cycle of cyanobacterial metabolism . Instead , we follow a top-down approach , such that the cellular objectives are defined as a function of time and change in accordance with light availability . Specifically , we use a recently obtained dataset on cyclic transcript behavior in Synechocystis sp . PCC 6803 [79] to obtain insight into the temporal coordination of cyanobacterial metabolism . The resulting expression patterns of metabolic enzymes , provided as Figure S2 , give important references to constrain the temporal coordination of phototrophic growth . In particular , unlike for some diazotrophic strains [56] , the overwhelming majority of oscillatory genes peak during day , indicating a strongly reduced expression activity during the night . Among those transcripts whose expression is highest during night , most are associated with transport processes and , to a lesser extend , TCA cycle activity – in particular the transcripts corresponding to the TCA bypass identified by Zhang and Bryant [49] . Indeed , given that many transport processes relate to the uptake of growth-limiting micronutrients , such as iron or manganese , there is little reason to assume that transport ceases during night . We then have to augment the view from expression data with physiological data obtained for Synechocystis or other cyanobacterial strains . For example , for Cyanothece sp . ATCC 51142 measurements of biomass and chlorophyll concentration , both by optical density proxy , indicate that chlorophyll concentration rises sharply from early morning to well before noon , and remains constant afterwards [57] . In contrast , biomass , does only significantly increase after the end of chlorophyll accumulation , corresponding to increase in storage compounds [57] . Furthermore , for Synechococcus PCC 7942 it was reported that , while cells are dividing rhythmically , DNA synthesis proceeds at a constant rate , effectively uncoupling DNA synthesis and cell division [62] . Based on these empirical observations , we constructed a putative time-resolved biomass objective function , shown in Figure 6 , to simulate diurnal metabolic activity of the metabolism of Synechocystis sp . PCC 6803 . Instead of a single BOF , the biomass components are synthesized according to the following rules: We assume constant rate of uptake of micronutrients ( inorganic ions ) . Likewise , DNA is assumed to be synthesized at a constant rate . All other biomass components are represented by a factor in a BOF that is optimized according to light availability . To mimic results on pigment fluorescence , the factor for pigments increases two hours before sunrise and decreases again after noon . In contrast , the factor corresponding to storage synthesis only increases after noon . In addition , ATP requirements for protein synthesis are included in the BOF , corresponding to an increased demand of ATP during growth . Similar to the dark simulation discussed above , the solution assumes a residual respiration also during periods of light availability , implemented as a lower bound for the corresponding flux . Formation of superoxide and the Mehler reaction is light dependent . During night glycogen is used to drive cellular respiration and maintenance . Biomass accumulation makes use of dynamic FBA [63] , computational details are provided in the Materials and Methods . The results of a full diurnal simulations are shown in Figure 6 . We observe complex transitions in metabolic flux over the full 24 h period , shifting from respiration-dominated night metabolism , to biosynthesis and growth during the day . By construction , pigments rise early in the morning and remain approximately constant after noon . Glycogen content sharply increases during the second half of the day and is utilized during night . The time-courses of selected metabolic fluxes are provided in Figure 7 . Shown in Figure 7A is the net-uptake of oxygen that is positive in the absence of light and follows light availability during the day; Figure 7B shows the flux through the RuBisCO reaction that describes photosynthetic activity and matches availability of energy . Figure 7C shows flux through the phosphoglycerate kinase with small negative flux during night corresponding to the utilization of glycogen and large positive flux during the day corresponding to the regeneration of the Calvin-Benson cycle . Figure 7D shows the interconversion of G1P and G6P with a positive flux corresponding to glycogen degradation and negative flux during the day , corresponding to glycogen , as well as lipid , synthesis . Figure 7E shows the interconversion of CTP and CDP corresponding to synthesis of pigments and DNA . Finally , Figure 7F shows the succinate-semialdehyde dehydrogenase that closes the TCA cycle and exhibits positive flux during night and no flux otherwise . A depiction of diurnal temporal changes over the entire network is provided as Figure S3 . Phototrophic microorganisms hold great promises as a resource to generate high-value products and biofuels using only atmospheric carbon dioxide , sunlight , and some minerals . In this respect , cyanobacteria have attracted recent attention as a possible chassis for the generation of third generation biofuels . We presented an updated and extended genome-scale reconstruction for the unicellular cyanobacterium Synechocystis sp . PCC 6803 . The updated reconstruction of the metabolic network is based on several existing reconstructions and incorporates novel results with respect to the cyanobacterial TCA cycle , an alleged glyoxylate shunt , as well as the role of photorespiration in cellular growth . The model includes various aspects specific for phototrophic metabolism , such as a light-dependent generation of reactive oxygen species . In addition to the model itself , which is encoded and made available in SBML format , we prepared a detailed graphical overview to facilitate discussion also among non-experts . The focus of our analysis was phototrophic growth of the organism , in particular the functional consequences of dissenting or unclear pathway topologies . Indeed , despite several recent reconstructions , and the extensive biochemical literature available for Synechocystis sp . PCC 6803 , several key reactions steps remain unclear . We evaluated several alternative possibilities to close the cyanobacterial TCA cycle , and compared biomass yield for different scenarios . An evolutionary advantage of the GABA shunt over the traditional OGDH , as proposed by Nogales et al . [17] , could not be confirmed . However , the recently identified TCA bypass of Zhang and Bryant [49] , as well as the GABA-shunt , require considerably smaller investment in enzyme synthesis than the OGDH complex – and therefore might be evolutionary advantageous for unicellular organisms that primarily rely on phototrophic growth . To reconcile existing reconstructions , we experimentally tested for the presence of an alleged glyoxylate shunt , included within several recent reconstruction based on biochemical evidence . We could not confirm enzymatic activity of the isocitrate lyase under the conditions tested . Neither was Synechocystis sp . PCC 6803 able to grow on acetate in the presence of DCMU to inhibit PSII and water oxidation and thus linear electron transport , but not PSI cyclic electron transport and ATP generation . Both facts strongly suggest the absence of a functional glyoxylate shunt . As a feature that is specific to phototrophic organisms , the re-organization of metabolism in alternating diurnal light/dark cycles was demonstrated . As yet , almost all existing reconstructions have focussed on an evaluation of hetero- , mixo- , or phototrophic growth under constant light , with little reference to the actual environmental conditions the organism experiences . While such a simulation is clearly in its infancy , data on gene expression and physiological properties allow to describe a basic diurnal metabolic cycle of the organism . We are confident that similar computational approaches are required to obtain a better understanding of principles and trade-offs during phototrophic growth . In addition to the evaluation of a diurnal cycle , our reconstruction highlights several open questions with respect to cyanobacterial metabolism that deserve future attention . In particular , dark metabolism , as well as the interplay between oxygenic photosynthesis and aerobic respiration taking place in a single compartment are still insufficiently understood . We also conjecture that large-scale metabolic network modelling has to move beyond the stoichiometric reconstruction process itself and increasingly has to take into account additional biophysical constraints , such as photorespiration and the generation of reactive oxygen species , as well as suboptimal flux distributions to elucidate and explain observed metabolic behavior .
Although several reconstructions of the cyanobacterium Synechocystis sp . PCC 6803 have recently become available , only few attempts have been made to systematize the missing metabolic knowledge . Indeed , several aspects of the metabolic network and its main synthesis pathways are still insufficiently understood . For example , within the current reconstruction , the amino acids methionine and asparagine lack a complete synthesis pathway . To ensure viability in silico , the synthesis steps from Microcystis aeruginosa have been adopted for the synthesis of methionine . Asparagine is assumed to be synthesized from aspartate via an asparagine synthetase ( EC 6 . 3 . 5 . 4 ) . Synthesis pathways for all remaining amino acids are annotated , the putative enzymatic steps for serine and glycine are discussed in more detail below . Cyanobacteria utilize glycogen , cyanophycin and polyhydroxybutyrate ( PHB ) as storage compounds . However , the enzymatic steps necessary for breakdown of internal PHB are not known , even though the compound is detected [64] . Enzymatic steps for the synthesis of several components of the cell wall are not annotated , such as UDP-glucose and glycerolipids . Likewise , the annotation of the synthesis pathways of vitamin B6 and B12 are fragmentary . More fundamental , it is not fully known whether plastoquinone or ubiquinone is used within the electron transport chain ( ETC ) . While the synthesis pathway of plastoquinone is partially present in Synechocystis sp . PCC 6803 , a knock-out showed that its absence had no effect on photosynthetic function [65] . Therefore , there might be an additional pathway for plastoquinone or the organism may use ubiquinone . An alternative pathway for plastoquinone was recently suggested [66] . Further missing enzymatic steps were identified using BLAST search in available repositories , starting with related cyanobacterial strains [67] . In addition , primary biochemical literature was screened to identify possible alternative enzymatic routes . A list of missing or unclear enzymatic steps and necessary additions to ensure viability of the organism in silico is included within Supplementary Table S1 . Prior to conversion into final simulation files , the network was tested for elemental and charge balances using the COBRA toolbox [30] and the toolbox SubLiminal [68] . In case of unclear specificity of a reaction for NAD/NADH or NADP/NADPH , only the latter was included . Within the section ‘Metabolic flux during periods of darkness’ , the glutamate dehydrogenase ( GDH ) reaction was assumed to be irreversible to avoid metabolic cycles that compensate for the lack of the transhydrogenase . The reconstructed network file ( Supplemental Dataset S1 ) is compliant with MIRIAM . When available , all metabolites are referenced by their corresponding CheEBI ID [69] . We note that the intracellular pH of Synechocystis sp . PCC 6803 changes under diurnal conditions , from approx to [70] . For simplicity , we use the values as a reference condition . The network file used for simulation with the COBRA toolbox is included as Supplemental Dataset S2 . While metabolic reconstruction of phototrophic organisms are still underrepresented as compared to heterotrophic microorganisms , recently a number of cyanobacterial reconstructions have become available . The first application of FBA on cyanobacterial metabolism was performed by Shastri and Morgan [10] , followed by an extension of the model by Hong and Lee [11] . Both reconstructions are comparatively small , with a focus on central metabolism . The models contain an incomplete TCA cycle and an alleged glyoxylate shunt . The first large-scale model was provided by Fu [12] . However , the reconstruction only involved little manual curation and actual flux was restricted to central metabolism , mainly because of the use of a restricted biomass objective function . The model also included an alleged glyoxylate shunt . An improved reconstruction was presented by Knoop et al . [13] , albeit still limited in size . The reconstruction went beyond pathway repositories and included the first detailed representation of the photorespiratory pathways in a metabolic reconstruction , did not include the glyoxylate shunt . Instead the GABA shunt was active to close the incomplete TCA cycle . Shortly afterwards , three additional reconstructions were published [14]–[16] , each again incorporating an incomplete TCA cycle and an alleged glyoxylate shunt . The analysis of Yoshikawa et al . [16] also compares the solutions of Knoop et al . [13] and Montagud et al . [14] under conditions of heterotrophic growth . A further reconstruction was presented by Nogales et al . [17] , making use of an improved biomass function derived from literature data . The reconstruction does not include a glyoxylate shunt but does also not incorporate the TCA bypass of Zhang and Bryant [49] . Subsequently , a reconstruction of Cyanothece sp . ATCC 51142 [71] , as well as of Synechococcus sp . PCC 7002 [72] was presented . Recently , a comparison of the metabolic potential of the strains Cyanothece sp . ATCC 51142 and Synechocystis sp . PCC 6803 was performed [18] . Network reconstruction is also increasing performed algorithmically [73] , based on genomic similarity [67] . However , in such cases , manual reconstructions remain the gold standard to test the validity of automatically generated networks . A tabular overview of existing reconstructions is provided as Supplemental Text S2 . Nogales et al . [17] argue that having a GABA shunt instead of a complete TCA cycle may represent an evolutionary advantage in autotrophic conditions at the expense of reduced growth performance in heterotrophic conditions . This hypothesis was tested by alternatively forcing flux through a potential OGDH complex , as well through the glutamate synthase in autotrophic and heterotrophic conditions . Forced flux through the OGDH complex leads to an immediate and strong reduction of growth under autotrophic conditions , whereas forced flux through the glutamate synthase only leads to a slight reduction of autotrophic growth at high flux rates . However , as detailed in the main text , since autotrophic growth does not require a closed TCA cycle , forced flux through the OGDH complex therefore represents an unnecessary metabolic burden under this condition . On the other hand , the glutamate synthase carries non-zero flux also during autotrophic growth . To introduce a forced lower bound is therefore expected to have no immediate impact and only affects growth at if a high flux is forced . Indeed , if flux is forced instead through the succinic semialdehyde dehydrogenase , rather than the glutamate synthase , similar results as for the OGDH complex are obtained . We suggest that a possible explanation for the absence of the OGDH complex , despite the higher stoichiometry yield of the respective pathway , are the high synthesis requirements for the OGDH multiprotein complex . For the modelling of a full diurnal cycle under usage of the flux balance analysis we assume a doubling time of about 24 h . Since FBA estimates the flux towards biomass components , given in mmol h−1 gDW−1 , we integrated the flux values over 24 h to get an estimate of the total amount of each component during one diurnal cycle . Based on these given amounts , we postulated a scenario for a full diurnal cycle . We assume a continuously synthesis of the biomass components ‘DNA’ and ‘inorganic ions’ . The components ‘Protein’ , ‘RNA’ , ‘Cell wall’ and ‘lipids’ are only synthesized during the light phase of the simulation . The synthesis of pigments starts two hours before the beginning of the light phase and ends two hours before the start of the dark phase . To account for the demand for energy during the dark phase , when no light is available , we assume that glycogen , synthesized and accumulated from the middle of the light phase on , is used as storage compound . During the light phase a steady respiration rate of the cytochrome c oxidase of is assumed , which corresponds to about 10% of the maximally photosynthetically produced , as well as a general ATP consumption for cellular maintenance of . In the absence of light , the basal respiration rate is decreased to only and ATP production is set as objective function . The full diurnal period was subdivided into steps . For each step a new biomass function was assigned . The transition between different biomass configurations , as outlined above , was smoothened by dividing the difference of the weight factors within the BOF by the amount of steps between the configurations and changing them stepwise . The time-dependent BOF is provided in Supplemental Table S3 . Light was assumed to follow a triangular shape , starting at circadian time and peaking at with a value of . Synechocystis sp . PCC 6803 was grown in BG11-medium at under continuous illumination with white light of and a continuous stream of air . Cultures were synchronized with three cycles of light/dark 12 h∶12 h prior sampling . Over a 24 h time course , 6 samples for RNA isolation were taken 30 minutes before and after light is switched off , ( sample 1 , 2 ) , 30 minutes before midnight ( sample 3 ) , 30 minutes before and after light onset ( sample 4 , 5 ) and 30 minutes before noon ( sample 6 ) . Two replicates were prepared from two synchronously growing cultures . The microarray design and hybridization procedure have been described previously [75] . The “Agilent Feature Extraction Software 10 . 5 . 1 . 1” was used for extraction of the spot intensities . In accordance to findings in other cyanobacterial species [56] , [76] , [77] we observe a large number of genes with diurnal expression patterns and a global trend towards higher gene expression levels over the subjective day . We adopted an approach similar to Calza et al . [78] by first finding a set of observed expression profiles which exhibit the lowest degree of diurnal oscillation . We assume , that the expression of this set of genes ( Least-Oscillating-Set or LOS ) remains unchanged and variation exclusively reflects technical variation . The Loess curve calculated between LOS gene expressions in each microarray and the LOS gene mean expressions is then applied to the entire microarray . The oscillation strength of an expression profile was measured by the power spectral density corresponding to a frequency of 1/d ( pd ) . By repeated shuffling and pd computation for 105 times , we obtained a p-value for randomly observing each genes diurnal oscillatory behavior . Genes with where included in the LOS , yielding a total of 1173 genes . The phase for each expression profile was also calculated using the Fourier transformation . Details on experimental and computational analysis are described in Lehmann et al . [79] . The following abbreviations are used in Figure 2: 2OG ( 2-Oxogluterate ) , 2Oiv ( 2-Oxoisovalerate ) , 2PGL ( 2-Phosphoglycolate ) , 3PG ( 3-Phospho-glyceroyl phosphate ) , 4AB ( 4-Aminobutanoate ) , 5PrPP ( 5-Phospho-ribose 1-diphosphate ) , A4Sa ( Aspartate 4-semialdehyde ) , AcCoA ( Acetyl-CoA ) , Ace ( Acetate ) , AceP ( Acetyl phosphate ) , ADP ( Adenosine 5′-diphosphate ) , AIR ( Aminoimidazole ribotide ) , Ala ( Alanine ) , Arg ( Arginine ) , Asn ( Asparagine ) , Asp ( Aspartate ) , ATP ( Adenosine 5′-triphosphate ) , ATPase ( ATP synthase ) , bCaro ( beta-Carotene ) , Chlp ( Chlorophyll a ) , Chor ( Chorismate ) , Cit ( Citrate ) , COX ( Cytochrome c oxidase ) , Cys ( Cysteine ) , Cyt b6f ( Cytochrome b6-f complex ) , DGDG ( Digalactosyl-diacylglycerol ) , DHAP ( Dihydroxyacetone phosphate ) , DX5P ( 1-Deoxy-xylulose 5-phosphate ) , E4P ( Erythrose 4-phosphate ) , Echi ( Echinenone ) , F6P ( Fructose 6-phosphate ) , FAD ( Flavin adenine dinucleotide ) , FBP ( Fructose 1 , 6-bisphosphate ) , FNR ( Ferredoxin-NADP reductase ) , Fum ( Fumarate ) , G1P ( Glucose 1-phosphate ) , G6P ( Glucose 6-phosphate ) , GAP ( Glyceraldehyde 3-phosphate ) , gCaro ( gamma-Carotene ) , GgPP ( Geranylgeranyl diphosphate ) , GL ( Glycolate ) , Gln ( Glutamine ) , Glu ( Glutamate ) , GLX ( Glyxoylate ) , Gly ( Glycine ) , GSH ( Glutathione ) , His ( Histidine ) , Hser ( Homoserine ) , Icit ( Isocitrate ) , Ile ( Isoleucine ) , IpPP ( Isopentenyl diphosphate ) , Leu ( Leucine ) , LpAD ( Lipid A disaccharide ) , Lys ( Lysine ) , MaCoA ( Malonyl-CoA ) , Mal ( Malate ) , mDom ( meso-2 , 6-Diaminopimelate ) , Met ( Methionine ) , MGDG ( Monogalactosyl-diacylglycerol ) , NAD ( Nicotinamide adenine dinucleotide ) , NADP ( Nicotinamide adenine dinucleotide phosphate ) , NDH ( NADPH dehydrogenase ) , OA ( Oxaloactete ) , PC ( Plastocyanin PEP ( Phosphoenolpyruvate ) , PepGlc ( Peptidoglycan ) , PG ( Phosphatidylglycerol ) , PG2 ( Glycerate 2-phosphate ) , PG3 ( Glycerate 3-phosphate ) , Phe ( Phenylalanine ) , Pho ( Phosphate ) , Phyq ( Phylloquinone ) , Ppg ( Protoporphyrinogen ) , PPP ( Phytyl diphosphate ) , PQ ( Plastoquinone ) , Prep ( Prephenate ) , Pro ( Proline ) , PS I ( Photosystem I ) , PS II ( Photosystem II ) , Ptd ( Phosphatidate ) , Ptsn ( Putrescine ) , Pyr ( Pyruvate ) , R5P ( Ribose 5-phosphate ) , Rbfv ( Riboflavin ) , Ru5P ( Ribulose 5-phosphate ) , RuBP ( Ribulose 1 , 5-bisphosphate ) , S7P ( Sedoheptulose 7-phosphate ) , SAM ( S-Adenosylmethioninamine ) , SBP ( Sedoheptulose 1 , 7-bisphosphate ) , ScCoA ( Succinyl-CoA ) , SDH ( Succinate dehydrogenase ) , Ser ( Serine ) , Spmd ( Spermidine ) , SPP ( Solanyl diphosphate ) , SQDG ( Sulfoquinovosyldiacylglycerol ) , Ssa ( Succinate semialdehyde ) , Suc ( Succinate ) , Sul ( Sulfur ) , THF ( Tetrahydrofolate ) , ThPP ( Thiamin diphosphate ) , Thr ( Threonine ) , Toco ( Tocopherol ) , Trp ( Tryptophan ) , Tyr ( Tyrosine ) , Upg III ( Uroporphyrinogen III ) , UpPP ( Undecaprenyl diphosphate ) , Val ( Valine ) , Vit B12 ( Vitamin B12 ) , Vit B6 ( Vitamin B6 ) , X5P ( Xylulose 5-phosphate ) , Zea ( Zeaxanthin ) .
|
Phototrophic microorganisms hold great promises as a resource to generate high-value products and biofuels using only atmospheric carbon dioxide , light , and some minerals . In particular cyanobacteria , the only known prokaryotes capable of oxygen-evolving photosynthesis , have attracted recent attention as a possible chassis for the generation of third generation biofuels . Rational engineering of microorganisms is increasingly guided by large-scale reconstructions of the metabolic network of the respective organism . Such reconstructions then serve as an integrated knowledge base for all metabolic interconversions taking place during cellular growth . Here , we present and analyze such a genome-scale reconstruction for the unicellular cyanobacterium Synechocystis sp . PCC 6803 . Taking into account several recent reconstructions , the functional consequences of unclear and dissenting pathway annotations are discussed . The model is supplemented with experimental data to validate specific reactions steps . As a specific feature of phototrophic organisms , the re-organization of metabolism in alternating diurnal light/dark cycles is studied .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"systems",
"biology",
"biochemistry",
"theoretical",
"biology",
"biology",
"computational",
"biology",
"marine",
"biology"
] |
2013
|
Flux Balance Analysis of Cyanobacterial Metabolism: The Metabolic Network of Synechocystis sp. PCC 6803
|
A striking feature of vascular plants is the regular arrangement of lateral organs on the stem , known as phyllotaxis . The most common phyllotactic patterns can be described using spirals , numbers from the Fibonacci sequence and the golden angle . This rich mathematical structure , along with the experimental reproduction of phyllotactic spirals in physical systems , has led to a view of phyllotaxis focusing on regularity . However all organisms are affected by natural stochastic variability , raising questions about the effect of this variability on phyllotaxis and the achievement of such regular patterns . Here we address these questions theoretically using a dynamical system of interacting sources of inhibitory field . Previous work has shown that phyllotaxis can emerge deterministically from the self-organization of such sources and that inhibition is primarily mediated by the depletion of the plant hormone auxin through polarized transport . We incorporated stochasticity in the model and found three main classes of defects in spiral phyllotaxis – the reversal of the handedness of spirals , the concomitant initiation of organs and the occurrence of distichous angles – and we investigated whether a secondary inhibitory field filters out defects . Our results are consistent with available experimental data and yield a prediction of the main source of stochasticity during organogenesis . Our model can be related to cellular parameters and thus provides a framework for the analysis of phyllotactic mutants at both cellular and tissular levels . We propose that secondary fields associated with organogenesis , such as other biochemical signals or mechanical forces , are important for the robustness of phyllotaxis . More generally , our work sheds light on how a target pattern can be achieved within a noisy background .
The shoot apex is a major organizer of the aerial architecture of vascular plants . Lateral organs ( leaves and flowers ) are successively initiated as primordia at the shoot apex yielding a regular arrangement on the stem known as phyllotaxis . Two main categories of phyllotactic patterns are observed: whorled – many primordia emerge simultaneously , and spiraled – a single primordium is initiated at a time . Spiral phyllotaxis features two sets of conspicuous spirals ( the parastichies ) rotating either clockwise or anti-clockwise , see Figure 1a; the numbers of spirals in each set are often two consecutive numbers of the Fibonacci sequence 1 , 1 , 2 , 3 , 5 , 8 , 13 defined by , ; moreover , the angle ( viewed from the apex ) between two consecutive organs , known as the divergence angle , is often strikingly close to the golden angle , which is about . This mathematical beauty has attracted a stream of mathematicians , computer scientists and physicists along with botanists and plant biologists , see for instance [1]–[3] for reviews . A number of models enabled the prediction of spiral phyllotaxis from the interactions between primordia: physical interactions such as optimal packing , e . g . [4] or mechanical forces , e . g . [5] , [6] , and biochemical interactions such as a reaction-diffusion Turing-like spacing mechanism [7]–[9] or the production of an inhibitor by each primordium preventing initiation in its vicinity [10] , [11] . Common to all these studies , phyllotactic spirals are emerge from the self-organization of interacting primordia , as also shown by more abstract dynamical models [12]–[17] . The concept of self-organization was also supported by the observation of phyllotactic-like patterns in physical experiments with ferromagnetic droplets [12] , self-assembled solidified microstructures [18] , rotating magnets [19] , or bubbles floating on a surface [20] . More recently , biological experiments enabled the identification of the primary mechanism of phyllotaxis [21]–[23] , i . e . the interaction mechanism behind self-organization . It is now thought that the accumulation of the plant hormone auxin in incipient primordia ( initia ) through a self-enhancing polar transport creates an auxin depletion playing the role of an inhibitory field , which was further supported by the simulation of cell-based models [24]–[28] in which phyllotaxis emerges from such cell-cell interactions . Altogether , this body of work is underpinned by an ideal , deterministic view of phyllotaxis , in which perfectly regular patterns can be reproduced by theoretical models . Nevertheless , living organisms are affected by a natural , stochastic variability . Along with a variability among species [1] , phyllotaxis proves to be variable at the inter and intra-individual scales [29] , [30] . Divergence angles turn out to be widely distributed around the golden angle in Arabidopsis thaliana [31]–[34] , and almost random in mutants of Arabidopsis [31]–[34] or of rice [29] , [35] , while short sequences of abnormal divergence angles can occur in sunflower [36] and in Arabidopsis [30] . More generally , a growing attention is given to stochastic variability in organismal development [37] , [38] . In plants , stochasticity can be either reflected in development , as in the variability of cell size in the epidermis of sepals [39] , or filtered out , as in the robust establishment of the identity of floral organs [40] . This raises the question of how stochasticity impacts on phyllotactic patterns . Auxin cell-based models of phyllotaxis are liable to show a noisy output [24] , [26] , [27] , but their high number of parameters makes them difficult to use for a systematic investigation of the link between variability and its causes [41] . In addition , two classes of models appear in recent literature because the molecular mechanisms controlling the polarization in a given cell of auxin efflux facilitators ( PIN-FORMED 1 proteins , abbreviated as PIN1 ) are largely unknown; these two classes posit polarization according to either the flux of auxin through cell walls ( flux-based , [27] ) or to the concentration of auxin in neighboring cells ( concentration-based , [24] , [26] , [42] ) . We therefore chose to use the abstract dynamical system introduced by Douady and Couder in [14] , which recapitulates most observed phyllotactic modes , while it enables a comprehensive exploration of the space of parameters . However , in order to make our results relevant to both cellular and tissular levels , we mapped the two cell-based models on this abstract tissue-level model; this mapping can be used to translate cellular parameters into macroscopic phenotypes and , conversely , phyllotactic observations into cellular behaviours . We incorporated stochasticity in this dynamical system and found that stochasticity yields stereotypical alterations of the phyllotactic pattern and that these alterations vary according to the source and intensity of randomness . Finally , inspired by work on noise in the primary patterning of the fruit fly embryo [43]–[46] , we investigated whether a secondary inhibitory field could reduce the number of phyllotactic alterations and we predicted the necessary properties of such a field .
We used the dynamical system introduced in [14] which implements the rules stated by Snow and Snow [47] . The main hypotheses are as follows ( see Materials and Methods for details ) . ( i ) The average stem apex has an axisymmetric shape . ( ii ) Organ primordia are formed at the periphery of the apex , on a competent circle of radius , and , because of growth , they move away with a radial velocity , which we assume here to be constant . ( iii ) Each primordium is a source of inhibition over a region of diameter and the steepness of gradient of inhibition is also a parameter of the model . ( iv ) A new primordium ( initium ) is initiated on the competent circle at the location and at the time such that the sum of the inhibitory fields generated by all previous primordia gets below a threshold . ( v ) The apex has the shape of a cone and distances are computed on this cone . The angle of the cone was chosen according to the shape of Arabidopsis thaliana apex . A typical simulated spiral sequence is shown in Figure 1c: the inhibition field generated by older primordia in the competent circle decreases as primordia move away , an initium is formed at the place and the time such that inhibition falls below the threshold . This process is repeated leading to a periodic temporal initiation and a spatial establishment of a spiral phyllotactic pattern . Figure 1b illustrates the outcome of this process . A main control parameter of this model is the ratio of the range of inhibition to the radius of the competent circle [14] , which will be referred to as . As this ratio is decreased , the phyllotactic mode undergoes a transition from distichous ( divergence angles of ) to phyllotactic modes of increasing order [14]: spirals with increasing number of parastichies or whorls with increasing number of simultaneous initiations . Neither the periodicity nor the spatiality of initiation are prescribed; they emerge from the self-organization of the system instead [14] . We next questioned whether this abstract tissue level model could be used to interpret observations at the cellular level . To do so , we re-considered cellular models of auxin polar transport [24] , [26] , [27] . We sought how the two main classes of cellular models ( polarization of auxin efflux based on concentration [24] , [26] or based on flux [27] ) can be formulated at the tissue level ( see Models section and Text S1 ) . Together with previous work [24] , [26] , [27] , [48] , our analysis shows that cellular parameters can be mapped on the properties of the abstract model – initiation of primordia close to a circle surrounding the apex , existence of an effective inhibitory interaction between primordia with a well-defined range and steepness , and a threshold for the initiation of a new organ ( see Figure 1C and Figure S1 ) . This mapping enables the interpretation of the effect of the abstract model parameters in terms of cellular parameters . Conversely , cell-based scenarii can be translated in parameter sets of the abstract model ( Figure S1 ) . Accordingly , we subsequently used the abstract dynamical system . In order to investigate the origin of variability of phyllotactic patterns , we incorporated two different sources of noise in this dynamical system ( Figures 2 and 3 ) . Available experimental data demonstrate that some mutations make phyllotaxis more irregular than in wild type plants [31]–[35] . This suggests the existence of processes regulating variability in phyllotaxis: either indirectly through e . g . changes in the radius of the competent zone , in the range of inhibition between primordia , modulation of cell activity , or directly by playing on the level of noise . In the latter case , differences between mutant and wild type would be explained within the framework developed above , i . e . a direct regulation of noise intensity . However , previous work in development suggests that it is often more efficient to control variability by adding appropriate feedbacks or additional modules to filter it [38] . In this framework , we sought mechanisms that could regulate variability in phyllotaxis , by adding another layer of control to the model discussed above .
We investigated the impact of noise on phyllotaxis starting from a deterministic model whereby primordia are sources of an inhibitory field [14] , which can be viewed as an abstract representation of the underlying auxin-based dynamics used in more realistic cell-based models ( see Text S1 for the mapping ) . Initia are formed on a competent circle when the inhibitory fields falls below a given threshold and then the primordia move away due to growth . In this model , the temporality and spatiality of organ initiation emerge from the self-organization of the system . This model reproduces most known types of phyllotaxis [14] and we used it in the range of parameters roughly corresponding to the spiral phyllotaxis observed in Arabidopsis , the main parameter being the ratio of the range of inhibition to the radius of competent circle ( ) . Parameter exploration ( Figures S3 , S4 ) only showed differences in the relative intensity of alterations but did not change the overall conclusions . Most previous theoretical research on phyllotaxis focused on its mathematical regularity . Nevertheless , observations indicate a variability across species and genotypes [1] , [29]–[36] . It also appears that studies on cellular models [17] , [24] , [26] , [27] alluded to noise or robustness . Indeed , different sources of stochastic variability can be envisaged , and four of them are discussed hereafter . ( i ) The discrete nature of the cellular template makes the positioning of an initium according to a given divergence angle only achievable within the precision of a cell radius , as observed in cell-based simulations of auxin transport [24] , [26] , [27] . The amplitude of the corresponding variability is however generally expected to be small: in the relatively small apex of Arabidopsis cell radius is about 5% of the radius of the competent circle . We did not consider this type of noise because its amplitude ( 5% ) is too small to induce the type of defects presented above . ( ii ) The level of inhibition can be noisy as recent work suggests that auxin level fluctuates in the shoot apex [52] . ( iii ) The sensitivity of cells to the signal can be noisy as cellular response can be variable [37] , [38] . We integrated these last two sources in a noise on the threshold for initiation . ( iv ) The activity of the apex can be noisy , which would have an impact on the effective radius of the generating circle [50] , [51] and/or the range of inhibition . As only the ratio , , of these two lengths is important , we modeled a noise on the size of the generating circle . We simulated two sources of noise , on the threshold for initiation and on the size of the generating circle . We found that noise leads to stereotypical alterations , in addition to fluctuations of the divergence angles and plastochrons around their deterministic values: ( i ) transient distichous pattern with angles of ; ( ii ) concomitant initiations corresponding to M-shaped sequences of angles; and ( iii ) reversal of the handedness of the phyllotactic pattern . These types of alterations correspond to an exploration of phyllotactic modes that are neighbors to the spiral mode: distichous for ( i ) ; whorls for ( ii ) ; and the spiral with the opposite handedness for ( iii ) . The proportion of the different alterations varies with the source of noise and its strength . M-shaped sequences are visible in sunflower and Arabidopsis [30] , [36] , angles of occur in a mutant of Arabidopsis [32] , while , to the best of our knowledge , reversals do not happen in nature . A caveat is that long sequences of angles might be required to make sure that a reversal has occurred . A possible explanation for a smaller importance of the noise on size is that the radius of the competent circle ( or the range of inhibition ) is determined by the behavior of all cells in the apex ( respectively the primordium ) which leads to some averaging of cellular noise , while the noise on threshold is a local property of the few cells that define an initium . In addition , the number of stem cells might be determined robustly as many levels of regulation are involved [50] , [51] . Consequently , a prediction of our work is that the noise on threshold , which corresponds mainly to noise in signaling , is the main source of stochasticity in the Arabidopsis shoot apex . We then investigated how the noise on threshold might be corrected . A pre-filter simply corresponds to a modulation of the level of noise . Other filters require the propagation of information from older primordia to an initium or to a primordium . Such a transfer of information might be provided by other hormones [53] , [54] or by mechanical signals [55] . Therefore we sought whether a second field can reduce alterations: a second field acting on the same level as the first field seems unlikely; a field acting post-initiation could play on the age of primordia . We assumed that each primordium that is old enough is a source of the secondary field and that initia sensing this field have their physiological age shifted . This shift may reflect a slowing down or an acceleration of the initiation of primordia or of the emergence of organs . If the shift is negative ( primordia maturation delayed ) , then the number of M-shaped sequences of angles is significantly reduced . At the cellular level , this shift could be implemented for instance by a delay in the activation of primordia-specific genes or by a decrease in the growth rate of an organ . Our secondary field differs from the one introduced in the dynamical system of [17] to stabilize whorled phyllotaxis , as , there , the two fields have the same spatial dependance . Reaction-diffusion phyllotactic models also used a second field [8] to add memory to the system , which turns out to act as a pre-filter . Our secondary field has more resemblance to proposals made for the early development of the fruitfly embryo [43]–[45] at a smaller scale: the diffusion of the secondary transcription factor Hunchback between nuclei would smooth out the interpretation of the noisy gradient of the primary transcription factor Bicoid . In our case , noise reduction is achieved when an initium is made younger if surrounded by young primordia . Therefore our secondary field implements an averaging of age information between neighboring organs . Our investigation of the space of parameters of our secondary field shows that it is more efficient in noise correction when its range is slightly larger than the range of the primary field and when primordia become sources of the secondary field with a delay ranging from a fraction of plastochron to two plastochrons following their initiation ( Figure S8 ) . Indeed mechanical signals , as indirectly reflected by microtubules , seem to become important at about a plastochron following initiation [55] . We predict that other secondary fields should also follow a specific spatial and temporal pattern , in order to be efficient in correction . Although we focused on spiral phyllotaxis , we expect our numerical observations on noise and robustness to also hold for whorled phyllotaxis . It appears that the spatial positioning of organs is rather robust , but that the temporality is more sensitive to noise . Thus secondary fields might be more useful in reducing fluctuations in plastochron . As we have shown that our model properties can be translated into cellular properties , our results can guide specific cellular simulations that address aspects of stochasticity . Conversely , if the fluctuation of a cellular property can be measured in experiments , it can be translated onto our model using the mapping from cell-based models ( Figure S1 ) . Thus our work yields a framework for the analysis of phyllotactic mutants by linking cellular data , the nature of noise , the level of control , and alterations of phyllotaxis . The different layers explored here reflect the complexity of development: inhibitory interactions between primordia emerge from auxin-based cell-cell interactions , phyllotaxis emerges from primordium-primordium inhibitory interactions , a secondary field corrects the phyllotactic pattern by feeding back on cell-behavior . Such a feedback may help achieving a target pattern in a noisy environment and thus provides a general concept in developmental systems biology .
We reimplemented the model of Douady and Couder [14] assuming that ( i ) the stem has the shape of a cone of angle and distances are computed on the cone; ( ii ) initia are formed on a circle of radius ( typical value 2 in arbitrary units ) and then move away with a constant radial velocity ( value 1 ) ; ( iii ) each primordium is a source of inhibitory field that is function of the distance to the primordium , ( typical value 2 ) measures the steepness of the field and ( typical value 3–4 ) its range , the inhibitory field is the sum of the sources due to all existing primordia; ( iv ) an initium appears on the competent circle when and where the total inhibition becomes lower than a threshold ( typical value 1 ) . The dynamical system was implemented in Python . The time of initiation is found using a standard dichotomic solver . At each step of this process , the minimum of the inhibition on the circle is calculated using the optimize library of scipy ( http://docs . scipy . org/doc/scipy/reference/optimize . html ) . The time of initiation is selected whenever this minimum reaches the threshold value . Then an initium is created and the process is repeated . We chose to achieve a precision on time of and on space of and we checked that these precisions were sufficient to achieve convergence . Simulations were generated on a processor Intel Xeon 2 Ghz . We considered the concentration-based model , as formulated by [26] , and the flux-based model of [27] . We studied the continuous limit of these cellular models as in [48] , [57]; the details are presented in the Text S1 . We added two sources of noise in the model , on the threshold of initiation and on the radius of the competent circle . In each case , the threshold ( resp . the radius ) is re-defined , following each initiation , from a random variable of Gaussian distribution of mean ( resp . ) and standard deviation ( resp . ) . We investigated the effect of initialization of the simulations by changing the initial values of the divergence angle , turning on noise before or after convergence to a stationary state . We found our results to be unaffected by the type of initialization ( Figure S2 ) . Therefore , in order to avoid errors on the measurement of the handedness of the phyllotactic pattern , we started each simulation with initial conditions corresponding to the equilibrium of the deterministic model with a right-handed chirality . Once the simulation had reached a steady state , we turned on noise and started recording the sequences of angles and plastochrons . When noise was large enough , we frequently observed a vanishing plastochron . Initia were considered as concomitant when the plastochron is smaller than the time-precision of the simulation . In this case , these initia are equivalent and so we defined their order of apparition at random . In order to separate the subsequent M-shaped patterns from other features of variability , we ignored M-shaped patterns when computing the standard deviation of divergence angles , i . e . the standard deviation was computed from the symmetrised distribution of angles with values in ( 0 , 180 ) . We investigated the effect of a second inhibitory field that is turned on with a delay after initiation and has similar properties to the first inhibitory field . After a delay ( of the same magnitude as the plastochron ) after initiation , each primordium becomes a source of a second inhibition of range ( of the same magnitude as ) , of the same form as the first inhibitory field; the second field is the sum of all the contributions of primordia . The new initium will be formed at the point where the interaction between the two fields reaches the threshold . The interactions tested are of redundant type or synergetic type . In the case of redundant inhibition , the total inhibition sensed by a new initium is of the form where and are the two inhibition fields and a constant modifying the weight of the second field . In the case of synergetic inhibition , the total inhibition sensed by a new initium is of the form where and are the two inhibition fields . After a delay ( of the same magnitude as the plastochron ) after initiation , each primordium becomes a step-like source of inhibition of range ( of the same magnitude as ) , of the form ( having the values 0 if and if ) ; the secondary field is the sum of all the contributions of primordia . Initia are formed according to the primary field , but their age is shifted by a value if the secondary field value at the position of inhibition is non zero . Primordia are then ranked according to their corrected age .
|
How living organisms affected by natural , stochastic variability achieve regular developmental patterns is a challenging question in biology . A fitting field of investigation is provided by phyllotaxis , the regular arrangements of lateral organs such as leaves or flowers on the stem of vascular plants , as visible on a pinecone or a sunflower head . In spiral phyllotaxis , the most frequent amongst higher plants , these arrangements can be described using spirals , numbers from the Fibonacci sequence and the golden angle , which has led to an ideal , deterministic view of phyllotaxis . Nevertheless , organ initiation can be influenced by cellular and organismal noise . In order to investigate the effect of such noise , and how it might be regulated , we developed a stochastic dynamical model of the inhibitory interactions between organs . Our model predicts stereotypical alterations of phyllotactic patterns , recapitulating disparate observations . Comparing simulations and experiments , we identified the main source of noise affecting phyllotaxis in planta . We further propose a generic mechanism of noise regulation through a secondary signal and predict its parameters for an optimal efficiency . More generally , our work suggests that noise can have visible macroscopic effects on developmental phenotypes , and that different layers of control are required to modulate these effects .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Models"
] |
[
"physics",
"systems",
"biology",
"developmental",
"biology",
"plant",
"science",
"condensed-matter",
"physics",
"organism",
"development",
"mathematics",
"theoretical",
"biology",
"plant",
"biology",
"biology",
"computational",
"biology",
"biophysics",
"nonlinear",
"dynamics",
"genetics",
"and",
"genomics"
] |
2012
|
Noise and Robustness in Phyllotaxis
|
The Direct Agglutination Test ( DAT ) has a high diagnostic accuracy and remains , in some geographical areas , part of the diagnostic algorithm for Visceral Leishmaniasis ( VL ) . However , subjective interpretation of results introduces potential for inter-reader variation . We report an assessment of inter-laboratory agreement and propose a pictorial-based approach to standardize reading of the DAT . In preparation for a comparative evaluation of immunochromatographic diagnostics for VL , a proficiency panel of 15 well-characterized sera , DAT-antigen from a single batch and common protocol was sent to nine laboratories in Latin-America , East-Africa and Asia . Agreement ( i . e . , equal titre or within 1 titer ) with the reading by the reference laboratory was computed . Due to significant inter-laboratory disagreement on-site refresher training was provided to all technicians performing DAT . Photos of training plates were made , and end-titres agreed upon by experienced users of DAT within the Visceral-Leishmaniasis Laboratory-Network ( VL-LN ) . Pre-training , concordance in DAT results with reference laboratories was only 50% , although agreement on negative sera was high ( 94% ) . After refresher training concordance increased to 84%; agreement on negative controls increased to 98% . Variance in readings significantly decreased after training from 3 . 3 titres to an average of 1 . 0 titre ( two-sample Wilcoxon rank-sum ( Mann-Whitney ) test ( z = −3 , 624 and p = 0 . 0003 ) ) . The most probable explanation for disagreement was subjective endpoint reading . Using pictorials as training materials may be a useful tool to reduce disparity in results and promote more standardized reading of DAT , without compromising diagnostic sensitivity .
Up until the 1990's accurate visceral leishmaniasis ( VL ) diagnosis necessitated parasitological confirmation by microscopy or culture of the blood , bone-marrow , lymph nodes or spleen [1] . Microscopic detection of parasites in clinical material from the spleen is still considered the reference standard; however , splenic aspirates are associated with risk of serious bleeding and should only be carried out in settings with access to blood transfusion and surgical services . The invasiveness and potentially fatal complications associated with splenic aspiration has spurred the development of non-invasive serological tests such as direct agglutination test ( DAT ) [2] over 25 years ago and in the past decade , lateral flow immuno-chromatographic tests ( ICT ) , commonly referred to as rapid diagnostic tests ( RDTs ) . RDTs have now been adopted widely , in the Indian subcontinent [3] , but in other endemic regions , DAT is part of the diagnostic algorithm or is used for epidemiological surveys due to variable sensitivity of RDTs [2] , [4] . The DAT , in its present form , is a freeze dried suspension of trypsin-treated fixed and stained culture of L . donovani promastigotes [5]; liquid formulations of DAT are also manufactured locally . During infection with VL , circulating antibodies are produced against the surface antigens of the invading parasites . The DAT detects antibodies to L . donovani s . l . in the blood or serum of those infected by means of direct agglutination . In the absence of antibodies to Leishmania the DAT antigen accumulates at the bottom of the plate to form a dark blue spot . If antibodies to Leishmania are present then the antigen forms a pale blue film over the well and this constitutes a positive result . DAT requires moderate technical expertise , and laboratory equipment and reagents , including calibrated pipettes , micro-titre plates , multiple reagents and a toxic solution ( chemical 2-beta Mercapto-ethanol ( 2-ME ) ) [2] . Furthermore , despite very good accuracy , inter-observer discrepancy in routine DAT serology readings is common [6]–[8] . Prompted by shared experiences of six endemic countries using DAT to characterize performance panel samples , we report an assessment of DAT inter-reader variability . It was noted that the inter-laboratory agreement of DAT titres on a panel of 15 sera was low . Here , our objective was to standardize the reading of DAT by developing and implementing pictorial training aids .
Nine laboratories from three global endemic regions were involved in a WHO/TDR-sponsored evaluation of VL RDTs; namely Asia ( n = 4 ) , South America ( n = 2 ) and Eastern Africa ( n = 3 ) ( Table 1 ) . The Institute of Tropical Medicine , Antwerp , Belgium ( ITM ) assembled a proficiency panel including sera from 10 VL confirmed patients including a range of DAT titres , and 5 VL negative patients , one healthy endemic control and others who harbored potentially cross-reacting , infections , including Chagas disease , tuberculosis , malaria and leprosy . Prior to shipping , each sample within the panel was assigned a random numerical code that varied from centre to centre . All samples were left over from samples that had been taken as part of research projects conducted between 1978 and 2000 at the Institute for Tropical Medicine ( ITM ) Antwerp , Belgium . The samples were anonymised and kept stored for future use for scientific purposes . In the studies conducted since 2000 explicit consent was asked for storage and future use of left overs of the samples that were taken . In the older studies no explicit mention was made of future use of the stored left overs though a general informed consent was asked . However , it was not possible to trace back the study participants in the studies preceding 2000 and to ask them for informed consent for storage and use of left over samples The proficiency panel was tested blindly using the DAT assay ( KIT-Biomedical Research , Lot 0904 ) in each of the nine evaluation laboratories ( Table 1 ) and both reference laboratories ( KIT and ITM ) . Results were returned electronically to ITM using a standard recording form . All microtitre plates used in the procedure were provided by reference laboratories ( Greiner 651101 100 ) . The DAT was performed as described previously [2] . Due to significant discordance in end-titres between all laboratories , photographs of DAT plates with 10 VL positive serum samples and 5 VL negative serum samples were prepared by the reference laboratories ( following joint agreement on end titres ) and were used as pictorial training aids . Refresher DAT training was given by staff of KIT and Banaras Hindu University ( BHU ) to all participating laboratories ( Table 1 ) . Trainers assessed equipment and compliance with the DAT SOP , including preparation of reagents . Subsequently , the proficiency panel was repeated in the presence of the trainer . End titres were read independently by two separate technicians and the trainer . When readers did not agree on the end titre they came to a common conclusion after joint discussion . Combined results of the readers were sent to ITM and decoded by a study team member not involved in the refresher training; results of the trainer were not taken into consideration unless the results of the readers were significantly different from those of the reference laboratories and the test was repeated . Disagreement was defined as greater than one titre above or below those of the reference laboratories [6] . Variance in results before and after refresher training was compared with a two-sample Wilcoxon rank-sum ( Mann-Whitney ) test .
Despite having received the same panels , batch of DAT , microtitre plates and protocol , overall DAT results concordance ( agreement within one titre ) with the reference laboratories was only 50% . Agreement on negative controls was very good ( 94% ) . Using a cut off of 1∶1600 serum dilution , the pre-training sensitivity and specificity were 79% and 94% , respectively . Refresher training was initiated due to the large differences in DAT reading between participating laboratories . Here , photographs of DAT plates were used as training aids , where end-titres had been agreed upon by the reference laboratories . During refresher training the trainers did not identify any faulty or inappropriate equipment , nor did they witness any non-compliance with the DAT SOP . After refresher training the concordance ( agreement with one DAT titre ) increased to 84% with the reference laboratories . The agreement on negative controls increased to 98% . Average variance in results before refresher training was 3 . 3 titres; this improved to an average variance of 1 . 0 titre reading ( the accepted limit ) after refresher training . A non-parametric test was used to test for significant differences before and after training using a two-sample Wilcoxon rank-sum ( Mann-Whitney ) which showed significant difference ( z = −3 , 624 and p = 0 . 0003 ) . Post-training the sensitivity increased to 97% and the specificity to 100% ( cut off values 1∶1600 ) . Overall , the refresher training increased the operator performance of the DAT in this small proficiency panel ( Table 2 ) . After refresher training a cut-off point of 1∶1 , 600 ( serum dilution ) gave 97% sensitivity ( CI: 91 . 6–99 . 0% ) and 100% specificity . Further , pictorial guides ( Figures 1 , 2 , and 3 ) of DAT training plates reflecting consensus end titres by several experts in the VL-LN , with many years of experience in using DAT as a diagnostic tool , are now available . Further recommendations to be taken into account for completion of the DAT assay are highlighted in Table 3 and data per laboratory pre-training and post-training with pictorial aids can be seen in Supporting Information S1 .
The DAT assay has been used as a diagnostic tool for more than 25 years , it is robust , reliable has a high clinical accuracy and can be performed in laboratories with minimal equipment . However , the subjective manner in which the result ( end-titre ) of the test is read means that inter-reader variation in titre reading can be an issue . Preparations for a multicenter evaluation of RDTs unexpectedly uncovered a significant discordance in DAT results among reference and evaluation centres; this presented an opportunity to address discordance and create an international , consensus-based protocol and training materials to strengthen standardized reading of the DAT for VL diagnosis without compromising diagnostic accuracy . The reasons for all of the discrepancies between the different laboratories is not fully understood however , it was noted that technicians were generally competent in the DAT procedure , particularly those who used it as part of their routine diagnostic algorithm . It was not possible to test the saline that the laboratories previously used in testing , but it is possible that the origin and quality of the saline solutions used as a diluent for the DAT antigen did affect performance , generating false positive precipitation in negative sera wells . The most consistent problem identified in the laboratories can be attributed to the subjective manner in which the end titre of the DAT test is typically read . Some readers record the end-titre when 50% of agglutination of the well has occurred ( as occurs with other agglutination tests ) , whilst other readers record the end-titre where the whole well has agglutinated and there is no difference between a negative control well ( antigen plus saline ) and the sample well . Even though 1 titre difference in reading is considered acceptable [6] , the discrepancy and variance in results reported here was far greater . Training plates developed by the reference laboratories proved to be extremely helpful in illustrating the end titre . Positive sample wells were defined by any reaction in the test well in comparison to the negative control well; this ensured that the high sensitivity of the DAT was not compromised . Figure 2 shows the end-titre as agreed by the VL-LN; a follow up plate can be used to test users before revealing the results as seen in figure 3 . High quality photos in figures 2 and 3 are also available by request ( contact corresponding author ) for use as reference training material for future DAT users . Since slight variations in readings between different DAT antigen batches may occur it is advised that the same batch of DAT should be used within one project or epidemic to decrease variability in results . If this is not possible then it is recommended to keep reference sera in order to assess this lot-to-lot variation , this should not be more than one titre difference . In addition , it is important that all users of the DAT specify the type of dilution used , i . e . serum dilution ( starting 1∶100 ) or antigen plus serum dilution ( starting 1∶200 ) . It is likely that cut-off values are different between endemic areas and even during epidemic cycles . Local guidance as to appropriate cut-off values is essential . The problems uncovered during a multicenter DAT proficiency testing scheme are potentially relevant to other DAT users . To reduce inter-reader variability and increase accuracy , photos of training plates were made , and end-titres were agreed upon firstly by the reference laboratories and subsequently by experienced users of DAT within the VL-LN . These photos can be used to promote a standardized approach to interpreting DAT without compromising sensitivity . Protocols and photos can be requested for training and quality control purposes by two of the major manufacturers of the assay , KIT and ITM . High sensitivity and specificity can be achieved with this reliable and robust diagnostic tool , and we hope that provision of good training materials can increase the usefulness of DAT .
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Until the 1990's accurate Visceral Leishmaniasis ( VL ) diagnosis necessitated parasitological confirmation by microscopy or culture of the blood , bone-marrow , lymph nodes or spleen . These techniques are invasive and splenic aspirates are associated with a risk of serious bleeding . This has led to the development of non-invasive serological tests such as the direct agglutination test ( DAT ) . During infection with VL , circulating antibodies are produced against the surface antigens of the invading parasites . The DAT detects antibodies to L . donovani s . l . in the blood or serum of those infected by means of direct agglutination . In the absence of antibodies to Leishmania the DAT antigen accumulates at the bottom of the plate to form a dark blue spot . If antibodies to Leishmania are present then the antigen forms a pale blue film over the well constituting a positive result . Here , we report on shared experiences of six endemic countries using DAT to characterize performance panel samples . There was considerable inter-reader variability and in order to standardize the reading of DAT we developed and implemented pictorial training aids . After refresher training , agreement between readers increased; the pictorial aids and recommendations for using DAT are available in this article .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"test",
"evaluation",
"diagnostic",
"medicine",
"leishmaniasis",
"protozoan",
"infections",
"parasitic",
"diseases"
] |
2012
|
Leishmaniasis Direct Agglutination Test: Using Pictorials as Training Materials to Reduce Inter-Reader Variability and Improve Accuracy
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Acquired immunity in vertebrates maintains polymorphisms in endemic pathogens , leading to identifiable signatures of balancing selection . To comprehensively survey for genes under such selection in the human malaria parasite Plasmodium falciparum , we generated paired-end short-read sequences of parasites in clinical isolates from an endemic Gambian population , which were mapped to the 3D7 strain reference genome to yield high-quality genome-wide coding sequence data for 65 isolates . A minority of genes did not map reliably , including the hypervariable var , rifin , and stevor families , but 5 , 056 genes ( 90 . 9% of all in the genome ) had >70% sequence coverage with minimum read depth of 5 for at least 50 isolates , of which 2 , 853 genes contained 3 or more single nucleotide polymorphisms ( SNPs ) for analysis of polymorphic site frequency spectra . Against an overall background of negatively skewed frequencies , as expected from historical population expansion combined with purifying selection , the outlying minority of genes with signatures indicating exceptionally intermediate frequencies were identified . Comparing genes with different stage-specificity , such signatures were most common in those with peak expression at the merozoite stage that invades erythrocytes . Members of clag , PfMC-2TM , surfin , and msp3-like gene families were highly represented , the strongest signature being in the msp3-like gene PF10_0355 . Analysis of msp3-like transcripts in 45 clinical and 11 laboratory adapted isolates grown to merozoite-containing schizont stages revealed surprisingly low expression of PF10_0355 . In diverse clonal parasite lines the protein product was expressed in a minority of mature schizonts ( <1% in most lines and ∼10% in clone HB3 ) , and eight sub-clones of HB3 cultured separately had an intermediate spectrum of positive frequencies ( 0 . 9 to 7 . 5% ) , indicating phase variable expression of this polymorphic antigen . This and other identified targets of balancing selection are now prioritized for functional study .
Evolutionary and population genetic analyses of pathogens should help discover mechanisms of pathogenesis , immune evasion and drug resistance . Application of these approaches to malaria parasites is a high priority , as there is an ongoing need to identify targets of immunity as potential vaccine candidates , and to understand and monitor the continuous evolution and emergence of drug resistance . Advances in genome sequencing methods now allow more comprehensive analyses of polymorphism within populations , and increases the efficiency of detecting signatures of natural selection from patterns of genetic variation [1] , [2] , [3] , [4] . This also encourages the scaled up use of allele frequency-based methods for detection of recent and ongoing selection [5] , [6] . Plasmodium falciparum causes more human disease than any other eukaryotic pathogen [7] , and contains ∼5560 annotated genes in a compact genome of ∼23 megabase pairs ( Mb ) with a high recombination rate in each of 14 chromosomes [8] , [9] . Previous analyses of microsatellites and single nucleotide polymorphisms ( SNP ) have identified selective sweeps around several previously-identified drug resistance genes , encouraging genome wide analyses to prospect for other chromosomal loci containing genes under recent positive directional selection [10] , [11] , [12] . Separately , studies of individual genes encoding surface-exposed protein targets of acquired immunity have shown signatures of balancing selection maintaining different alleles within populations ( reviewed in [13] ) , and these results replicate well in independent studies of different endemic populations [13] , [14] , [15] , [16] , [17] . This indicates that new potential candidates for vaccine development based on multi-allelic antigen formulations might be identified with a systematic genome-wide scan for such signatures in an endemic population . The very low levels of linkage disequilibrium due to frequent recombination in highly endemic P . falciparum populations [10] , [18] , [19] means that contiguous sequence data are needed to allow an effective scan for signatures of balancing selection . The limited numbers and disparate sampling of P . falciparum genome sequences until recently have not enabled such frequency-based analyses to be effectively applied [11] , [20] , [21] , [22] . More thorough analysis of sequence diversity within local populations is now possible by paired-end short-read sequencing of parasites in clinical isolates [23] , [24] , which facilitates new approaches . Here , we present a genome-wide survey of polymorphism in coding sequences of P . falciparum in an endemic population sample of 65 Gambian clinical isolates , the largest sample of parasite genomes from a single location reported to date . We identified genes having polymorphic site frequency spectra consistent with effects of balancing selection , forming a prime catalogue of candidates for studies of immune mechanisms and potential vaccine development . Genes expressed at the merozoite stage were more likely than others to show such patterns , as were members of several small multigene families encoding surface and exported proteins that are yet to be studied intensively . The product of the gene with the strongest statistical signature was studied , revealing an unexpected pattern of variation in expression among different isolates and within individual parasite clones , suggesting that selection for phase variation may operate alongside selection for amino acid polymorphisms .
We generated genome-wide short read sequences for each of 65 Gambian P . falciparum clinical isolates and aligned these to the 5560 gene coding sequences in the genome sequence of P . falciparum clone 3D7 ( version 2 . 1 ) , yielding sequence contigs for 5475 ( 98 . 5% ) of the genes . The overall coding sequence coverage of each isolate was >80% ( mean of 95 . 6% ) at a read depth of 10 or more ( Table S1 ) . For each isolate , the consensus read sequence was taken as the majority parasite allele sequence for each gene . We excluded 419 genes from analysis that belonged to var , rifin or stevor hypervariable families , or that did not have more than 70% coverage at a read depth of 5 or more for at least 50 of the isolates , and thus proceeded to analyse sequences of 5056 genes ( 90 . 9% of all annotated in the genome ) . We identified 2203 genes with minimal or no polymorphism ( 769 with 0 SNPs , 794 with 1 SNP and 640 with 2 SNPs ) , and 2853 genes with at least 3 SNPs ( mean coverage of the coding sequences of these genes was 98 . 5% ) . Genes with at least 3 SNPs were considered informative for comparisons of polymorphic nucleotide site frequency spectra in analyses that aimed to be as comprehensive as possible , although data for individual genes are inevitably statistically stronger for those with higher numbers of SNPs ( 1009 of these had ≥10 SNPs , and 51 had ≥50 SNPs; results for all individual genes are given in Table S2 ) ( Figure 1 ) . Across all 2853 genes with 3 or more SNPs , values of Tajima's D and Fu & Li's indices were mostly negative ( mean Tajima's D = −1 . 00 , Fu & Li's D* = −1 . 14 , F* = −1 . 24 ) , indicating an excess of low frequency and singleton polymorphisms compared with that expected under neutrality for a population at mutation-drift equilibrium ( Figure 2A ) . This is consistent with historical population expansion , as supported also by predominantly negative values of Fu's Fs index ( mean = −7 . 1 ) . As expected , values of Tajima D correlated with those of Fu & Li's F* ( r = 0 . 68 ) ( Figure 2B ) , and D* ( r = 0 . 50 ) , while Fu & Li's F* and D* indices were very highly correlated ( r = 0 . 96 ) . Genes with high Tajima's D values had a wide distribution across all chromosomes ( Figure 3A ) . Overall , 337 ( 11 . 8% ) of the 2853 genes with at least 3 SNPs had Tajima's D values above zero , of which 241 also had positive values for Fu & Li's F and D , and these loci were widely distributed throughout the genome ( Figure 3B ) . Table 1 shows indices for the genes with the top 25 values of Tajima's D , among those having at least 10 SNPs . The full list of results for all of the 2853 genes with 3 or more SNPs is given in Table S2 . Several of the genes at the top of the list encode antigens that are known targets of immunity , the most studied of which is the apical membrane antigen 1 ( AMA1 ) , against which many naturally-acquired and experimental vaccine-induced immune responses are allele-specific [25] , [26] , [27] , [28] . The ama1 gene previously showed very high Tajima's D values in independent studies from The Gambia [17] and other endemic populations [25] , [29] , [30] , [31] , and has long been recognised to have exceptional nucleotide diversity at nonsynonymous positions compared with synonymous positions [32] , [33] as reflected also in the data here . Generally , for genes that had been studied previously in endemic African populations by capillary re-sequencing of particular loci ( data reported in [14] or from studies reviewed in [13] ) , there was strong correlation between the Tajima's D values obtained here and those previously reported . Particularly , 11 ( 92% ) out of 12 genes that had positive indices in previous studies also had positive values here: PF10_0355 ( value of 3 . 31 ) , ama1 ( 1 . 95 ) , PF10_0348 ( 1 . 81 ) , Pf38/6cys ( 1 . 57 ) , csp ( 1 . 20 ) , eba-175 ( 1 . 29 ) , SURFIN4 . 2 ( 1 . 04 ) , msp7 ( 1 . 01 ) , trap ( 0 . 77 ) , msp3 ( 0 . 09 ) , sera5 ( 0 . 07 ) . Thirty seven ( 57% ) isolates had mixed genotype infections and 28 ( 43% ) were apparently single-clone infections , as determined by genotyping with highly polymorphic loci msp1 and msp2 , similar proportions to those seen in previous studies of clinical isolates at this site [34] , [35] . These two separate strata of isolates showed very similar site frequency spectra , with a high correlation of Tajima's D values across all 2853 genes analyzed ( Spearman's ρ = 0 . 62 , P<0 . 0001 ) . The 30 genes having the highest values overall were similarly placed in the top tail of values in both strata , indicating a high level of replication of outlier results ( Figure S1 ) . Transcriptome data from microarray analyses on synchronized parasite asexual blood stages and gametocytes were available ( www . plasmodb . org [36] , [37] ) for 2710 ( 95 . 0% ) of the 2853 genes with 3 or more SNPs , enabling exploration for associations between stage-specificity of expression and Tajima's D indices of the polymorphic site frequency spectrum ( Figure 4 ) . Genes with estimated peak expression in merozoites had higher indices than genes with peak expression at other life cycle stages , significantly for the distribution of values for the merozoite stage compared with four of the other stages separately ( Mann-Whitney tests each p<0 . 01 ) , whereas none of the other stages differed significantly between each other . Tajima's D values were above zero for 17 . 8% ( 72 of 404 ) of merozoite stage peak expression genes , compared with 10 . 5% ( 241 of 2306 ) of those with all other stage peaks ( P = 0 . 0001 after Bonferroni correction for testing each separate stage against the others combined ) . This indicates that balancing selection is particularly strongly active on this extracellular invasive stage of the parasite in the blood . Members of small gene families , and others encoding proteins broadly categorized by location of expression were investigated ( Figure 5 ) . Most showed a broad range of values of Tajima's D , predominantly negative with a minority positive . The four families with the highest values overall were clag , Pfmc-2TM , surfin and msp3-like genes ( Figure 5 ) . Highly positive values were also seen for individual genes as diverse as ADP/ATP carrier protein ( PF10_0051 ) and acylCoA synthase ( PFD0980w ) , as well as loci encoding hypothetical proteins of unknown function ( Table 1 and Table S2 ) . The indices of selection detected were highly locus-specific , as expected where effective recombination rate is very high and linkage disequilibrium ( LD ) declines rapidly with nucleotide map distance , as expected for most P . falciparum populations in Africa [10] , [18] , [19] . This study was not designed to investigate issues relating to LD in depth , as there is a possibility that some false haplotypes would be derived from consensus sequence contigs generated from mixed genotype infections . Nevertheless , examination of data from genes with 10 or more SNPs was informative even in crude analysis , with very strong LD only generally seen among sites separated by a few hundred nucleotides or less ( Figure 6A ) . A minority of the genes ( examples shown in the bottom panels of Figure 6A ) showed patterns indicating blocks of sequence that may be in virtually absolute LD , illustrated most clearly for PF10_0355 in which such LD extends for almost 1 kb ( as shown previously for this gene with alleles grouping into two major dimorphic forms ) [14] . Given that strong LD did not usually persist throughout genes , sliding window analysis was able to reveal heterogeneity of signatures in different parts of a gene ( examples shown in Figure 6B ) . The strongest signature consistent with balancing selection in the PHISTa gene PFL2555w is towards the 5′-end ( top panel , Figure 6B ) , whereas for the clag-like gene MAL7P1 . 229 the strongest evidence is near the 3′-end ( middle panel , Figure 6B ) , and for the msp3-like PF10_0355 it is in the middle of the gene ( bottom panel , 6B ) . We investigated PF10_0355 further , as the top hit from the genome-wide analysis . This msp3-like gene was originally predicted to encode a protein designated MSPDBL2 ( the second merozoite surface protein to have a Duffy-Binding Like domain ) [38] , also given the designation MSP3 . 8 [39] , and over-expression of the gene by episomal plasmid transfection has conferred reduced sensitivity to culture inhibition by halofantrine [40] . Although most msp3-like genes encode proteins associated with merozoites ( within schizonts and after extracellular release ) , microarray and RNA sequence analyses of a few cultured P . falciparum lines have previously shown little transcription of PF10_0355 at any developmental stage [37] , [41] , [42] , [43] . To survey transcript profiles of the six msp3-like genes ( Figure 7A ) , 45 Gambian clinical P . falciparum isolates cultured ex vivo to schizont stage were assayed by quantitative RT-PCR ( Figure 7B ) . The msp3 gene ( PF10_0345 ) was expressed in all isolates , while msp6 ( PF10_0346 ) and dblmsp ( PF10_0348 ) were expressed in most isolates at varying levels , and the other three genes ( including PF10_0355 ) showed relatively low transcript levels in almost all isolates . Schizont stage cultures of 11 long term culture-adapted parasite lines of diverse origin were assayed ( Figure 7C ) , showing a similar range of expression for each gene as observed among clinical isolates . It is notable that the h103 gene ( PF10_0352 ) , which has also been named as msp11 , was highly transcribed in one clinical isolate only . Cluster analysis of the transcript profiles showed the laboratory and clinical isolates interspersed with each other ( Figure 7D ) , and levels of PF10_0355 transcript were very low in all except clinical isolate 97 and laboratory clone HB3 . To investigate protein expression in schizonts , 12 genetically distinct parasite lines that were each apparently clonal were studied by immunofluorescence microscopy with antibodies raised against recombinant proteins based on conserved parts of the product of PF10_0355 ( Figure S2 ) . Remarkably , antibodies to the PF10_0355 product reacted against only a small minority of mature schizonts ( Figure 8A ) . Immature parasite stages including early schizonts with <8 nuclei were all negative , so each parasite line was scored by counting several hundred mature schizonts ( with at least 8 DAPI-stained nuclei ) , showing that ∼1% or less were positive in each line , with the exception of HB3 in which 12 . 7% ( 68/535; 95% CI , 10 . 0–15 . 8% ) were positive ( Figure 8B , and Table S3 ) . To test for stability of proportions positive , HB3 was grown again from cryopreserved stock , and mature schizonts tested after approximately 2 weeks of independent culturing . These showed a MSPDBL2-positive proportion of 9 . 1% ( 52/574; 95% CI , 6 . 8%–11 . 1% ) , marginally lower than seen in the previous culture ( P = 0 . 051 ) . To test for changes over a longer period of independent culturing , a panel of 8 sub-clones of HB3 that had been cultured separately for an average of 4 months ( ∼60 replicative cycles ) was then assayed . Among these sub-clones , the proportions of mature schizonts positive showed a spectrum ranging from 0 . 9% ( 5/533; 95% CI , 0 . 3–2 . 2% ) to 7 . 5% ( 23/306; 95% CI , 4 . 8–11 . 1% ) ( Figure 8C ) . Multiple-labelling immunofluorescence assays were then performed on the panel of 12 different parasite lines using antibodies against conserved sequences of MSP3 ( product of gene PF10_0345 ) , MSP6 ( PF10_0346 ) , and DBLMSP ( PF10_0348 ) . This indicated no mutual exclusion between MSPDBL2 and the other more commonly-expressed MSP3-like proteins , with MSPDBL2-positive schizonts being positive for MSP3 , MSP6 and DBLMSP in each of the lines ( Figure S3 ) . A particular histone methylation mark H3K9me3 ( tri-methylation of lysine at residue 9 of H3 ) is a feature of sub-telomeric antigenic variant genes in their repressed state , and PF10_0355 is one of only very few genes elsewhere in the genome to have such a heterochromatic signature , apparent also on flanking sequences but not extending to the other msp3-like paralogues [44] . In contrast to the anti-MSPDBL2 antibodies , anti-MSP3 , anti-MSP6 and anti-DBLMSP antibodies reacted against most mature schizonts in every isolate studied here , with an exception that DBLMSP expression was absent in the RO33 line ( as expected from the existence of a stop codon in the PF10_0348 gene in this line only ) [17] .
This population genomic analysis of P . falciparum in a single endemic location has indentified many new genes with polymorphic site frequency spectra consistent with balancing selection , as well as confirming results for previously studied candidate antigen genes . These genes appeared as exceptions against a genomic background in which most genes had negative values of Tajima's D , as expected from historical population expansion [45] and also seen with the mitochondrial genome [46] . As all parasite isolates sequenced were collected in one transmission season from a single area in the Gambia we minimized population structuring in the sample , and were able to identify genes in the outlying skewed tail of strongly positive values of Tajima's D and other supporting indices . Not all individual genes with high values of Tajima's D will be under balancing selection , as there is likely to be a wide range of values under neutrality due to genetic drift variance among loci , as well as sampling variance affecting the values for genes with few SNPs . The analysis should therefore be regarded as a screen to identify potential candidates under balancing selection , in which all hits require validation . Earlier data on polymorphism among small numbers of P . falciparum lines and a partial sequence of the chimpanzee parasite P . reichenowi allowed preliminary survey of diversity-versus-divergence indices including McDonald-Kreitman and Hudson-Kreitman-Aguade ratios [20] , [22] , and comprehensive analyses to derive such indices from our population-based data will be useful after a more complete draft of the P . reichenowi genome sequence is published . Analyses here considered the consensus sequence for each gene in each isolate , as this could be clearly determined using available methods . Results were similar for subsets of isolates that respectively contained apparent single or multiple parasite clones , on the basis of a routine genotypic screen . It is not currently possible to resolve parasite genomic haplotypes within mixed genotype infections [23] , unless they are cloned and cultured long term in vitro [47] during which artificial selection may occur . Although haplotype resolution was not necessary for the current analyses , development of future methods to achieve this could allow investigation of processes of within-isolate selection among parasites , which have a spectrum of relatedness due to mixed inbreeding and outbreeding [47] , [48] . As expected from the high recombination rate in this parasite , patterns of polymorphism consistent with balancing selection were tightly localized within individual genes , whereas in organisms with lower recombination rate balancing selection often affects polymorphism at flanking loci [49] . Tajima's D values were highest for genes with estimated peak expression in merozoites , indicating that exceptionally strong balancing selection operates on this extracellular stage , consistent with likely effects of acquired immunity or interaction with diverse erythrocyte receptors for invasion . There were high values for previously studied antigen genes , such as the apical membrane antigen 1 gene ( ama1 ) which encodes a prime vaccine candidate [27] previously shown to be under balancing selection in several independent studies of different populations [17] , [25] , [29] , [30] , [31] . The gene with the highest Tajima's D value overall was the msp3-like PF10_0355 that also had the highest value among the candidate genes studied previously [14] . Recent data have indicated differences in transcript levels of this gene among some parasite lines [40] , and existence of a protein product on the merozoite surface [39] . Our results significantly extend these findings to show that this merozoite protein is expressed in only a minority of mature schizonts within any parasite clone , but the proportion of positive schizonts varies significantly among clones , and also varies over time for a single clone and among sub-clones cultured separately . There has been considerable characterization of antigenic variation caused by large sub-telomeric gene families expressing proteins on the infected erythrocyte surface [50] . The merozoite protein genes for which variant expression has previously been described are also sub-telomeric [51] , [52] , [53] , [54] , whereas the PF10_0355 gene is exceptional in showing the H3K9me3 heterochromatic marking associated with silencing of sub-telomeric genes , but in a more centromeric position [44] . Over-expression of the gene on an episomal plasmid ( free from heterochromatin-associated repression ) conferred resilience of in vitro growth in the presence of halofantrine through an unknown mechanism [40] . If this protein directly affects parasite growth in differing environments , this could potentially contribute to a system of balanced polymorphism and repression of expression . It would also suggest that identifying its importance as a target of naturally-acquired immunity might be more demanding than has been the case for other merozoite antigens [55] . A clag-like gene ( MAL7P1 . 229 ) had the second highest Tajima's D value in the genome , and the family of clag genes that encode merozoite rhoptry proteins [56] also generally ranked highest , although values for clag 3 . 1 and the adjacent gene clag 3 . 2 may be affected to some extent by gene conversion [57] . Particular clag genes have alternative expression patterns between parasites within a single clone [51] , suggesting that structural polymorphism under balancing selection may also be associated with variant expression . Members of the Pf-mc-2TM family encoding proteins associated with Maurer's clefts also had very high values ( most exceptionally for PF11_0014 and PFA0065w ) , and members of this family have been shown to have clonally variant expression [58] . The families of surfin and eba genes also ranked highly , and each contain members that have previously shown patterns of polymorphism suggesting balancing selection [14] , [59] , [60] and exhibit variable expression among parasite isolates [35] , [40] , [61] , [62] . Polymorphic site frequency spectra consistent with balancing selection were also seen in some exported protein genes , including members of the HYP and PHIST families that have transmembrane domains or signal peptides [63] . Individual members of all three classes of the PHIST gene family had high values of Tajima's D , of which the highest were for particular PHISTa ( PFL2555w ) and PHISTb genes ( PFD1170c and PFB0080c ) . Allelic polymorphisms in HYP and PHIST genes are likely to contribute to observed phenotypic diversity in clinical isolates , alongside effects of variant expression [62] , [64] , [65] . These results suggest that many targets of balancing selection may also undergo phase variation . We consider that immune selection is likely to be the primary cause of such selection on asexual haploid blood stage parasites [60] , [66] , [67] , [68] , but other mechanisms may operate on some genes , including interactions with genetically polymorphic host cell receptors that are themselves under balancing selection [69] , [70] , or hypothetical systems of non-self recognition among genetically heterologous asexual parasites within infections [71] . At other stages of the life-cycle , it is possible that balancing selection is driven by non-self recognition among parasite gametes that are transmitted to mosquitoes , or heterozygote advantage operating at the very brief diploid stage in the mosquito midgut . Further work is needed to determine causes of selection on most of the affected genes highlighted , and it is preferable to first perform population genetic analyses in other endemic populations to test initial inference of selection for individual genes emerging from this study . Similar approaches should also be effective in identifying candidate targets of balancing selection in the genomes of other eukaryotic pathogens , including other malaria parasite species .
Ethical approval for the study was obtained from the Gambia Government and MRC Joint Ethics Committee , and the Ethics Committee of the London School of Hygiene and Tropical Medicine . Written informed consent was obtained from a parent or guardian of each child contributing a blood sample . In addition , assent was obtained from children over 10 years of age . Following review ( LSHTM Approval PF-486 ) , antibodies were obtained commercially under commercial sub-contract , and all animal work protocols were approved and licensed by the UK Home Office as governed by law under the Animals ( Scientific Procedures ) Act 1986 ( Project licenses 70/7051 and 80/2061 ) . The animals were handled in strict accordance with the “Code of Practice Part 1 for the housing and care of animals ( 21/03/05 ) ” available at http://www . homeoffice . gov . uk/science-research/animal-research/ , and the numbers used were the minimum consistent with obtaining scientifically valid data . Patients with P . falciparum malaria were recruited in the malaria season between August and December 2008 from four health facilities located within a radius of 20 km in the coastal area of The Gambia ( Royal Victoria Teaching Hospital in Banjul , the MRC clinic in Fajara , Jammeh Foundation for Peace Hospital in Serekunda , and Brikama Health Centre ) . All recruited malaria cases had a temperature of >37 . 5°C on presentation or history of fever in the previous 48 hours , and a minimum of 5000 P . falciparum parasites µl−1 estimated by thick film examination . A thin blood smear confirmed each infection as P . falciparum only . After informed consent , and under approval by the Joint Gambian Government and MRC Ethics Committee , up to 5 ml of venous blood was collected from each subject in heparinised tubes immediately prior to treatment . Plasma was removed from blood samples after centrifugation , and erythrocytes were separated from leukocytes by Nycoprep density gradient centrifugation , washed and re-suspended at 50% haematocrit in incomplete RPMI medium . Samples were further processed to deplete human leukocytes , either by filtration of cell suspension through Plasmodipur filters , or by sedimentation on plasmagel followed by magnetic capture using anti-HLA antibody-coated beads . Following separation , leukocyte-depleted erythrocytes were washed in incomplete RPMI 1640 and stored at −80°C . DNA was assayed for presence of single or multiple clones of P . falciparum by genotyping the highly polymorphic repeat loci in msp1 and msp2 [72] . For analysis of gene expression , forty five clinical isolates of P . falciparum cultured ex vivo to the first generation schizont stage were analyzed from samples collected over three previous malaria seasons ( 2005–2007 ) in The Gambia [35] , [73] . Fourteen laboratory-adapted P . falciparum isolates of diverse origin were cultured separately in London: 3D7 , cloned from an airport malaria case in The Netherlands; D6 , RO33 and Palo Alto , from Africa; FCR3 and Wellcome , nominally from Africa but each suspected to have been contaminated and clonally replaced by different parasites more than 20 years ago during culture; K1 , T9/96 , T9/102 , Dd2 , FCC2 and D10 , from Southeast Asia; HB3 from Honduras; 7G8 from Brazil . Parasite DNA was extracted from 400 µl of packed erythrocytes from each sample using QIAamp DNA blood midi kit ( Qiagen , United Kingdom ) . The ratio of human to parasite DNA was then determined by quantitative PCR assays on parasite apical membrane antigen 1 gene ( ama1 , following published protocol [74] ) and human RnaseP gene ( Applied Biosystems protocol ) . Sixty eight processed samples containing over 50% parasite DNA ( <50% human DNA ) were selected as potentially suitable for Illumina paired-end shotgun sequencing , and standard Illumina sequencing libraries were prepared following the manufacturer's recommended protocol . Short paired-end reads ( 37 or 76 base pairs ) were generated and mapped onto the P . falciparum 3D7 reference genome sequence ( version 2 . 1 , June 2010 ) using the Burrows-Wheeler Aligner ( BWA ) program , with an algorithm that allowed for polymorphic positions ( >98 . 5% matched excluding indels [75] ) . The sequence read data have been made available at the European Nucleotide Archive http://www . ebi . ac . uk/ena/data/view/ERP000190 , and individual sample ID numbers are given in Table S1 . To generate consensus contiguous sequences for each gene per isolate , majority reads were assembled across each coding sequence [76] , using SAMtools to generate a read pileup . Sixty-five of the isolates ( Table S1 ) had coverage of above 80% of all coding sequences with mean read depth of at least 10; three other isolates had lower read coverage and were not analysed further . The consensus majority read sequence for each gene in each isolate was analyzed as a sampled allele sequence , which would correspond in almost all cases to the actual allele of the single or most abundant clone in the infection . We excluded genes from three hypervariable families ( var , rifin and stevor ) and any other individual genes that did not have more than 70% coverage for at least 50 isolates at a read depth of 5 or more . An R script automating analysis with Tandem Repeat Masker and Muscle 3 . 6 software was used to mask repeats and re-align non-repetitive sequences for each of the genes analysed . Poorly aligned contig sequences were checked and removed using BioEdit ( http://www . mbio . ncsu . edu/bioedit/bioedit . html ) . Cultures of parasites predominantly at schizont stage were mixed with four volumes of TRIzol Reagent ( Ambion ) , and aliquots stored at −80°C for subsequent RNA extraction using RNeasy Micro ( Qiagen , UK ) . RNA concentration and purity were determined using a NanoDrop ND-1000 , and mRNA was reverse-transcribed with Oligo-dT using TaqMan reverse transcription reagents ( Applied Biosystems , UK ) . For real-time PCR-based transcript quantification , cDNA was assayed in a fluorogenic 5′ nuclease assay ( TaqMan chemistry ) on a Rotor-Gene 3000 ( Corbett Life Sciences ) , with gene-specific TaqMan primers and probe sets based on non-polymorphic unique sequences within each of the six msp3-like genes ( Applied Biosystems ) ( Table S4 ) , and primers and probes for ama1 based on those previously described [74] . All probes were labeled with 6-carboxy-fluorescein ( FAM ) on the 5′-end and a non-fluorescent quencher ( MGB-NFQ , Applied Biosystems ) on the 3′-end and used in single reporter assays . Reactions were carried out in 25 µl volumes using 900 nM of each primer and 250 nM of probe , with one cycle at 50°C for 2 min and 95°C for 10 min , followed by 40 cycles of 95°C for 15 s and 60°C for 1 min . Each run included controls and a standard curve based on 10-fold dilutions of 3D7 genomic DNA . Two constructs were designed to express parts of the N- and C-terminal regions of the MSPDBL2 ( MSP3 . 8 ) product of PF10_0355 , as glutathione S-transferase ( GST ) -tagged proteins in E . coli ( Figure 3A ) . Sequences corresponding to nucleotide positions 70–273 and 1615–1770 of the PF10_0355 gene , flanking the central region encoding the DBL domain ( Figure S2 ) , were PCR amplified from 3D7 genomic DNA , cloned into the pGEM Easy TA vector ( Promega ) , and sequence verified . Correct sequence inserts were subcloned into the pGEX-2T expression vector ( GE Healthcare ) , sequenced again to ensure fidelity and transformed into BL21 ( DE3 ) E . coli cells for expression . Expression and affinity purification was performed as described previously for other GST-fusion proteins [77] . Products were visualised by SDS-PAGE and assayed for antigenic reactivity to IgG in sera from a panel of Gambian adults ( Figure S2 ) . Antibody reactivities of murine antisera raised to the MSPDBL2 recombinant proteins , a rabbit antiserum to a conserved part of MSP3 ( codons 234–354 of PF10_0345 ) [60] , and a rat antiserum to a conserved part of MSP6 ( codons 198–255 of PF10_0346 ) , were tested against different P . falciparum lines using immunofluorescence assays ( IFA ) . Parasite cultures with a large proportion of schizonts were washed in PBS/1% BSA , resuspended to 2 . 5% hematocrit and 15 µl aliquots spotted onto multiwell slides ( Hendley , Essex , UK ) which were then air-dried and stored at −40°C with desiccant until required . Following a recommended fixation protocol [78] , slides were bathed in 4% paraformaldehyde in PBS for 30 min , followed by 10 min in 0 . 1% Triton X-100 in PBS and then overnight at 4°C in PBS/3% BSA . After air-drying , wells were incubated with defined dilutions of each test serum ( including initial serial doubling dilutions from 1/200 to 1/409600 ) in PBS/3% BSA and incubated for 30 min at room temperature . Slides were rinsed 3 times in PBS , excess wash buffer removed and wells incubated for 30 min with a 1/500 dilution of biotinylated anti-mouse IgG ( Vector Laboratories , USA ) in PBS/3% BSA , washed 3 times in PBS , and incubated for 30 min with 1/500 dilution of fluorescein strepavidin ( Vector Laboratories ) . Mounting fluid with DAPI ( Vectashield , Vector Laboratories ) was added and each slide sealed with a cover slip . For triple-labelled IFA , selected individual murine antibodies raised to the PF10_0348 and PF10_0355 N-terminal conserved antigens were used , followed by incubation with rhodamine-labelled anti-mouse Ig ( at 1/250 dilution ) . To the same wells rabbit anti-MSP3 conserved antigen raised to the C-terminal region of MSP3 was added followed by 1/250 dilution of FP642-labelled anti-rabbit Ig ( FluoProbes , Interchim , France ) . Finally , rat anti-MSP6 was added followed by 1/500 dilution of FITC labelled anti-rat Ig ( Jackson laboratories , USA ) . All antigen-specific sera were used at 1/400 . For doubled-labelled IFA , the protocol was as outlined except that rabbit antiserum raised to the PF10_0348 N-terminal conserved region was used alongside mouse antiserum to the PF10_0355 N-terminal conserved region . Antibodies to recombinant proteins were obtained commercially by immunization of a small number of laboratory animals as reagents to characterize native parasite proteins , with all protocols and practices approved and licensed by the UK Home Office as governed under the Animals ( Scientific Procedures ) Act 1986 . Numbers of animals immunized were the minimum to reasonably ensure that at least one animal would produce adequate titer antibodies to each protein . Five CD1 outbred mice were immunized with 25 µg of each of the N- and C-terminal MSPDBL2 recombinant antigens emulsified in Freund's complete adjuvant delivered subcutaneously , and boosting immunizations were performed twice at 28 day intervals in Freund's incomplete adjuvant . Sera were collected before immunization and final serum collection made 7 days after the last immunization ( Pharmidex , UK ) . Purified recombinant MSP3 conserved antigen was used to immunize three New Zealand white rabbits with each receiving 200 µg doses of purified protein emulsified in Freund's adjuvant . Following a primary intramuscular immunization in Freund's complete adjuvant , booster immunizations were given in Freund's incomplete on days 14 , 28 , 42 , 56 and 70 . Sera were collected before immunization and final serum collection made 7 days after the last immunization ( Pettingill Technology Ltd , UK ) . Two Sprague-Dawley rats were immunized with 25 µg doses of purified recombinant MSP6 protein , with Freund's complete adjuvant for primary immunization and Freund's incomplete adjuvant for three boosting doses at intervals of 7 days , with final serum collection made 7 days after the last immunization ( Harlan , UK ) . Summary statistics and neutrality tests based on the polymorphic nucleotide site frequency spectra were calculated using Variscan 2 . 0 [79] . Tajima's D test takes into account the average pairwise nucleotide diversity between sequences ( π ) and the population nucleotide diversity parameter ( Watterson's θw ) expected under neutrality from the total number of segregating sites for a population at mutation-drift equilibrium [80] , with positive values ( when π>θw ) indicating an excess of intermediate frequency polymorphisms and negative values indicating an excess of rare polymorphisms . Fu and Li's test statistics D* and F* are based on the difference between the observed number of singleton nucleotide polymorphisms and the number expected under neutrality given estimates of nucleotide diversity from θw and π , for D* and F* respectively [81] . An R script was employed to automatically run Variscan 2 . 0 on all masked gene coding sequence alignments generated above , on a module that included only sites from alignments in which at least 50 out of the 65 isolates had nucleotide calls . Output was transformed into tables with a Matlab script and filtered to include only genes for which >70% of non-repeat nucleotide sites were confidently aligned . Gene alignment data meeting these criteria were further analysed for summary indices of allele frequency distributions and linkage disequilibrium using DnaSP 5 . 1 [82] to check for concordance of results obtained with Variscan 2 . 0 and perform additional tests including calculation of dN/dS ratios . Mann-Whitney tests were used to assess significance of differences in distribution of values of indices across different gene categories . Peak stage of gene transcript expression in published microarray data was assessed using an expression time series query in PlasmoDB ( http://plasmodb . org/plasmo/ ) [36] , [37] . Correlations between pairs of indices were analysed by Pearson's correlation coefficient , and comparisons between proportions of categorical variables were performed by Chisquare tests . Transcript levels derived by quantitative reverse transcriptase PCR , as described above for each of the 6 msp3-like genes , were normalized as a proportion of the sum of the transcript levels for these genes within each isolate . Expression profiles were generated using a heat map representing the relative proportions of each transcript using the Bioconductor suite in R , and Ward's clustering was applied to derive a hierarchical cluster analysis of isolate expression profiles , in which each object is initially assigned to its own cluster and then the algorithm proceeds iteratively by continually joining the two most similar clusters ( dissimilarities between clusters are the squared Euclidian distances between cluster means ) . Spearman's rank nonparametric correlation coefficient was used to measure the correlation between relative levels of expression . Mann-Whitney U tests were used to assess whether there were significant differences between dichotomous groups in the distributions of continuous variables including the relative amounts of each of the transcripts . Statistical analysis was performed using Stata version 9 . 0 or 11 . 0 software , and plots were generated using GraphPad Prism version 4 . 02 software .
|
The memory component of acquired immune responses selects for distinctive patterns of polymorphism in genes encoding important target antigens of pathogens . These are detectable by surveying for evidence of balancing selection , as previously illustrated in analyses of genes encoding malaria parasite antigens that are candidate targets of naturally acquired immunity . For a comprehensive screen to discover targets of immunity in the major human malaria parasite Plasmodium falciparum , an endemic population in West Africa was sampled and genome sequence data obtained from 65 clinical isolates , allowing analysis of polymorphism in almost all protein-coding genes . Antigen genes previously studied by capillary re-sequencing in independent population samples had highly concordant indices in the genome-wide analysis here , and this has identified other genes with stronger evidence of balancing selection , now prioritized for functional study and potential vaccine candidacy . The statistical signatures consistent with such selection were particularly common in genes with peak expression at the stage that invades erythrocytes , and members of several gene families were represented . The strongest signature was in the msp3-like gene PF10_0355 , so we studied the transcript and protein product in parasites , revealing an unexpected pattern of phase variable expression . Variation in expression of polymorphic antigens under balancing selection may be more common than previously thought , requiring further study to assess vaccine candidacy .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"parasite",
"evolution",
"population",
"genetics",
"immunology",
"microbiology",
"host-pathogen",
"interaction",
"parasitic",
"diseases",
"plasmodium",
"falciparum",
"parasitology",
"genome",
"sequencing",
"adaptive",
"immunity",
"infectious",
"diseases",
"genetic",
"polymorphism",
"biology",
"protozoan",
"infections",
"immunity",
"natural",
"selection",
"malaria",
"genomics",
"evolutionary",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
Population Genomic Scan for Candidate Signatures of Balancing Selection to Guide Antigen Characterization in Malaria Parasites
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Eukaryotic organelles evolve to support the lifestyle of evolutionarily related organisms . In the fungi , filamentous Ascomycetes possess dense-core organelles called Woronin bodies ( WBs ) . These organelles originate from peroxisomes and perform an adaptive function to seal septal pores in response to cellular wounding . Here , we identify Leashin , an organellar tether required for WB inheritance , and associate it with evolutionary variation in the subcellular pattern of WB distribution . In Neurospora , the leashin ( lah ) locus encodes two related adjacent genes . N-terminal sequences of LAH-1 bind WBs via the WB–specific membrane protein WSC , and C-terminal sequences are required for WB inheritance by cell cortex association . LAH-2 is localized to the hyphal apex and septal pore rim and plays a role in colonial growth . In most species , WBs are tethered directly to the pore rim , however , Neurospora and relatives have evolved a delocalized pattern of cortex association . Using a new method for the construction of chromosomally encoded fusion proteins , marker fusion tagging ( MFT ) , we show that a LAH-1/LAH-2 fusion can reproduce the ancestral pattern in Neurospora . Our results identify the link between the WB and cell cortex and suggest that splitting of leashin played a key role in the adaptive evolution of organelle localization .
Membrane bound organelles are fundamental constituents of eukaryotic cells that execute wide-ranging functions associated with growth and development . Some organelle functions are ubiquitous while others are only found in evolutionarily related organisms and perform lifestyle supporting adaptive functions . Most of the fungi proliferate through the extension and branching of tubular cells called hyphae . Hyphae can be divided into compartments by cell walls known as septa and septal pores provide a connection that allows adjacent cellular compartments to cooperate and coordinate their activities . This syncytial cellular architecture underlies many unique aspects of the fungal lifestyle including rapid radial growth , the invasive growth of saprobes and pathogens and the development of multi-cellular reproductive structures [1] . Major groups of filamentous Basidiomycetes and Ascomycetes have evolved distinct septal pore associated organelles [2]–[4] . Filamentous Ascomycetes ( The Pezizomycotina ) are a monophyletic group estimated to comprise 90% of Ascomycetes and 50% of all fungal species [5] and these ecologically diverse fungi [6] , [7] possess peroxisome-derived organelles called Woronin bodies ( WBs ) [4] , [8] . WBs are centered on a self-assembled matrix protein , HEX , and function to seal the septal pore in response to hyphal wounding [9]–[12] . WB biogenesis occurs in the growing apical hyphal compartment through a process determined in part by apically biased hex gene expression [13] . In apical compartments newly synthesized HEX is imported into peroxisomes via its consensus PTS1 sorting signal and assembled de novo into micrometer scale protein complexes [13] , [14] . The Woronin sorting complex protein ( WSC ) envelops HEX assemblies to help them bud from the peroxisome matrix [14] . Through physical association with the cell cortex , these newly formed organelles are inherited into sub-apical compartments where they are immobilized and poised to execute their function in pore sealing [13] , [15] . Cortex association also requires WSC [14] , but the link between WSC and the cell cortex remains unknown . Organelle Inheritance by cortex association is a recurring theme in fungal systems [16] . In the yeast Saccharomyces cerevisiae , organelles need to be equitably partitioned between mother and daughter cells and various organelles share a common strategy that balances cytoskeleton dependent transport into the bud with immobilization at the mother cell cortex . This type of mechanism has been implicated in the segregation of mitochondria [17] , [18] , peroxisomes [19] , and the endoplasmic reticulum [20] . The yeast peroxisome provides an especially clear example; here the peripheral peroxisome membrane protein Inp1 promotes cortex association . Deletion of Inp1 results in excessive acto-myosin dependent peroxisome transport into daughter cells while overproduction results in aberrant immobilization and accumulation of peroxisomes at the mother cell cortex [21] , [22] . Interestingly , the ability of WSC to promote cortex association also depends on its level in the membrane [14] , suggesting that the accumulation of key membrane proteins may regulate the segregation of diverse organelles . Within the Pezizomycotina , patterns of WB distribution vary systematically; in most species , WBs are tethered to the septal pore at a distance of 100 nm–200 nm and associate with the pore rim through a filamentous [15] and elastic [23] tether of unknown composition . By contrast , in a group defined by Neurospora and Sordaria , WBs occur at the cell cortex in a delocalized pattern , suggesting that a new pattern evolved in the common ancestor of these genera .
In a screen for genes involved in WB segregation , we identified a spontaneous mutation that behaves as a single recessive locus and accumulates HEX assemblies in the apical compartment ( Figure 1A ) and based on our functional analysis , we named this locus leashin ( lah ) . To assess the effect of the lah mutation on the function of WSC , the lah mutant was transformed to express WSC-GFP and RFP-PTS1 ( A marker of the peroxisome matrix ) . In the lah background , WSC envelops HEX assemblies to produce budding intermediates similar to those observed in wild-type cells [14] , however , these accumulate and aggregate aberrantly ( Figure 1B ) in the apical hyphal compartment , suggesting that LAH functions downstream of WSC and plays a role in WB inheritance . lah was mapped to the left arm of chromosome I by meiotic recombination and one cosmid carrying an ∼30-kilobase gene , NCU02793 , was found to complement the lah WB segregation defect and NCU02793 deletion results in phenotypes similar to those observed in the original lah mutant ( data not shown ) . The lah gene encodes the largest predicted protein ( 10 , 821 amino acids ) found in the Neurospora genome and homologs were identified in all sequenced genomes of the Pezizomycotina; these predicted proteins range in size from the smallest from Aspergillus nidulans ( 5 , 936 amino-acids ) to the largest in Giberella zea ( 7 , 480 amino-acids ) . Overall these proteins exhibit complex N- and C- terminal sequences separated by poorly conserved intervening sequences ( Figure S1 ) . Predicted Neurospora leashin is highly acidic with a calculated charge of −1088 at neutral pH and encodes three repetitive regions , R1 , R2 , and R3 ( Figure 1C and 1D ) , which are enriched in proline , leucine and acidic amino acids aspartic acid and glutamic acid . Below , we present evidence showing that Neurospora leashin actually comprises two transcription units encoding distinct proteins , which we name Leashin-1 and Leashin-2 . To begin to dissect the function of leashin , we used homologous recombination to introduce a stop codon at various positions of the predicted lah gene . Numbering from the start codon , truncation at nucleotide positions 3604 , 14902 and 16981 , produce defects in WB inheritance . By contrast , truncations at 17680 , 25786 and 31603 do not interfere with WB segregation and cause mild defects in maximal hyphal growth rate ( Figure 2 ) . These data indicate that the 5′-half of the lah locus is required for WB segregation while 3′-regions are dispensable . LAH should localize to the WB surface if it plays a direct role in WB segregation . We next assessed localization of LAH fragments of fused to the red fluorescent protein ( RFP ) and identified an N-terminal domain encompassing amino acids 1–344 ( LAH1–344RFP ) that co-localizes with WSC-GFP at the WB surface ( Figure 3A ) . In extracts prepared from cells expressing an HA-epitope tagged version of this LAH fragment , the fusion protein sediments at very low centrifugal forces , consistent with association with the dense-core WB , but is rendered mostly soluble in extracts prepared from a wsc deletion strain ( Figure 3B ) , suggesting that LAH associates with WBs via WSC . To further investigate this interaction , we examined WSC deletion mutants; deletion of the WSC C-terminal tail ( Δ236–307 ) blocks WB segregation , but not the envelopment of HEX assemblies and production of nascent WBs ( Figure 3C ) . WB localization of LAH1–344RFP is also abolished in the WSC C-terminal deletion , further suggesting that N-terminal sequences of LAH associate with WBs through the C-terminus of WSC ( Figure 3D ) . The large size of the predicted lah gene precluded its manipulation by standard recombinant methods . This prompted us to develop a new method , marker fusion tagging ( MFT ) that allows tagging and deletion analysis of chromosomally encoded genes . Briefly , fusion PCR was used to generate an in-frame fusion between the dominant selectable marker for Hygromycin resistance ( hygr ) and eGFP or the 3×HA epitope tag . A second round of fusion PCR was then used to append in-frame fragments from leashin to this central cassette . The integration of this cassette by homologous recombination results in the production of a fusion protein where Hygromycin resistance is engendered by the chromosomally encoded fusion protein . Using MFT , we integrated the hygr-gfp cassette at two positions biased to the N- ( 1-GFP ) and C- ( 2-GFP ) terminus of the predicted lah gene; both of these strains display wild-type growth and WB segregation , indicating that the tagged proteins are functional ( Figure 2 ) . 1-GFP is localized to the WB surface in a pattern similar to that produced by the LAH1–344RFP protein ( Figure 4A ) . Surprisingly , 2-GFP does not localize to the WB , but is found at the septal pore in sub-apical hyphae and in a single punctate structure immediately beneath the growing hyphal tip in apical compartments ( Figure 4A ) , suggesting localization to the vicinity of the Spitzenkörper , a vesicle supply center tightly associated with hyphal growth [24] , [25] . This punctate structure detached from the hyphal apex when hyphae stopped growing and in the largest hyphae , a LAH-2 ring structure with a diameter of up to 1 µm was observed ( Figure 4A ) . Additional insertions biased to the predicted N- and C- terminus also localized either to the WB or the septal pore and hyphal tip ( Figure 4A ) , further suggesting that leashin produces two distinct polypeptides . We next inserted the HA-epitope tag at positions 1 and 2 to estimate the size of lah encoded proteins ( Figure 4B ) . 1-HA identifies an ∼400 kDa protein associated with WB enriched fractions while 2-HA encodes a polypeptide of ∼450 kDa , which can be detected in total cell extracts ( Figure 4B ) . Together , these data suggest that the lah locus produces two distinct polypeptides localizing to different subcellular compartments . lah can produce two proteins by distinct mechanisms; a single polypeptide can be post-translationally processed or alternatively , the locus may produce two independent transcripts . To distinguish these models , we subjected the 2-GFP strains to a second round of insertional mutagenesis and used the Aspergillus nidulans panB gene to insert an upstream stop codon at position 1 to produce 1-STOP , 2-GFP . If two proteins are processed from a single precursor , this should abolish the 2-GFP signals . Alternatively , if the pore-localized polypeptide is produced from a second transcriptional unit , 2-GFP should persist . Consistent with the later model , 2-GFP is readily detected at the septal pore in the presence of 1-STOP , suggesting that the 3′-end of lah encodes an independent gene ( Figure 4C ) . We designated these two-transcription units lah-1 and lah-2 . We next sought to define the lah-2 promoter by fusing overlapping fragments from the putative promoter region to the hygr gene and assessing their ability to confer Hygromycin resistance ( Figure 5A and 5B ) . We assessed 7 fragments encompassing approximately 10 KB and found one fragment capable of engendering levels of stable Hygromycin resistant colonies comparable to the strong ccg-1 promoter ( Figure 5B ) [26] , suggesting that this region contains the lah-2 promoter . We were unable to detect lah transcripts by Northern blotting but were able to use RT-PCR to amplify overlapping fragments encompassing the entire lah locus ( Figure S2 ) . We confirmed all introns predicted at the Broad Institute Neurospora crassa database ( http://www . broad . mit . edu/ ) , and identified seven new introns ( Figure 5A and Figure S2 ) . Six of these are in-frame with predicted lah exons , suggesting that they do not significantly alter the encoded polypeptide . However , splicing of Intron 10 , found immediately upstream of the lah-2 promoter sequences produces a new reading frame and 2607 bp exon leading to a down-stream stop codon ( Figure 5A ) . An MFT tag introduced into this frame decorates the WB , suggesting that these sequences encode the C-terminus of LAH-1 . Moreover , unlike the N-terminally biased LAH-1 tag which uniformly decorates the WB ( Figure 4A ) , the signal from this C-terminally biased tag is enriched between the WB and cell cortex ( Figure 5C ) , suggesting that LAH-1 C-terminal sequences cluster at the cell cortex . Collectively these data show lah encodes two large polypeptides from distinct regulatory sequences . LAH-1 is required for WB segregation and binds WBs via WSC and requires its C-terminal sequences for cortex association . LAH-2 and LAH-1 possess related repetitive sequences , but LAH-2 is not required for WB tethering and localizes in a novel pattern at both the growing hyphal apex and septal pore . Mutations in lah-2 produce mild defects in maximum growth rate ( Figure 2 ) . We carefully examined the lah-2 deletion mutant and found significant defects in radial growth early in colony establishment ( Figure S3 ) , suggesting that lah-2 plays an important role in colony development . Woronin bodies are synthesized in the apical hyphal compartment and segregated into sub-apical compartments by tethering to the cell cortex [13]–[15] . In most of the Pezizomycotina , mature organelles are tethered at the septal pore [8] , [15] . By contrast , in Neurospora and close relative Sordaria , WB biogenesis is not closely associated with the hyphal tip and segregation is achieved by a dispersed pattern of cortex association [8] , [13] ( Figure 6A ) . To better understand the evolution of these two modes of cortex association , we used 18S ribosomal RNA to construct a phylogenetic tree from a group of species where Woronin body position has been determined and used the yeast Saccharomyces cerevisiae and Schizosaccharomyces pombe , which do not contain WBs or the hex gene [27] as outgroups . This tree produces a pattern of relationships consistent with previous reports [6] , [7] . Neurospora and Sordaria comprise a clade within the Sordariomycetes and both possess the dispersed pattern of cortical association while all the basal lineages possess the septal-pore associated pattern , suggesting that septal pore tethering is the ancestral mode of segregation ( Figure 6A ) . Variations in the function of the leashin could determine evolutionary changes in patterns of cortical association . Specifically , we hypothesize that splitting of a single ancestral lah gene might have led to the derived pattern of WB-localization . In this case , the reunion of lah-1 and lah-2 should recreate the ancestral pattern of WB-distribution in Neurospora . MFT permits this experiment – we deleted intervening sequences , fusing N- terminal sequences of LAH-1 with C-terminal sequences of LAH-2 to produce a single chromosomally encoded polypeptide . Strains bearing this fusion as the sole version of leashin displays a pattern of WB-localization remarkably similar to the ancestral pattern ( Figure 6B ) : HEX assemblies accumulate in the vicinity of the septal pore on both sides of the septum , and in a cluster immediately beneath the hyphal tip . Both of these localization patterns are abolished in a wsc deletion background ( data not shown ) , indicating that the hybrid Leashin engages WBs via WSC . The LAH-1/2 expressing strain also grows slightly faster than the lah-1 deletion strain ( Figure S3 ) and presents a significant reduction in tip lysis induced protoplasmic bleeding ( Figure 6C ) , suggesting that the fusion protein is partially functional .
Woronin body biogenesis occurs through a series of steps that link organelle morphogenesis and inheritance . HEX self-assembles to form the organellar core [10] and recruits WSC to the membrane . In turn , WSC envelops HEX assemblies in the matrix [14] and recruits LAH-1 in the cytoplasm ( Figure 3 ) . In the absence of LAH-1 , nascent WBs fail to segregate and instead accumulate aberrantly in the apical compartment ( Figure 1 ) . Final fission of Woronin bodies from their mother peroxisomes also fails in the absence of LAH-1 ( Figure 1B ) , suggesting that membrane fission [28] occurs after cortex association . WB biogenesis has interesting parallels with the biogenesis of neuroendocrine dense-core secretory granules . In these organelles , aggregates of self-assembled core proteins are believed to promote budding from the trans-Golgi network through physical interaction with the membrane ( for a review , see [29] ) . Aggregates at the center of dense-core secretory granules contain a complex mixture of proteins . By contrast , WBs with comparatively simple core composition and known membrane receptor [14] provide a genetically amenable system to study basic principles of aggregate promoted vesicle budding . Neurospora LAH-1 possesses the properties expected of a tether; an N-terminal domain sufficient for WB localization ( Figure 3 ) is linked to C-terminal sequences required for cell cortex association ( Figure 2 ) by a poorly conserved linker domain enriched in repeat sequences ( Figure S1 ) . The central regions of fungal LAH proteins are not conserved at the level of primary sequence , but retain a similar character; they are highly acidic and enriched in the amino acids , PELS . Interestingly , PEVK repeats in the vertebrate muscle protein Titin adopt a random coiled configuration that forms an elastic filament [30] . WBs have been manipulated using laser-tweezers and observed recoiling towards the septum after being pulled and released [23] , suggesting that WB-tethers are also elastic . In this case the similarities between PEVK regions of Titin and PELS regions of LAH proteins may represent overlapping convergent solutions to the problem of protein elasticity . In most of the Pezizomycotina , WBs are associated with the septal pore at a distance of 100 to 200 nm and the length of the LAH proteins may determine the spacing between the cell cortex and WB . The largest known protein , vertebrate Titin is 4 mega-Daltons in size and has been purified and measured at approximately 1 µm in length [31] . lah genes in the Pezizomycotina are predicted to encode proteins between around 600 and 800 kDa and based on the size of Titin are expected to span a distance of around 200 nm , which is in reasonable agreement with the observed distance between WBs and the septum [15] . The presence of the WB specific genes hex [4] , [10] , wsc [14] and leashin in sequenced genomes of diverse members of the Pezizomycotina and their absence from sequenced genomes of fungi found outside this group , suggests that WBs arose in a common ancestor of the Pezizomycotina . Adaptations that occur at the origin of successful clades are likely to continue evolving as the group undergoes ecological specialization and evolutionary radiation . Neurospora and its close relative Sordaria are distinguished from other Pezizomycotina in several aspects of hyphal organization and physiology . In addition to the dispersed pattern of cortical association , both manifest large vegetative hyphae , extensive protoplasmic trafficking through septal pores [13] , [32] ( see Video S1 for trafficking in Sordaria fimicola ) and unusually rapid growth , which in Neurospora can exceed 1 µm/second [33] . In nature , Neurospora occurs on burnt vegetation soon after forest fires [34] and this ecology may explain the evolution of rapid growth . We further speculate that extensive protoplasmic streaming may be incompatible with WB-tethering at the septal pore and provided a selective pressure for evolution of the delocalized pattern of cortex association . Our analysis suggests that three events were required to evolve lah-1 and lah-2 from a single ancestral locus ( Figure 7 ) . These are - evolution of promoter sequences for independent production of lah-2 , intragenic termination for the production of lah-1 and acquisition of a new cortex-binding domain in lah-1 . Splicing at intron 10 results in an alternative exon that terminates with a stop codon immediately upstream of the lah-2 promoter region ( Figure 5A ) . MFT tags in this exon are enriched between the WB and cell cortex ( Figure 5C ) and this region is required for WB inheritance ( Figure 2 ) , suggesting that C-terminal sequences of LAH-1 constitute a new cortex-binding domain . Tethering is a fundamental eukaryotic strategy to control and coordinate the activities and spatial distribution of cellular organelles . For example , in the secretory pathway tethers control Golgi architecture and help determine the specificity of vesicle trafficking ( for reviews , see [35] , [36] ) . Tethers can also provide stable connections between functionally related organelles allowing for their efficient communication [37] . The Leashin tether promotes WB inheritance and holds the organelle in position until signals from cellular damage induce release , translocation to the septal pore and membrane resealing . Future work focused on these aspects of WB function should provide insights into reversible tethering , fungal signal transduction and plasma membrane dynamics .
Vogel's N synthetic medium was used for growth in solid and liquid medium and for the induction of colonial growth; conidia were grown in plating medium [38] . Neurospora crassa conidia were transformed either by electroporation [39] or when cosmids were transformed , by chemical transformation of spheroplasts [40] . Strains used in this study can be found in Table S1 . For the measurement of growth shown in Figure 2 , Neurospora strains were grown on solid Vogel's medium overnight with appropriate supplements in 25 ml pipettes used as race tubes . Maximal growth rate was assessed after an initial period of 16 hours . Data shown are the mean and standard deviation from three replicates . Confocal microscopy and Woronin body quantification were conducted as previously described [14] . For the determination of protoplasmic bleeding ( Figure 6C ) , strains were grown embedded in top agar containing sorbose , which induces tip lysis and colonial growth [38] . 0 . 75 mg/ml of Phyloxin dye is added to enhance the visualization of bled protoplasm . On the third day , images were obtained at the periphery of colonies using a stereomicroscope and the volume of the largest 15 bleeds from each colony was calculated using MetaVue software . A total of 225 individual hyphal bleeds were examined for each strain . Plasmids for the expression of WSC-eGFP ( GJP#1081 ) and RFP-PTS1 ( GJP#1406 ) were previously described [14] . To delete the C-terminal tail of WSC , two Xba I site encompassing the deleted region were created using site directed mutagenesis . The mutated plasmid was digested with Xba I and re-ligated resulting in the deletion of amino acids 236 to 307 . To generate pmf272::lah1–344GFP , an Xba I to Pac I fragment encompassing the first 1032 nucleotides of leashin was inserted into pmf272 [26] , to generate pmf272::lah1–344GFP ( GJP# . 1812 ) . GFP was replaced using Eco RI and Pac I to create RFP ( GJP#1898 ) and 3× HA ( GJP#1848 ) tagged versions of this plasmid . Primers used for the construction of these plasmids can be found in Table S7 . For differential centrifugation , extracts were prepared from frozen Neurospora powder as previously described [13] . The lysate was passed through a 40-µm cell strainer to obtain a crude cellular extract . This extract was centrifuged at 100×g for 2 minutes to remove cellular debris . This cleared extract was centrifuged at 1 K×g and 10 K×g for 45 minutes and supernatant and pellet fractions were collected and analyzed by western blotting ( Figure 3B ) . For Figure 4B ( 2-HA ) , 100 µl of the frozen Neurospora powder was added in equal volume of 2× loading buffer ( 2× LB ) , boiled and analyzed by western blotting . For Figure 4B ( 1-HA ) , frozen Neurospora powder were extracted in isolation buffer ( 20 mM Hepes pH 6 . 8 , 150 mM KCL ) with protease inhibitor cocktail ( Roche ) and a crude lysate was isolated as described above . The lysate was centrifuged at 6 K×g for 10 minutes and this pellet was re-suspended in isolation buffer containing 0 . 5% Triton X-100 ( TX-100 ) . The sample was passed through a 5-µm filter and centrifuged at 6 K×g for 10 minutes . The pellet was then washed twice in the same buffer using centrifugation at 8 K×g for 10 minutes . The final pellet , highly enriched in Woronin bodies was dissolved in 2× LB and analyzed by western blotting .
|
In the kingdom Fungi , tubular cells called hyphae grow by tip extension and lateral branching to produce an interconnected multicellular syncytium and this unique cellular architecture is especially suited to foraging , long distance transport , and invasive growth . Major groups of fungi have independently evolved cellular organelles that support this form of multicellularity . Woronin bodies evolved over 400 million years ago in the common ancestor of filamentous Ascomycetes and perform an adaptive function to seal pores that connect hyphal compartments ( septal pores ) in response to cellular wounding . This study identifies Leashin , a tethering protein that promotes equitable Woronin body inheritance by providing a link to the cell cortex . Patterns of cortex association display systematic variation; in most of the filamentous Ascomycetes , Woronin bodies are tethered to the septal pore . By contrast , a delocalized pattern has recently evolved in a group represented by Neurospora and Sordaria . We present evidence suggesting that the ancestral leashin gene was split into two independent transcription units to permit this evolutionary transition . This work is exemplary of how filamentous Ascomycetes with well-resolved phylogenetic relationships , diverse sequenced genomes and powerful haploid genetics provide model systems for understanding evolutionary innovation within a functional cellular and physiological context .
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2009
|
A Tether for Woronin Body Inheritance Is Associated with Evolutionary Variation in Organelle Positioning
|
Future infectious disease epidemics are likely to disproportionately affect countries with weak health systems , exacerbating global vulnerability . To decrease the severity of epidemics in these settings , lessons can be drawn from the Ebola outbreak in West Africa . There is a dearth of literature on public perceptions of the public health response system that required citizens to report and treat Ebola cases . Epidemiological reports suggested that there were delays in diagnosis and treatment . The purpose of our study was to explore the barriers preventing Sierra Leoneans from trusting and using the Ebola response system during the height of the outbreak . Using an experienced ethnographer , we conducted 30 semi-structured in-depth interviews in public spaces in Ebola-affected areas . Participants were at least age 18 , spoke Krio , and reported no contact in the recent 21 days with an Ebola-infected person . We used inductive coding and noted emergent themes . Most participants feared that calling the national hotline for someone they believed had Ebola would result in that person’s death . Many stated that if they developed a fever they would assume it was not Ebola and self-medicate . Some thought the chlorine sprayed by ambulance workers was toxic . Although most knew there was a laboratory test for Ebola , some erroneously assumed the ubiquitous thermometers were the test and most did not understand the need to re-test in the presence of Ebola symptoms . Fears and misperceptions , related to lack of trust in the response system , may have delayed care-seeking during the Ebola outbreak in Sierra Leone . Protocols for future outbreak responses should incorporate dynamic , qualitative research to understand and address people’s perceptions . Strategies that enhance trust in the response system , such as community mobilization , may be particularly effective .
Countries without the capacity to conduct surveillance , monitor , or control outbreaks are particularly at risk for negative consequences of infectious disease epidemics [1] . These countries’ inability to swiftly contain infectious diseases can result in global vulnerability to epidemic spread [2] . Since the Ebola outbreak in West Africa , there have been calls to improve the technical capacity for outbreak response [1 , 2] . Lessons from the Ebola outbreak can inform the development of future effective epidemic responses in under-resourced settings . As of January 2016 , Ebola resulted in 3 , 956 cumulative confirmed deaths in Sierra Leone [3] . One study estimated that Ebola likely killed more people in Sierra Leone in 2014 than the second ( lower respiratory infections ) and third ( HIV/AIDS ) leading causes of death and may have killed more people than the leading cause of death ( malaria ) [4] . A weak health system is partly to blame for Sierra Leone’s devastating experience of the 2014–16 Ebola virus disease outbreak ( Ebola ) [5–7] . In December 2014 , the U . S . Centers for Disease Control ( CDC ) reported that Sierra Leone had more Ebola cases than Guinea and Liberia and that a significant number of Ebola-infected persons in Sierra Leone were being identified only after death , suggesting that these persons were either not captured by the surveillance system or were reluctant to use the response system [8] . Furthermore , WHO reported that between August and December 2014 the time between the onset of Ebola symptoms and hospitalization averaged two to three days [9] . This time lag may be explained by a reluctance to seek care . Nevertheless , in September 2014 , over 90% of participants in a national household survey said they would go to a hospital or health facility if they thought they had Ebola [10] . In December 2014 , 95% agreed that a person had a higher chance of survival if s/he immediately went to a health facility [11] . Although Sierra Leoneans reported high intentions for using the response system , previous anthropological studies of Ebola outbreaks elsewhere in Africa revealed apprehension about using it . These studies cited various factors including high Ebola-related fatality rates in hospitals , the isolation of infected patients , and the fear induced by the response workers’ full-body personal protective suits as reasons for people’s apprehension [12 , 13] . Patients fled hospitals and refused to refer loved ones for treatment [13] . These studies identified a need for qualitative research on the barriers to trusting the public health response system during outbreaks [12] . Trust has received increased attention as a key concept determining personal compliance with and the overall success of public health efforts in developing countries [14–16] . Trust is conceptualized as a person or group’s perception of the health system , as well as their confidence in the system to competently deliver health services and contribute to overall social well-being [16 , 17] . Trust is influenced by individual and peer or family members’ experiences of the system , the reputation of the system , and messages from the media [15 , 16] . For example , a study from Tanzania demonstrated that pregnant women’s low trust , based in their experiences of poor quality services , impeded their use of rural maternal health care [14] . In a recent review article , Cairns et al . argued that risk communications moderate the relationship between trust and individual behavior during communicable disease outbreaks [15] . They suggested that designing credible and effective risk communication messages requires in-depth assessment of public understanding and risk perceptions at multiple time points during an epidemic [15] . The purpose of our study was to explore Sierra Leoneans’ perceptions of and intentions for using the Ebola response system at a critical time during the outbreak . We did not have any a priori hypotheses , but aimed to inform ongoing public health efforts to minimize Ebola’s spread . We used semi-structured in-depth interviews and our research team included an anthropologist with over twenty years’ experience conducting research in Sierra Leone ( third author ) . Our inductive analysis revealed that trust was related to several of our findings .
This study occurred during January through March 2015 in Western Urban and Bo Districts , the two most populous districts in Sierra Leone . When formative research for the study commenced ( 1 January 2015 ) , Sierra Leone had 248 new confirmed cases that week , the highest number in any of the three affected countries [18] . Western Urban , including Freetown , had the most new confirmed cases ( 93 ) [18] . Bo District , although heavily affected from September to November 2014 ( with as many as 33 cases per week ) , reported only three new cases in the first week of 2015 and even fewer as the outbreak continued [18] . Our research occurred just after the rapid scale-up of the response system in Sierra Leone [6] . In January 2015 , there were 4 . 6 beds for every confirmed and probable case , compared to 1 . 0 bed in October 2014 [19] . By January 2015 , there were eleven laboratories nationwide , and every district had a laboratory and a contact tracing team to monitor quarantined individuals [19] . A national hotline ( call 117 ) , established on August 5 , 2014 , responded to reports of suspected Ebola cases and reports of any deaths , and also provided information about Ebola to citizens [20] . Fig 1 illustrates the Ebola response system from the perspective of a potential user , as confirmed by our observations during fieldwork . There were three ways to meet the “suspected Ebola case” definition: 1 ) a temperature of greater than 100 . 4°F ( 38 . 0°C ) and three or more Ebola symptoms ( vomiting , diarrhea , abdominal pain , headache , joint pain , fatigue , or unusual bleeding ) ; 2 ) a fever and contact with a confirmed case in the preceding three weeks; or 3 ) unexplained bleeding [8] . Those who experienced any of these symptoms or noticed these symptoms in others were encouraged to call 117 [20] . The call center dispatchers alerted district-level response teams [20] . Ambulance workers dressed in personal protective equipment ( PPE ) arrived to transport suspected cases to holding centers . The ambulance workers also sprayed chlorine at homes of suspected cases in order to inactivate the virus [21] . Temporary holding centers isolated suspected Ebola patients awaiting laboratory test results [22] . Due to inefficiencies in the transportation and reporting systems , returning results on an Ebola test took a minimum of 48 hours [22] . People confirmed to have Ebola then moved to a free-standing Ebola Treatment Unit for isolation and care [22] . A contact tracing team then worked to find and monitor all those with whom the infected person had had contact [23] . Those confirmed not to have Ebola were released back into the community and told to return for testing if they developed Ebola symptoms . We reviewed the consent form verbally with each potential participant and obtained verbal consent . Our ethics committees approved the use of verbal consent in part because the only record linking the participant to the research was the consent document . Furthermore , we anticipated that some participants may not have wanted their names recorded for fear of stigma and that some participants would be illiterate . Verbal consent was requested and then documented by the study interviewer . To preserve anonymity , we conducted all interviews in a semi-private public space where the content of the interview could not be overheard , we did not collect names , and we redacted identifying information from the transcripts . As a token of appreciation , we provided each participant with a bottle of hand sanitizer , a valued but not readily available commodity . The Institutional Review Boards ( IRB ) at American University ( AU ) and the Office of the Sierra Leone Ethics and Scientific Review Committee approved our ethical procedures . Unfortunately , we did not obtain permission from participants to release their data for public use , and thus the AU IRB did not grant permission to release the data . The eligibility for our study included: being aged 18 years and older; having had no known contact with an Ebola-infected person for the past 21 days ( to protect ourselves and our future contacts ) ; and willingness to provide informed consent . We conducted semi-structured in-depth interviews with a convenience sample of thirty Sierra Leoneans split evenly between Western Urban and Bo Town . We recruited participants by approaching people in public places such as marketplaces , roadsides , checkpoints , and previously quarantined settlements . Although congregating was prohibited at this time of the outbreak , there were still people publically socializing . We elected to recruit participants in public places rather than through non-governmental organizations in order to capture a more general population . We purposively interviewed participants who looked to be diverse along three demographic characteristics: gender , age and occupation . These characteristics were observed by the interviewer and then a potential participant was approached . No person refused to participate . The interviews lasted thirty to forty minutes and were conducted in Krio by an experienced ethnographer ( the third author ) and a trained , local qualitative interviewer . Questions included people’s beliefs about Ebola , their perception of their risk for Ebola and what they had done to protect themselves . We asked participants to tell us what they would do if they or someone they knew developed a fever , and if they would call or had called the national hotline . We also asked about their perceptions of the Ebola laboratory test . Demographic characteristics captured included the participant’s age , occupation and sex . All interviews were translated to English from Krio by two research assistants and the translations were verified by the third author . All authors read all English transcripts . Using Atlas . ti version 7 , the second author assigned codes deductively from the interview guide and then inductively identified emergent codes from the data . The codes and quotes were reviewed by the lead investigator . Once the data were categorized , comparisons were made between participants . Data saturation was reached after several rounds of analysis by all authors . The most salient and common themes regarding respondent perceptions of the Ebola response system were described and matrices were used to summarize diverse perceptions related to each theme .
We interviewed sixteen men and fourteen women aged 18–56 . In approximately equal proportions , the categories of their occupations included: 1 ) business ( trader , seamstress , manager ) ; 2 ) social service ( nurse , teacher , burial team driver ) ; 3 ) petty business ( car wash , gambling , roadside seller ) ; 4 ) youth ( students , young mother ) ; and 5 ) community leaders ( section chief , pastor , community-based organization leaders ) . Nearly all participants reported that at the epidemic’s beginning they did not believe Ebola was real . Some believed Ebola was another known illness like cholera . Other explanations included government collusion: However , nearly all participants reported that they came to believe Ebola was real because they observed or knew of people dying , or because they heard that health professionals died: Given that our participants believed in Ebola we asked them about their likelihood of using the response system . Our results are described below and summarized by Fig 2 . In response to the question “what would you do if you had a fever ? ” very few participants said they would call 117 right away . A few participants said they would call , but only when the fever was very bad: if they could not walk , the fever persisted more than a few days , or it did not respond to other treatment . In about half of interviews , respondents said they would start by treating themselves with medicines normally used for other illnesses such as malaria , typhoid or cholera . Several participants claimed to know what the fever would feel like if it was Ebola , and that they would wait for those symptoms before reporting: Three-quarters of participants said that if they got a fever they would go to a local health center before calling 117 . One participant said that he could not afford to go to a health center and so he would pray . Another respondent said she would go to a health center , but she was afraid of being sent to an Ebola treatment unit . Participants were more willing to call 117 if they observed symptoms in another person . However , about one-third of participants stated they were afraid of community members’ anger if they were to call . One person said that he would not call personally , but he was confident someone else in the community would . Some expressed fears that by calling they would never see the person again: Only three participants had ever called 117 . One person reported that he called 117 for his neighbor and the neighbor became very angry with him . Another person reported calling six times , not receiving a response , and then seeking other health care resources for the suspected case . About one-third of participants expressed the belief that chlorine was harmful to people . Most believed danger arose when patients were put inside ambulances with chlorine fumes: However , some respondents who believed chlorine was harmful also used chlorine as a part of their preventative hand-washing to avoid spread of the disease . Some did not think that the chlorine in the hand-washing bucket was the same as that used by health workers to spray . Two respondents even believed it might be the chlorine spray that was killing people and not Ebola: When asked what they knew about the laboratory Ebola test , nearly half of participants said they did not know anything . When asked explicitly about what they knew about the test , five respondents were only able to discuss the use of infrared thermometers as a pre-test for Ebola: Four respondents mentioned the test occurring only after a person had died . Most did not have a clear idea of how the testing process worked . Many respondents expressed concern or even disbelief that the testing process was valid: Some also misunderstood the need to re-test for Ebola . Several participants discussed that they did not understand how a person who tested negative for Ebola could have subsequently died because their original Ebola “status was negative . ”
While national surveys showed high levels of intent to use the Ebola response system during the 2014–2015 outbreak in Sierra Leone [10] , our qualitative findings reveal apprehension that may have contributed to the low usage rates . Several of our findings , including participants’ reluctance to call the national hotline and endure the chlorine sprayed by ambulance workers , point to a lack of trust in the Ebola response system . While other researchers have reported on people’s fears of the response system’s management of burials and quarantine during the West African outbreak [24–26] , we explore aspects of resistance that , to our knowledge , have not yet been discussed . Below we reflect on our findings , discuss how the response system might have enhanced trust , and provide concrete suggestions for the public health response during an Ebola outbreak . Participants in our study reported reluctance to call the national hotline if they had a fever because they were afraid they would not return alive . Participants reported seeing many deaths at the beginning of the epidemic which provided the rationale for these fears , as did the high Ebola-related fatality rate in 2014 in Sierra Leone [22 , 27] . Although Ebola treatment and surveillance capacity had increased greatly by the time of our research [6] , participants’ early impressions likely remained salient in their attitudes towards accessing the response system . Trust in a public health system is enhanced when the system demonstrates accountability to its users [14] . Thus , one implication of this finding is that demonstrating an effective response very early during an outbreak is useful for gaining citizens’ trust and ongoing use of a response system . Participants also reported they would ensure that a fever was due to Ebola , rather than another illness , before seeking care . In the interim they would try to self-medicate or visit local trusted health care facilities . This finding is a potential explanation for why there was a two to three day delay between onset of Ebola symptoms and hospitalization [9] . However , it is important to note that local facilities often did not have adequately trained personnel to deal with Ebola [6] . Therefore , we suggest that public health efforts directly address the similarities between Ebola and other illnesses , as well as encourage citizens to use specialized health facilities . Some participants feared that the chlorine used by ambulance workers was toxic and potentially lethal . Participants’ fears may have resulted from real observations . There was a reported case of a nurse experiencing respiratory distress and brief loss of consciousness after inhalation of highly concentrated chlorine gas at an Ebola treatment unit in Sierra Leone [28] . To our knowledge , public health messaging did not substantially address the fear of chlorine until May 2015 [29] . Our participants were not fearful of the chlorine they used to wash their hands , referred to as “Britex” in Sierra Leone . Thus , we suggest that public health messaging use local terms to describe the chlorine spray and reassure citizens that it is not dangerous . We found that participants did not understand the need to re-test for Ebola should symptoms develop . Several participants used the word “status” to refer to the Ebola test . It is possible that they were appropriating HIV prevention language which encourages citizens to “know your HIV status . ” Thus , people diagnosed as Ebola-negative may have returned to communities thinking they were Ebola-free , despite a high likelihood of exposure while waiting at holding centers . Although the importance of re-testing may be challenging to communicate , it is crucial to identify the most relevant information to distribute . Moreover , public health messages persisted in communicating the “Ebola is real” message , although most of our participants already believed that . Building trust in a response system requires institutions to distribute risk reduction messages that are tailored to the public’s perceptions over time [15] . Our findings suggest that as an outbreak persists , more complex messages should be distributed to the public like the need for repeat testing and the effects of the chlorine sprayed by ambulance workers . Sierra Leoneans’ distrust of the state and lived experiences of corruption may have also negatively affected perceptions of the government-run Ebola response system [6 , 30 , 31] . The devastating civil war created a breakdown of civic trust , whose rehabilitation is hampered by continued poor governance and corruption , including in the health care system [30] . Mistrust in a state’s ability to respond to an epidemic is not unique to Sierra Leone . A quantitative study of Italian citizens during January to March 2015 found that they did not trust in their institutions’ preparedness for Ebola [32] . Health care systems can build trust by exhibiting technical competence , transparency , and reliability during outbreaks and developing strategic response plans during non-outbreak periods [14 , 15 , 17] . Using cultural insiders and leaders to address people’s misperceptions and demonstrate accountability to the public can also enhance trust and encourage health system use [26 , 33] . A case study of community resistance from February 2015 in a village of the Guinean Forest region provides an example [34] . To transform the villagers’ perception that the Ebola treatment centre ( ETC ) sold people’s organs for trade , the organization responsible for the ETC asked four survivors to publicly discuss their positive experiences in the ETC . After the survivors’ testimonials , the villagers cooperated with the contact tracing and surveillance team . An understanding of community perceptions and the use of cultural insiders were successful strategies in this case . There are several limitations of our study . We interviewed people during a time when resources were adequate for the cases reported . Following the recommendations by Cairns et al . , we ideally would have interviewed people at multiple time points during the epidemic to assess their dynamic perceptions of the response system [15] . In addition , although our sample was diverse , we cannot claim it was representative of a larger population . Restrictions on movement during the Ebola outbreak constrained our interviews to two urban parts of Sierra Leone , preventing a broader reach . In conclusion , our research during the Ebola outbreak revealed that Sierra Leoneans had several fears and misperceptions about using the response system that have heretofore not been reported . We used in-depth interview methods and an experienced ethnographer , allowing us to explore lower-than-ideal usage of the response system in the face of national surveys that demonstrated uniformly positive attitudes towards it . Our findings and reflections on trust as a conceptual framework contribute several concrete suggestions for public health response during an Ebola outbreak . These suggestions include the need to conduct multiple and in-depth assessments on the public’s perceptions of the response system and to use these data to distribute more complex public health messages as the epidemic progresses .
|
To decrease the severity of epidemics in countries with under-developed health system capacity to control outbreaks , lessons can be drawn from the Ebola outbreak in West Africa . This is the first study , to our knowledge , to use qualitative research methods to understand community members’ perceptions of using the Ebola response system during the outbreak in Sierra Leone . We conducted this study in two of the most populous districts during a time when there were still a high number of Ebola-related fatalities , and the Ebola response system had been scaled up . While national household surveys demonstrated high levels of intent to use the response system at the time , epidemiological reports suggested that there were delays in seeking testing and treatment . Our use of semi-structured in-depth interviews , as well as an ethnographer with experience in Sierra Leone , enhanced our ability to elicit people’s fears and misperceptions . Concerns about the response system clustered around three key themes: fears of calling the national hotline , negative perceptions of the chlorine spray , and misperceptions about the Ebola laboratory test and the need to re-test . These fears and misperceptions likely delayed people from seeking care . Our results lend support to the argument that trust in the public health response system was integral to citizens’ use of the system . We make several recommendations for how trust could have been enhanced during the Ebola outbreak . Protocols for future outbreaks should incorporate dynamic and qualitative research both to understand perceptions of the response system and to use these data to inform a more effective response .
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2016
|
Fears and Misperceptions of the Ebola Response System during the 2014-2015 Outbreak in Sierra Leone
|
New strategies to combat the global scourge of schistosomiasis may be revealed by increased understanding of the mechanisms by which the obligate snail host can resist the schistosome parasite . However , few molecular markers linked to resistance have been identified and characterized in snails . Here we test six independent genetic loci for their influence on resistance to Schistosoma mansoni strain PR1 in the 13-16-R1 strain of the snail Biomphalaria glabrata . We first identify a genomic region , RADres , showing the highest differentiation between susceptible and resistant inbred lines among 1611 informative restriction-site associated DNA ( RAD ) markers , and show that it significantly influences resistance in an independent set of 439 outbred snails . The additive effect of each RADres resistance allele is 2-fold , similar to that of the previously identified resistance gene sod1 . The data fit a model in which both loci contribute independently and additively to resistance , such that the odds of infection in homozygotes for the resistance alleles at both loci ( 13% infected ) is 16-fold lower than the odds of infection in snails without any resistance alleles ( 70% infected ) . Genome-wide linkage disequilibrium is high , with both sod1 and RADres residing on haplotype blocks >2Mb , and with other markers in each block also showing significant effects on resistance; thus the causal genes within these blocks remain to be demonstrated . Other candidate loci had no effect on resistance , including the Guadeloupe Resistance Complex and three genes ( aif , infPhox , and prx1 ) with immunological roles and expression patterns tied to resistance , which must therefore be trans-regulated . The loci RADres and sod1 both have strong effects on resistance to S . mansoni . Future approaches to control schistosomiasis may benefit from further efforts to characterize and harness this natural genetic variation .
Approximately one-sixth of the global burden of infectious disease in humans is due to parasites transmitted by invertebrate hosts [1] . A major avenue to combat these diseases lies in understanding the genetic and biochemical basis for natural variation in host resistance [2] , [3] . The identification of resistance genes will facilitate genetic manipulation or marker-assisted selective breeding for resistance [4] . An understanding of host-parasite interactions may also suggest molecular therapeutic targets in the parasite . In addition , genes affecting host fitness and host-parasite co-evolution may serve as markers to predict and monitor host population responses to changes in parasite prevalence or virulence . Although arthropod vectors have been the focus of most work on host resistance genetics , other important hosts include the aquatic snails that carry schistosomes . Measured in terms of healthy years of life lost , schistosomiasis ranks among the most consequential of infectious diseases , costing on the order of 10 million disability-adjusted life years [5] . Specifically , schistosomes infect over 200 million people [6] , [7] , causing a chronic disease burden that can be lifelong [8] , [9] . Furthermore , the disease causes up to 200 , 000 deaths per year [10] . Treatment is usually based on regular dosing with a single drug , praziquantel [11] , which is likely to become less effective as the parasite evolves drug resistance [12] , [13] . Resistance of schistosomes to other antihelminthic therapeutics like artemisinin [14] is also possible . Vaccine development is proving elusive [15] . Thus , the development of alternative and complementary strategies , including those targeting the aquatic snails that serve as intermediate hosts , is crucial for controlling this disease . A major barrier to developing new strategies to interrupt transmission is our limited understanding of the molecular pathways by which snails and parasites interact . Resistance to schistosome infections is highly heritable in snails , and resistance often appears to be due to one or a few major-effect loci [16–19] . However , there are still very few snail loci at which allelic variation is known to associate with resistance . Most genetic work has focused on the New World snail Biomphalaria glabrata . Expression levels of some B . glabrata genes are correlated with resistance [20–23] , but it is unknown whether this variation is controlled by cis-regulatory elements , and therefore whether these genes are fundamentally a cause of resistance . The Guadeloupe Resistance Complex ( GRC ) is a snail genomic region with a strong effect on resistance to Schistosoma mansoni in a Caribbean population of B . glabrata [3] , but its importance outside of this population remains to be shown . Alleles of the sod1 gene , encoding superoxide dismutase , affect resistance to S . mansoni in the 13-16-R1 laboratory population of snails , possibly via the role this gene plays in the oxidative burst [24] , [25] , or perhaps owing to linkage with other immunity genes [26] . A few additional molecular markers are tied to resistance [18] , [27] , but the functional genes responsible for the phenotype are still unknown . These described loci explain only a minority of phenotypic variation in resistance , suggesting that additional resistance loci remain to be discovered . Not only will such markers represent progress toward the identification of causal resistance genes , they also can provide clues to these genes’ modes of action . That is , even before a functional gene is identified , a linked marker can reveal whether the gene acts dominantly , how it interacts epistatically with other genes , the magnitude of its effect , and which other genes are linked to it . Strains of mixed geographical origin are ideal for identifying genetic markers with phenotypic effects , because large interpopulation differences will be reflected in the segregating allelic variation , most loci will be polymorphic with intermediate-frequency alleles , and because high linkage disequilibrium ( LD ) maximizes the power to find markers associated with functional polymorphisms . The B . glabrata strain 13-16-R1 has ancestry in both the Caribbean and Brazil [28–30] and thus shows high sequence diversity and high LD across its genome . Although sod1 is linked to resistance in 13-16-R1 [24] , [25] , sod1 explains only 4% of phenotypic variation in this lab population , so other genes are likely to be equally or more important . Expression levels at three additional genes ( aif , infPhox , and prx1 ) are correlated with resistance in this population , but their causal roles remain to be demonstrated [23] . Most previous genetic work on this population has targeted candidate genes like these with putative immunological roles [23] , [24] , [31] , but other genes with no prior expectation of immune function could be equally or more important . Here we perform a genome-wide scan to identify other molecular markers tied to resistance . We then test these alongside multiple candidate genes for a correlation with resistance to S . mansoni strain PR1 in the 13-16-R1 strain of B . glabrata .
We used mice to maintain the schistosome parasites and to produce miracidia for challenge experiments . Infection is through contact with inoculated water and involves minimal discomfort . Infected rodents are euthanized with CO2 prior to showing clinical signs of disease and are dissected to recover parasitic eggs . Animal numbers were held to the minimum required for the research . The Oregon State University Institutional Animal Care and Use Committee , which adheres to Public Health Service Domestic Assurance for humane care and use of laboratory animals ( PHS Animal Welfare Assurance Number A3229-01 ) , approved this research as Animal Care and Use Proposal 4360 . From 52 inbred lines of B . glabrata 13-16-R1 ( described in [23] ) , we examined 9 lines with low ( <25% ) susceptibility ( “resistant” ) and 10 lines with high ( >85% ) susceptibility ( “susceptible” ) ( Table 1 ) . The identities of these lines were largely , but not perfectly , concordant with the previously designated S and R lines in Larson et al . [23] . We genotyped a randomly chosen snail from each of these lines using RAD-Seq [32] with SbfI on the Illumina HiSeq 2000 at Oregon State University . We mapped reads to the B . glabrata reference genome version BglaB1 [33] with BWA [34] and converted genotypes to vcf format with SamTools [35] . We considered only codominant single-nucleotide polymorphisms ( SNPs ) ( i . e . both alleles seen rather than presence/absence markers defined by null alleles ) . For each SNP , we counted the observations of the minor ( less frequent ) allele ( heterozygous lines counted as 1 , lines homozygous for the minor allele counted as 2; “minor allele count” = MAC ) , and we calculated the difference in this MAC between the resistant and susceptible lines . This framework is analogous to an FST outlier approach [3] , [36] , but because of the small sample sizes and high homozygosity of the inbred lines , we found MAC differences to be more straightforward to interpret than FST . We included only SNPs with a MAC in the full dataset of at least 4 , since lower MAC values would provide negligible statistical power to detect a meaningful difference between resistant and susceptible lines . We also excluded all SNPs with missing genotypes . For each “RAD site” ( 200bp region surrounding a RAD cut site , only retained for analysis if possessing a SNP ) , we identified the SNP showing the largest difference in MAC , and then we compared these SNPs among all RAD sites to find the RAD sites with the largest MAC differences . Scaffolds with RAD sites showing the largest differences in MAC were considered to be candidate resistance markers and examined further . Specifically , we designed PCR primers for two such candidate resistance markers ( S1 Table ) . We Sanger sequenced one of them in representative snails from 51 of the inbred lines , and we genotyped both of them in an independent phenotype-genotype association test ( described below ) . For all pairs of RAD sites , we calculated linkage disequilibrium ( LD ) as the correlation coefficient , r , and assessed the extent of high-LD regions by examining pairs with high ( r > = 0 . 75 ) or perfect ( r = 1 ) LD . LD blocks of interest were visualized with Haploview [37] . We also chose eight candidate loci to test for an association with resistance phenotype in outbred snails ( S1 and S2 Tables ) . Although other genes in B . glabrata have also been tied to immune function , this set of eight represented all genes previously found to show immune relevance in this particular 13-16-R1 lab population , as well as all known coding genes at which allelic variation correlates with S . mansoni resistance in any B . glabrata population . There are four sod1 alleles ( A , B , C , and D ) segregating in this population [26] , but because B is the allele correlated with resistance , for most analyses we grouped A , C , and D together as “nB” ( not B ) . For clarity , we assigned each allele at each locus a distinct letter name without repeating letters among the loci examined in this study ( Table 2 ) . The loci aif , infPhox , and prx1 were described in Larson et al . [23] , but we designed new primers to target these loci ( S1 Table ) . To represent the GRC [3] , we used primers targeting Scaffold1_1732kb ( B . glabrata reference genome BglaB1; S1 Table ) , here designating this locus grc . We designed primers to target the gene encoding biomphalysin ( here designated bmplys ) , which is on the same scaffold as sod1 ( S1 Table ) . This locus is interesting because biomphalysin was recently shown to be a schistosome-killing protein [38] , and it is located only ~500 Kb from sod1 ( B . glabrata reference genome BglaB1 ) , close enough for substantial LD . Thus , it was of interest to ask if the association with resistance was higher for alleles at bmplys than at sod1 . We also included the two scaffolds identified in the inbred line RAD-Seq described above ( “RADres1” and “RADres2” ) . Genotypes at all loci were determined with Sanger sequencing or high resolution melt , except for RADres2 , at which we observed PCR products of three different sizes ( alleles G , H , and I ) , and confirmed by sequencing of homozygotes that they differ from each other by ≥ 118bp due to copy number variation . We were consequently able to genotype samples at RADres2 by visual inspection of agarose gels after electrophoresis , without sequencing . We arbitrarily chose 456 outbred 4mm juvenile snails from the 13-16-R1 population , challenged them each with five S . mansoni miracidia ( strain PR1 ) , and classified them as infected or not , following methods described in Bonner et al [25] . Using DNA isolated from these phenotyped outbred snails , we amplified the eight candidate loci with PCR and genotyped them with Sanger sequencing , high resolution melt , and/or agarose gel electrophoresis . We tested for LD among all pairs of loci as described above , applying a Bonferroni correction of 300 ( number of pairwise comparisons for 25 distinct alleles ) . We tested for a correlation with resistance using logistic regression ( generalized linear model with a binomial response in R ) , as is standard for infection genetics ( e . g . [39] ) , applying a Bonferroni correction of 25 ( number of distinct alleles tested ) . We treated infection as a binary response variable , and modeled genotype as either additive ( 0 , 1 , or 2 alleles ) or dominant ( 0 or 1 ) . Thus , analyses were performed on the odds of infection , rather than the probability [40] , although we also report and display probabilities for ease of interpretation . We ran simple logistic regression for all alleles at all eight loci ( “single-allele tests” ) , and we ran multiple logistic regression with two independent variables for all pairs of alleles ( “two-allele tests” , either for alleles at the same locus or across loci ) . We estimated the impact on odds of infection for each allele as the antilog of the β coefficient . We measured the proportion of variation explained ( R2 ) as ( Null deviance-Residual deviance ) / ( Null deviance ) . In order to test for a dominance effect , we calculated Clopper-Pearson binomial 95% confidence intervals [41] for the proportion of infected snails of each genotype , and tested whether estimates for heterozygotes and homozygotes overlapped . To test for partial dominance , we calculated the expected heterozygous effect for each locus , under additivity , as the mean between the two homozygous genotypes , based on log of odds , and tested whether this fell within the 95% confidence interval for heterozygotes . We tested for epistatic interaction by adding an interaction term to multiple logistic regression models . We further explored epistatic interaction by testing the effect of an allele at one locus in subsets of individuals with fixed genotypes at another , putatively interacting , locus . Illumina data have been deposited at NCBI SRA , Bioproject Accession PRJNA270097 .
We observed 4304 RAD-Seq SNPs , of which 692 had a MAC under 4 and were discarded , leaving 3612 SNPs on 1611 informative RAD sites on 1259 scaffolds totaling 209Mb . These represented fewer than 1% of the scaffolds in the reference genome assembly , but because of the bias toward large scaffolds , they accounted for 23% of the genome sequence . The largest observed difference in MAC was 13 , seen at 11 RAD sites ( top 1% ) , all on different scaffolds ( Fig 1 ) . Of these , 10 had SNPs showing identical segregation patterns , with the minor allele absent in all 9 resistant lines , fixed in 6 of the 10 susceptible lines , and heterozygous in a 7th susceptible line . In other words , these 10 RAD sites showed mutually perfect LD ( r = 1; p < 10−5 ) and seemed to comprise a large haplotype block , here designated the RADres genomic region ( Table 3 and S1 Fig ) . The combined size of all ten scaffolds in the RADres region is 2 . 4Mb . The one remaining RAD site with a MAC difference of 13 , on Scaffold7510 , was in high but not perfect LD with RADres ( r = 0 . 79 , p < 10−4 ) , with the minor allele homozygous in two additional lines , one resistant and one susceptible . We developed primers to amplify one of the RADres sites , on Scaffold115_333kb ( “RADres1” , Tables 2 and 3 ) , and we obtained genotypes from 51 inbred lines . We observed two alleles at RADres1 , defined by a single C/T SNP at position 333090 ( allele E = T; allele F = C; S2 Table ) . Allele E was associated with resistance and allele F with susceptibility , with mean susceptibility in EE lines ( 38% ) half that of FF lines ( 76% ) ( t-test , p < 0 . 001; Fig 2 ) . Among 19 resistant lines ( <25% susceptibility ) , 18 were EE homozygotes and only one line with borderline susceptibility ( 22% ) was an FF homozygote . All 15 highly resistant lines ( < 20% susceptibility ) were EE homozygotes . In contrast , among the 31 homozygous lines with > 25% susceptibility , 14 were EE and 17 were FF ( Fisher’s exact test , p < 0 . 001 ) . After excluding the 19 resistant lines , there was no relationship between genotype and phenotype among the 31 homozygous lines with > 25% susceptibility ( mean EE susceptibility = 77%; mean FF susceptibility = 79%; t-test , p > 0 . 1 ) . In addition , we designed PCR primers to amplify a portion of another RADres scaffold , Scaffold332_88kb ( “RADres2” ) , within an intron of a putative glycosyltransferase gene . This gene was of interest because glycosylation could be involved in self vs . non-self recognition . Therefore , we wanted to test whether RADres2 showed a higher association with resistance than our original RAD marker , RADres1 . We did not genotype the inbred lines at RADres2 , but we used it in our assessment of outbred snails ( see below ) . The sod1 allele that correlates positively with resistance , allele B [24] , had a MAC of 11 and a MAC difference of 5 , so although it was more prevalent in resistant lines , as expected , it was not an outlier when compared to all RAD sites as was RADres ( Fig 1 ) . We observed 80 RAD SNPs at 46 RAD sites on 34 scaffolds showing perfect LD with the sod1 B allele , including a RAD site at Scaffold10_584kb , very near the sod1 position at Scaffold10_612-616kb . The total combined length of these scaffolds was 10 . 8Mb . Thus , sod1 also resides in a large haplotype block , probably even larger than RADres ( S2 Fig ) . We observed relatively high genome-wide LD in 13-16-R1 . Among the 340 RAD site pairs that were on the same scaffold and at least 0 . 1 Mb apart , 60% showed r > 0 . 75 , and 42% showed perfect LD ( r = 1 ) for at least one SNP . Among all RAD site pairs , including those on separate scaffolds , there were 7757 pairs showing perfect LD for at least one SNP , equivalent to each RAD site occurring in a cluster of 10 mutually identical RAD sites on average . There were 33 haplotype blocks that each contained at least 10 distinct RAD sites showing perfect mutual LD for at least one SNP , encompassing 32% of RAD sites ( Fig 3 ) . The single largest haplotype block was the sod1 block described above with 46 RAD sites , and the second-largest block had 34 RAD sites . Thus , multi-scaffold haplotype blocks of high LD extending hundreds of kilobases or more are common in this laboratory population , and the extent of LD observed at RADres is not atypical , although the extent of LD observed at sod1 is . Of the 456 challenged outbred snails , we obtained phenotype and genotype data from 439 ( S3 Table ) , with the remaining failures owing to snail death prior to phenotyping or insufficient extraction of DNA . In all 439 snails we obtained complete genotypes for six loci: sod1 , RADres1 , RADres2 , aif , infPhox , and prx1 . We also obtained genotypes for the majority of snails for two additional loci: grc ( N = 278 ) , and bmplys ( N = 405 ) . All loci were polymorphic with 2–4 alleles , and all had intermediate frequency alleles ( 25–75% frequency ) , thus conveying high power to detect both correlations with resistance and linkage disequilibrium among loci ( Table 2 ) . All loci were found to be in Hardy-Weinberg equilibrium ( p > 0 . 05 ) except for aif , which showed a slight deficit of heterozygous genotypes ( observe 216 , expect 253; p < 0 . 0001 ) , which could be explained by a rare null allele ( ~6% frequency ) that by chance was never observed as a homozygote . We observed significant LD between the two pairs of loci for which it was expected ( S4 Table ) . The loci sod1 and bmplys are separated by a physical distance of <500kb on the same scaffold . They are in high but imperfect LD , with the highest LD observed between the sod1 B allele and bmplys ( r = 0 . 91 , p < 10−15 ) . There were two bmplys alleles ( J and K ) , with allele K at 37% frequency showing a positive correlation with the resistance allele B at sod1 , which occurred at a similar frequency ( 33% ) . Almost all ( 99% ) sod1 B alleles co-occurred with bmplys K , and the remaining bmplys K alleles most commonly occurred with sod1 A . Similarly , all three RADres2 alleles showed significant high LD with RADres1 , and allele G showed the highest LD with RADres1 ( r = 0 . 71 , p < 10−15 ) . Allele G at RADres2 occurred at 71% frequency and was positively correlated with resistance allele E at RADres1 , which occurred at a similar frequency ( 68% ) . The only other pairwise combination between alleles at different loci showing significant LD was between sod1 D and infPhox S , which was marginally significant after Bonferroni correction ( p = 0 . 04; S4 Table ) ; because these two rare ( ≤ 2% frequency ) alleles co-occur in only three individuals , and because the more common alleles at these loci do not show LD , we dismissed this result as unlikely to be biologically meaningful . No other pairwise combination between alleles at different loci showed significant LD ( p > 0 . 05 for all ) . We observed 174 infected and 265 non-infected outbred snails ( 40% susceptibility ) . Our logistic regression analyses repeatedly showed the same main result: two genomic regions , ( sod1/bmplys ) and RADres , were significantly correlated with resistance , and no other loci were . We describe these results in detail below . In the single-allele tests , at least one allele of sod1 , bmplys , RADres1 , and RADres2 showed a significant correlation with resistance ( p < 0 . 05 for each; Fig 4 and S5 Table ) . For all alleles correlated with resistance at these four loci , an additive model explained more of the phenotypic variation and had a lower Akaike information criterion ( AIC ) than the dominant model . Comparing within the two pairs of linked loci , sod1 had a non-significantly stronger effect on resistance than bmplys , and RADres1 had a non-significantly stronger effect on resistance than RADres2 ( Table 2 and Fig 4 ) . No allele at any of the remaining loci ( aif , infPhox , prx1 , and grc ) showed a significant association with resistance , whether modeled as dominant or additive ( p > 0 . 1 for each; Fig 4 ) . Thus , we found two independent genomic regions correlated with resistance in 13-16-R1 , and in both regions the allele most strongly correlated with resistance ( sod1 B and RADres1 E ) acts additively , reducing the proportion of infected snails by approximately 16% with each additional copy ( Fig 5 ) . Our results from the single-allele tests were mirrored in the two-allele tests , in that only alleles at sod1 , bmplys , RADres1 , and RADres2 ever showed a significant correlation with resistance ( p < 0 . 05 ) ( Tables 2 and S6 ) . For the two haplotype blocks with multiple loci and/or alleles ( RADres1 and the three alleles of RADres2; bmplys and the four alleles of sod1 ) , we never observed a separate significant effect for a second allele after including the best allele for that block ( sod1 B or RADres1 E ) . Although there is a trend that sod1 D conveys resistance ( Fig 4 ) , it has no significant effect either alone or in a model that also includes sod1 B . The single best fitting model included sod1 B and RADres1 E , which were also the two alleles with the strongest individual effects on resistance in their haplotype blocks ( Fig 4 ) . A simple additive model , in which alleles B and E contribute to resistance with no dominance and no epistasis , fits the data well ( S6 Table and Fig 6 ) , with both terms significant ( p < 0 . 01 for both ) . This additive model explains 7% of the population variance in resistance , with each sod1 B allele decreasing the odds of infection by 2 . 2 ( 95% CI: 1 . 58–2 . 99 ) , and each RADres1 E allele decreasing the odds of infection by 1 . 8 ( 95% CI: 1 . 32–2 . 43 ) . Thus , the odds of infection for a snail with genotype BB/EE ( predict 13% infected , odds = 0 . 15 ) are 16-fold lower than for a snail without a resistance allele at either locus ( predict 70% infected , odds = 2 . 33 ) . The genotype combinations with the poorest fit to this model ( green squares in Fig 6 ) are BB/FF ( predict 33% infected , observe 75% infected , N = 4 ) and BB/EF ( predict 22% infected , observe 13% infected , N = 24 ) . However , predicted values for all genotype combinations fall into the 95% Clopper-Pearson confidence intervals . We tested for non-additive effects with more complex models , beginning with a test for dominance . If there were complete dominance , we would expect heterozygotes to show an identical impact on resistance as one of the homozygous genotypes . If there were partial dominance , the effects of heterozygotes might be intermediate between both homozygous genotypes , but more similar to one homozygous genotype than the other . For both RADres1 and sod1 , all three genotypes are significantly different from each other with respect to proportion of infected snails ( Clopper-Pearson 95% confidence intervals do not encompass the proportions of other genotypes; Fig 5 ) . Specifically , heterozygotes are intermediate in resistance; they are significantly more resistant than susceptibility allele homozygotes ( FF or nBnB ) and significantly less resistant than resistance allele homozygotes ( EE or BB ) . Thus , for both loci we can reject the hypothesis of complete dominance . Observed heterozygous effects were not significantly different than the mean between the homozygous effects ( encompassed in Clopper-Pearson 95% confidence intervals ) , indicating no evidence for partial dominance . Thus , we observe purely additive effects at both loci , rather than complete or partial dominance . We then tested for epistatic interactions between loci . In a model with an additive effect of both RADres1 and sod1 , plus an interaction term , the interaction term was not significant ( p > 0 . 1 ) . To further explore the possibility of an interaction , we tested for an additive effect of each locus in the subset of individuals with each possible genotype at the other locus ( Fig 6 ) . The estimated reductions of odds of resistance of the sod1 B allele in specified RADres1 genetic backgrounds were as follows: FF background 1 . 0-fold ( 95% CI: 0 . 39–2 . 77 ) , EF background 2 . 5-fold ( 95% CI: 1 . 58–4 . 10 ) , EE background 2 . 2-fold ( 95% CI: 1 . 35–3 . 66 ) , E- ( EE or EF ) background 2 . 4-fold ( 95% CI: 1 . 68–3 . 33 ) . Thus , there is no significant effect of sod1 in a FF background , but this is not significantly lower than the effect of sod1 in an E- background ( t-test , p > 0 . 1 ) . The estimated reductions of odds of resistance of the RADres1 E allele in a specified sod1 genetic background were as follows: nBnB background 1 . 6-fold ( 95% CI: 1 . 06–2 . 53 ) , BnB background 1 . 8-fold ( 95% CI: 1 . 12–2 . 78 ) , BB background 4 . 4-fold ( 95% CI: 1 . 06–17 . 99 ) , B- ( BB or BnB ) background 1 . 9-fold ( 95% CI: 1 . 23–2 . 92 ) . Thus , RADres1 has a significant effect in all sod1 genetic backgrounds , with an effect in the BB background estimated to be higher , but not significantly so , than in the other genetic backgrounds . In summary , our data are consistent with an additive model ( Fig 6 ) , with no significant evidence for epistatic effects , although we may have low power to detect certain epistatic effects , especially those driven by BB/FF dual homozygotes of which we observed only four individuals .
We have identified a genomic region in B . glabrata , RADres , at which each copy of a resistance allele cuts the odds of schistosome infection in half . The impact of this locus is of similar magnitude to the only other known resistance locus in the 13-16-R1 snail strain , sod1 , which also conveys an approximately 2-fold effect with each copy of a resistance allele . Thus , combined homozygosity at both loci confers a 16-fold change in the odds of infection ( from approximately 2:1 odds to approximately 1:8 odds ) . Other candidate genes show no association with resistance , including the GRC [3] and three loci with constitutive expression patterns correlated with resistance in this population [23] . The RADres genomic region has not been previously implicated as relevant to snail immunity . Because the RADres region extends over several megabases , and likely includes many scaffolds that were not represented in our RAD-Seq data , the identities of candidate causal genes remain speculative . RADres1 occurs on a scaffold that includes putative genes inferred to encode a methyltransferase , an ATP-dependent zinc metalloprotease , and an F-box/LRR-repeat protein . RADres2 occurs in a putative glycosyltransferase gene , which could have an immune function due to the importance of glycoproteins in snail-trematode interactions [42] . The same scaffold also includes putative genes encoding an autophagy-related protein , a serine carboxypeptidase , and a single-pass transmembrane protein with no sequence similarity to other known proteins . One of the most studied immunity gene families in B . glabrata , the FREPs [43] , does not occur among the known RADres scaffolds . Even without identifying the causal gene , we can make inferences about its probable function from the fact that RADres apparently acts additively . As a caveat , we acknowledge that the causal locus could act dominantly , with our observation of additivity being due to imperfect linkage disequilibrium between it and our markers ( for example , RADres1 EE homozygotes could be more resistant merely because they have two chances to share a haplotype with a rare dominant causal allele that occurs with only some E alleles ) . However , both our observation of high genome-wide LD and the admixed history of this laboratory population make this scenario unlikely . Thus , RADres seems to have a quantitative rather than qualitative effect , as resistance scales with the amount of its protein product , with homozygotes being more resistant than heterozygotes . RADres therefore represents a potentially rate-limiting step in the immune response , rather than a step with full functionality at low concentrations such as a recognition molecule that launches a signaling cascade after a single match to its target . If RADres encodes an immune effector like sod1 does , the success of an immune response may depend on the quantity of this protein . Alternatively , the probability that an immune response is mounted at all may scale with the concentration of a signaling molecule produced by RADres , given the importance of signaling in snail defense [44] . In addition , two lines of evidence suggest that RADres contains a fundamental component of the immune system upon which other immune components depend . First , all inbred lines showing high resistance ( < 20% susceptibility ) are homozygous for the RADres resistance allele . Second , although there is no significant effect of RADres1 genetic background on the effect of sod1 , it is striking that the sod1 B allele is not correlated with resistance among RADres1 FF homozygotes ( Fig 6 ) . Because we observed only 4 BB/FF dual homozygotes , we have little power to test the role of sod1 in this genetic background . However , the trend is that RADres1 FF homozygotes are highly susceptible regardless of sod1 genotype , suggesting that allele RADres1 E may be required for sod1 to function as an immunity gene . Thus , our data are consistent with the hypothesis that this population includes highly resistant genotypes ( i . e . would nearly always resist infection with PR1 strain S . mansoni after indefinite trials ) of which RADres1 E is a necessary , though not sufficient , component . The sod1 locus was first identified as a candidate gene for S . mansoni resistance due to its crucial role in the oxidative burst [45] . It was subsequently shown that sod1 genotypes correlate with resistance in 13-16-R1 [24] and that the resistance-associated B allele shows higher constitutive expression than the other alleles [46] . In this study we found a 2-fold effect of the B allele , the same magnitude reported by Goodall et al . [24] . Goodall et al . [24] also reported that the C allele is correlated with susceptibility , but they did not tease apart whether C is significantly different than A , or whether the link between C and susceptibility is simply the converse of the B/nB difference . Here , we did not detect an effect of C independent of B , suggesting that C and A are functionally equivalent . Intriguingly , our point estimate of infection odds reduction by sod1 D , after accounting for sod1 B and RADres1 E , was quite high ( 3 . 5 ) but not significant ( uncorrected p = 0 . 06 ) . Our power to assess sod1 D was limited as this allele was rare ( 2% ) and never observed to be homozygous , but it is possible that this allele conveys a resistance impact even greater than sod1 B or RADres1 E . Because sod1 was chosen as an a priori candidate , our working hypothesis is that this gene itself is the causal gene , not merely a linked marker for an unknown causal gene . However , there are several caveats to the putative causality of sod1 . First , sod1 resides on the largest high-LD haplotype block in this population , showing perfect LD with markers on many other scaffolds in the 19 inbred lines , suggesting that the region of association with resistance likely extends across many megabases encompassing numerous genes . Second , sod1 is linked to at least two other genes ( cat and prx4 ) that also contribute to the oxidative burst , both of which are in LD with sod1 in the 13-16-R1 population [26] . These genes reside on contigs that were not represented by RAD-Seq sites , underscoring the fact that our RAD-Seq data likely underestimate the true size of the sod1 haplotype block . Third , other potentially immune-relevant genes occur on contigs in this haplotype block , notably the bmplys gene encoding biomphalysin , a β pore-forming toxin which can directly kill S . mansoni sporocysts [38] . We have shown that bmplys is in high LD with sod1 and both are therefore strongly correlated with resistance . If there is a genomic cluster of immunity genes , sod1 is not necessarily the one harboring the functional polymorphism . On the other hand , the correlation with resistance is slightly , though not significantly , lower at bmplys than at sod1 , suggesting that sod1 may be closer to the causal center of this region of association . The fact that sod1 was not an MAC difference outlier ( Fig 1 ) underscores the limits of our statistical power with only 19 inbred lines and suggests that other loci of similar effect on host resistance may remain undetected . It is also possible that the effect of sod1 is relatively weaker in highly inbred genetic backgrounds because non-additive genetic variance can play a larger role . Although expression levels of aif , infPhox , and prx1 are all positively correlated with resistance in this population [23] , segregating polymorphisms at these loci show no phenotypic association . These three genes were initially targeted because of their potential immunological role . Specifically , aif encodes allograft inflammatory factor , associated with inflammatory response and activation of hemocytes [23] . Likewise , infPhox , encoding a putative phagocytic oxidase subunit of NADPH oxidase , and prx1 , encoding peroxiredoxin , both likely affect the concentration of parasite-killing reactive oxygen species [23] . Our results do not invalidate the potential functional importance of the variation in expression at these loci , but show that it must be controlled in a trans , rather than cis , manner . That is , these genes are upregulated in response to a signal that ultimately depends on genetic variation elsewhere in the genome . For example , resistant individuals may have a transcription factor allele that binds more strongly to the motifs at aif , infPhox , and prx1 . It is tempting to speculate that RADres in fact contains such a trans-acting variant . The strong additive effect of the E allele at RADres1 could be due to its influence on expression of a large number of immune-relevant genes . Unfortunately , for the small number of inbred lines in which gene expression was assessed , we do not have the statistical power to dissect an association with RADres from the correlated association with resistance . Furthermore , the majority of phenotypic variation in resistance remains unexplained by the combination of RADres and sod1 , so it is likely that other loci also play a role . A promising future direction will be to test for an effect of RADres genotype on genome-wide gene expression patterns . The GRC genomic region is strongly correlated with S . mansoni resistance in B . glabrata from Guadeloupe [3] . Patterns of polymorphism at several genes in this genomic region in the Guadeloupean snails suggest long-term balancing selection , which would predict that functionally divergent alleles occur through the species range . However , we did not detect a correlation between GRC genotype and resistance in 13-16-R1 . This negative result may be due in part to the different parasite strain used in this study ( PR1 ) versus in Tennessen et al . [3] ( Guadeloupian S . mansoni ) , since strain-by-strain ( G x G ) interactions are known to be highly variable in this host-parasite system [47–51] . In addition , GRC diversity may have been lost in the narrow laboratory bottleneck during the founding of 13-16-R1 . That is , 13-16-R1 may have retained only functionally similar GRC alleles by chance , even if all founding populations harbored functional genetic diversity . Intriguingly , no locus in mixed-origin 13-16-R1 conveys as strong an effect on resistance as the GRC in the natural Guadeloupe population ( 8-fold impact on odds of infection , explains 23% of variation in trait , [3] ) . As snails likely coevolve with trematodes , it is plausible that many variants affecting immunity will become fixed as populations diverge , resulting in many resistance loci in mixed-origin populations like 13-16-R1 , each of which determines a small fraction of phenotypic variation . In contrast , fewer loci will be segregating within a single population in the wild , and thus those that do harbor functional variants , like the GRC in Guadeloupe , will explain a large proportion of the phenotypic variation . This research represents a step forward toward identifying resistance genes and immune mechanisms in snails that could be targeted in the control of global schistosomiasis . Although no one resistance allele is likely to be a “silver bullet” that could unilaterally block all schistosome infection [52] , the host-parasite molecular interactions represented by these resistance loci may be exploited in combination with other strategies for disease control . We have identified in B . glabrata a new genomic region , RADres , with a strong influence on resistance to schistosomes . Genes in this region may be suitable for manipulation of immunity in wild snails , but further work is needed to fine-map the causal gene ( s ) and to determine their other possible phenotypic effects and their immune roles under different genetic backgrounds , parasite strains , or environmental conditions . We have also further characterized the genomic region harboring sod1 , notably showing that linkage disequilibrium is particularly high in this region . Therefore , the causal gene could be any of the other linked genes nearby , not necessarily sod1 itself , although because of its immunological role and expression patterns that vary by genotype [45] , [46] , sod1 is still the best candidate in this genomic region . Finally , we have shown that several candidate genes known to be correlated with resistance either in other populations ( GRC ) or via expression patterns in this population ( aif , infPhox , and prx1 ) do not contain genetic variants linked to resistance under the conditions of this study . These other loci could still be useful targets in manipulating the immunity of wild snails , but the RADres region may encode a candidate of similar or greater importance for generating the desirable phenotypic outcome of heightened resistance .
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Aquatic snails transmit schistosome blood flukes , causing a parasitic disease second only to malaria in its global health impact . The mechanisms by which some snails naturally resist infection are poorly understood , but if characterized could enable protocols to interfere with transmission of the disease . Here we identify a region of the snail genome that correlates with resistance to infection , and we examine its effect on immunity jointly with a previously described resistance gene . Variation at other candidate genes has no effect on parasite resistance in this laboratory population . The two resistance regions could serve as targets to block parasite transmission via snails .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Genome-Wide Scan and Test of Candidate Genes in the Snail Biomphalaria glabrata Reveal New Locus Influencing Resistance to Schistosoma mansoni
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Fungal pathologies are seen in immunocompromised and healthy humans . C-type lectins expressed on immature dendritic cells ( DC ) recognize fungi . We report a novel dorsal pseudopodial protrusion , the “fungipod” , formed by DC after contact with yeast cell walls . These structures have a convoluted cell-proximal end and a smooth distal end . They persist for hours , exhibit noticeable growth and total 13 . 7±5 . 6 µm long and 1 . 8±0 . 67 µm wide at the contact . Fungipods contain clathrin and an actin core surrounded by a sheath of cortactin . The actin cytoskeleton , but not microtubules , is required for fungipod integrity and growth . An apparent rearward flow ( 225±55 nm/second ) exists from the zymosan contact site into the distal fungipod . The phagocytic receptor Dectin-1 is not required for fungipod formation , but CD206 ( Mannose Receptor ) is the generative receptor for these protrusions . The human pathogen Candida parapsilosis induces DC fungipod formation strongly , but the response is species specific since the related fungal pathogens Candida tropicalis and Candida albicans induce very few and no fungipods , respectively . Our findings show that fungipods are dynamic actin-driven cellular structures involved in fungal recognition by DC . They may promote yeast particle phagocytosis by DC and are a specific response to large ( i . e . , 5 µm ) particulate ligands . Our work also highlights the importance of this novel protrusive structure to innate immune recognition of medically significant Candida yeasts in a species specific fashion .
The innate immune response against fungal pathogens is effected by macrophages , neutrophils and dendritic cells ( DC ) . DC may encounter opportunistic fungi , such as Candida species , in cutaneous and mucosal tissue as well as in disseminated infections associated with serious disease . The innate immune response relies on two classes of pattern recognition receptors , the Toll-like receptors ( TLR ) and C-type lectin receptors ( CLR ) that permit the apprehension of non-self ligands . Infection by Candida species yeasts represents an important opportunistic infectious disease threat that must be constantly countered by the innate immune system . Candida is ubiquitous and exists as a normal commensal microbe on human mucosal and cutaneous surfaces . However , Candida is also an opportunistic human fungal pathogen often infecting intensive care , post-surgical and neutropenic patients . The immunocompromised population has grown in modern times with the spread of AIDS and more widespread use of immunosuppressive therapies in groups like solid organ transplant recipients . These patients are generally at risk for fungal infection . In the United States , C . albicans accounts for the majority ( 70–80% ) of clinical fungal isolates; however , other species ( C . glabrata , C . tropicalis , C . parapsilosis and C . krusei ) are emerging fungal pathogens with C . glabrata and C . tropicalis accounting for 5–8% of clinical isolates [1] , [2] . In Europe , Asia , and South America , the incidence of C . parapsilosis infection can exceed that of C . albicans , and C . parapsilosis is the most rapidly emerging Candida species since 1990 [3] . The fungal cell wall contains mannan , β-glucans , chitin and other carbohydrates recognizable as non-self epitopes [4] , [5] . These cell wall carbohydrates are ligands for CLRs . Dectin-1 , DC-SIGN and CD206 ( Mannose Receptor ) are transmembrane CLRs expressed on immature DC that contribute to recognition of yeasts [6] . Dectin-1 binds β- ( 1 , 3 ) -glucans and contains a cytoplasmic ITAM motif allowing Syk-dependent activation of phagocytosis and cytokine production . Plasma membrane microdomains of DC-SIGN in immature DC contribute to avid binding of high mannose carbohydrates such as mannan [7] , [8] . CD206 binds terminal mannose , fucose and N-acetyl glucosamine residues conferring binding activity for mannan and chitin in the yeast cell wall [9] , [10] , [11] . CD206 participates in fungal acquisition , cytokine elaboration and phagocytosis of yeast [12] , [13] , [14] . Internalization is central to many aspects of CD206 biology . Indeed , the majority of CD206 is found within the endocytic system , and internalization of antigen via CD206 in DC results in MHC class II antigen presentation for T cell activation [15] . Furthermore , CD206 also has important homeostatic functions in endocytically removing hydrolases , tissue plasminogen activator and myeloperoxidase during inflammatory responses [9] , [16] , [17] . The cytoplasmic tail contains a dihydrophobic motif involved in endosomal sorting and a tyrosine-based FxNxxY internalization motif similar to that found in the cytoplasmic tail of LDL receptor [18] . The latter motif supports attachment of CD206 to clathrin lattices via AP-2 leading to internalization within clathrin-coated vesicles ( CCV ) . Dendritic actin network polymerization via Arp2/3 is a recurrent theme in host cell-microbe interactions . Arp2/3 complex binds to existing F-actin and upon activation nucleates new filament growth from this branch point [19] , [20] . DC express the Arp2/3 activators , WASP and cortactin . Cortactin-mediated actin remodeling is co-opted by numerous microbial pathogens [21] . Cortactin stabilizes Arp2/3 branch points driving dynamic actin structures such as lammelipodial protrusions [22] , actin pedestals [23] , and actin comet tails [24] . Serine and tyrosine phosphorylation as well as physical recruitment variously determine the location and timing of cortactin activity [25] , [26] , [27] , [28] . For example , dynamin recruits cortactin to clathrin-coated pits during vesicle scission [29] , [30] , [31] leading to actin polymerization around the nascent vesicle neck . Activation of the actin remodeling machinery is a normal part of the professional phagocyte's response to microbes ( although it is sometimes co-opted by pathogens ) . This machinery must be directed by pattern recognition receptors to recognize invading microbes . In this report we describe a novel actin-based protrusive structure formed by DC in response to ligation of CD206 by yeast cell walls .
We found that human monocyte-derived DC generated peculiar dorsal pseudopodial structures after several hours of exposure to zymosan . We designated these protrusions “fungipods” . They were visible in DIC imaging , resembling long tethers connecting DC and zymosan , and were comprised of an apparently smooth , well-ordered distal region tapering into a convoluted cell-proximal region . Distal fungipods averaged 7 . 4±3 . 3 µm long ( N = 35 , range: 2 . 7–16 . 8 µm ) and 1 . 8±0 . 67 µm wide ( range: 0 . 92–4 . 6 µm ) at the contact site . Their overall length was 13 . 7±5 . 6 µm ( range: 5 . 9–25 . 3 µm ) ( Figure 1A , B ) . Most distal fungipods appeared roughly cylindrical in SEM ( Figure 1C ) , but we occasionally observed ribbon-like fungipods with longitudinal ridges ( Figure 1D ) . This variety suggested that the cross-sectional shape of the distal fungipod was determined by the geometry of the contact site on the zymosan particle . SEM imaging and DiI labeling of contact site membranes revealed a contact site structure limited to the footprint of the fungipod with no fungipod membrane extended outside of the fungipod contact site footprint visible in DIC ( Figure 1E and data not shown ) . The fungipod plasma membrane was tightly apposed to zymosan , and electron dense juxtamembrane patches were observed by TEM in the fungipod plasma membrane present at the zymosan contact site ( Figure 1F , G ) . Interestingly , these membrane densities were often associated with membrane pits and displayed knobbed or studded juxtamembrane densities . In Figure 1F , the zymosan is contacted by two fungipods , and the lower one is detailed to emphasize these juxtamembrane densities . Finally , we found that zymosan-treated human monocyte derived immature macrophages produce fungipods similar to those on DC ( Figure S1A ) . Activation of human immature DC or macrophages with LPS significantly attenuates fungipod formation efficiency , although fungipods are not completely abolished on LPS-activated cells ( Figure S1B ) . Fungipodial structures required association of zymosan particles with the plasma membrane . Blocking with excess soluble mannan plus the β-glucan laminarin completely inhibits interaction of zymosan with CLR on DC [14] , and this prevented binding and fungipod formation ( data not shown ) . Time-lapse DIC imaging revealed that the initial fungipod extension was typically observable from ∼0 . 5–2 hours post-attachment and protrusions were only observed to form next to and bind to zymosan after prolonged association with DC ( Figure 1H; Video S1 ) . The fungipods in this example grew from a plane below the focus . We have not observed fungipods to form independent of surface associated zymosan . This demonstrates that the fungipodial structure is a response to a previously ligated particle and is not a pre-formed protrusion searching for a ligand . Early zymosan-induced fungipodial extensions were often thin and lacked a well-ordered distal fungipod , but they matured into the previously described fungipods over the course of approximately 45–60 minutes ( Figure 1I; Video S2 ) . These mature fungipods exhibited steady growth and movement ( Figure 1J ) . Zymosan was firmly bound by fungipods and consistently remained stably associated over hours ( Figure 1K ) . We confirmed that DC form fungipods after exposure to live cultures of budding S . cerevisiae . These protrusions exhibited a morphology identical to those observed for zymosan particles ( Figure 1L ) . Finally , we also considered that fungipod formation might arise only in DC highly stimulated via the acquisition of many zymosan particles . In this dose-dependence model , fungipod formation efficiency would be positively correlated with zymosan dose such that high fungipod formation efficiencies would be predicted at high dose and vice versa ( Figure 2A ) . To address this possibility we treated DC with our standard zymosan particle density ( 20 µg dry weight/ml ) and a one log lower density ( 2 µg/ml ) . We found that the fungipod formation efficiency under these two regimes was statistically indistinguishable by Student's t-test ( p = 0 . 5 ) ( Figure 2B ) . Another prediction of the dose-dependence model is that within an experiment , cells that happen to bind larger numbers of zymosan particles will have higher fungipod formation efficiency . We plotted efficiency versus the number of surface-bound zymosan particles and found that , regardless of the zymosan density applied to the DC culture , individual DC with higher zymosan loads generally had low fungipod formation efficiencies ( Figure 2C ) . We found similar trends if the number of internalized zymosan particles or the total number of cell-associated zymosan particles was plotted on the abscissa . We also found no correlation between the number of cell-associated zymosan particles and the kinetics of fungipod formation ( time from binding to initial fungipod extension ) ( Figure 2D ) . Therefore , there is no positive correlation between degree of global zymosan stimulation and fungipod formation efficiency or fungipod formation kinetics in immature DC . Distal fungipods contained copious F-actin in a structure tapering away from the contact site ( Figure 3A , B; Video S3 ) . The convoluted proximal fungipods typically exhibited weaker F-actin signals ( Figure 3B , arrow ) . Existing fungipods soon collapsed upon addition of the F-actin elongation blocker cytochalasin D leaving disordered membranous extensions ( Figure 3C ) . These data demonstrate that a dense actin structure is necessary to maintain the distal fungipod and suggest that this actin structure is actively assembling/disassembling leading to catastrophe when the drug blocked elongation . We found that the actin nucleation factor cortactin was abundantly localized to the distal fungipod . Interestingly , cortactin was configured in a conical sheath with a core rich in F-actin ( Figure 3D , E ) . The fungipod/zymosan contact site viewed en face revealed a ring of cortactin surrounding an actin core . Since cortactin is associated with the generation of Arp2/3-mediated dendritic actin networks , these data suggest that a dense branched actin network is generated in the distal fungipod . We next examined the involvement of microtubules in fungipods . Immunofluorescence localization of α-tubulin revealed normal microtubular staining in the DC cell body ( Figure 3F ) but a diffuse tubulin distribution throughout the fungipod ( Figure 3G ) . We observed no microtubules in distal or proximal fungipods by immunofluorescence or TEM thin sections ( Figure 1F; data not shown ) . Furthermore , treatment of existing fungipods with the microtubule depolymerizing drug nocodazol had no effect on fungipod structural integrity or growth even after prolonged exposure ( Figure 3H ) . Therefore , we conclude that the cytoskeletal structure of DC fungipods is actin-driven and not dependent upon microtubules . As previously mentioned , elongating fungipods were often apparent in DIC time series . Upon closer examination we noted that a rearward flow of refractile material moving from the contact site toward the cell body was visible in the distal fungipod ( Video S4 ) . Kymographic velocity analysis revealed an apparent rearward flow of 225±55 nm/second ( N = 40 velocity measurements in 8 fungipods; Figure 3A , B ) . We considered that mobile ripples on the membrane could cause a spurious appearance of flow , so we stained DC membranes with DiI and observed distal fungipod membranes for undulations . We saw no undulation of fungipod membranes in movies acquired at ∼7 Hz ( Figure 4C , D ) , and we also note that distal fungipod membranes appear quite smooth by SEM imaging ( Figure 4E ) . We also observed that fungipodial growth is accompanied by rotation about the longitudinal axis of the fungipod . In cases where the attached zymosan particle became detached from the cell surface while still bound at the fungipod contact site , the fungipod particle was observed to rotate or be driven in a circular motion ( Figure 4F; data not shown ) . In more constrained cases this rotation resulted in kinking and even supercoiling of the fungipod ( Figure 4G , H; Video S5 ) . The function of fungipod growth with rotation is not completely clear . We have observed fungipod kinking coincident with visible and repeated displacement of the attached zymosan into the cell membrane ( Figure 4I; Video S6 ) followed by eventual phagocytosis of the particle . One possible interpretation is that fungipod rotation during growth results in a twisted fungipod that upon relaxation applies a force displacing the zymosan forward ( i . e . , toward the DC membrane ) thus promoting particle retention and perhaps aiding in phagocytosis . We tested whether ligation of the phagocytic receptor Dectin-1 by zymosan was involved in the generation of fungipodial protrusions . The β-glucan laminarin binds Dectin-1 and inhibits its interaction with zymosan particles . We found that blocking with excess soluble laminarin did not inhibit the formation of zymosan-induced fungipods or number of zymosans per cell ( Figure 5A; Figure S2A , B ) . Similarly , blocking with anti-Dectin-1 polyclonal antibody did not prevent formation of fungipods ( Figure 5B ) . Anti-Dectin-1 blocking did reduce the amount of internalized zymosan ( Figure S2C ) consistent with its known role in yeast phagocytosis [32] , [33] . Finally , pharmacological inhibition of Syk ( activated by Dectin-1 ) also did not block fungipod formation ( Figure 5C ) . Together these data indicate that Dectin-1 is not required for fungipod generation . Treatment with excess soluble mannan completely inhibited formation of fungipodial protrusions ( Figure S2B ) . Mannan blocking also reduced the number of cell-associated and internalized zymosans per cell ( Figure S2A , C ) , consistent with prior reports [34] . However , extracellular membrane-associated particles were still seen ( Figure 5D ) and examples of internalized zymosan particles ( presumably via Dectin-1 ) further proved that zymosan particles were able to interact with DC membrane proteins under mannan blocking conditions ( Figure 5E ) . Furthermore , mannan blocking greatly reduced fungipod formation efficiency ( Figure S2D ) indicating that inhibition of fungipods by mannan was due to abolition of a mannan-sensitive receptor interaction and not merely by the decrease in binding . This blocking experiment implicated receptors with affinity for high-mannose polysaccharides ( i . e . , DC-SIGN and CD206 ) in the generation of fungipods . We adsorbed mannan to 5 µm polystyrene beads and exposed DC to these beads . We found that bead-immobilized mannan was sufficient to generate fungipods with similar size and structure to those observed after zymosan exposure ( Figure 5F ) . In addition , we found that particles of purified chitin similar in size to zymosan particles ( ∼5 µm ) were also capable of generating fungipods with a normal appearance and an apparent rearward flow visible in DIC ( Figure 5G; Video S7 ) . As a negative control for non-specific effects of particulate binding to DC surfaces , we coated 5 µm beads with bovine serum albumin , chicken ovalbumin or fetal bovine serum proteins and applied these beads to DC . While the beads were able to attach to the DC , no fungipods were induced by these non-specific control beads ( Number of bead-bound cells observed: BSA , N = 56; OVA , N = 57; serum , N = 54 ) . As a further specificity control , we applied Alexafluor-488 labeled E . coli to DC for 4 hours . No fungipods were present at the end of this period and the bacteria were entirely internalized at this point ( data not shown ) . We concluded that the receptor responsible for fungipod generation possesses binding affinity for both mannan and chitin . CD206 has these characteristics whereas DC-SIGN does not bind chitin . Blocking with anti-DC-SIGN polyclonal antibody failed to significantly reduce fungipod numbers or the efficiency of their formation ( Figure S2B , D ) further demonstrating that DC-SIGN/zymosan interaction is not required for fungipod growth . Blocking CD206 with anti-CD206 polyclonal antibody prior to addition of zymosan dramatically inhibited formation of fungipods and the efficiency of their formation ( Figure 5H; Figure S2B , D ) . Anti-CD206 antibodies adsorbed on 5 µm beads induced abundant fungipods with normal morphology ( Figure 5I ) . CD206 crosslinking with soluble secondary antibody did not induce fungipods ( data not shown ) . In summary , when CD206-mediated interactions between the DC and zymosan are blocked by soluble antibody , the fungipod fails to form . Conversely , when an antibody-coated , zymosan-sized bead engages CD206 , a fungipod is generated . Therefore , we conclude that the C-type lectin receptor CD206 is necessary and sufficient for the formation of zymosan-induced fungipods on immature DC . CD206 was greatly enriched at most zymosan/DC contact sites including the contact site on the distal fungipod ( Figure 5J ) . CD206 is known to associate with clathrin via AP-2 interactions with its cytoplasmic tail , and we found that clathrin light chain was abundant throughout the distal fungipod ( Figure 5K ) . Dynamin can be recruited to clathrin patches and contributes to initiating actin reorganization . Therefore , we investigated dynamin localization in zymosan-apposed membranes . While mature fungipods were dim or negative for dynamin staining , we found smaller membrane protrusions juxtaposed to zymosan that stained intensely for dynamin ( Figure 5L ) . We have noted that fungipod/yeast attachments appear quite durable as we do not observe loss of yeast particles over hours of imaging and the contacts are quite tightly apposed . Furthermore , we often saw single zymosan particles contacted by multiple distinct fungipods ( as many as 14 ) ( Figure 6A , B; Video S8 ) . We also observed that zymosan particles could become wrapped within a cage of membrane protrusions formed by lateral interactions of the proximal and distal fungipod walls with the zymosan particle ( Figure 6C; Video S9 ) . Such tethering via multiple attachments is likely to provide extremely stable retention of bound zymosan particles thus improving engulfment and antigen sampling for immature DC . To examine the kinetics and probability distribution of internalization and/or fungipod formation , we have undertaken an extensive quantitative analysis of individual zymosan histories ( N = 301 ) as observed in their interactions with DC during a cumulative ∼980 hours in contact with DC . We quantified the distribution of zymosan in various states ( DC surface bound , fungipod-associated , and internalized ) as well as the kinetics of transition between those states ( summarized schematically in Figure S3A ) . When direct internalization ( without a fungipod ) was observed , it occurred at an average time of 72±58 minutes . When fungipod formation occurred , it was seen at an average time of 78±57 minutes . Internalization of a fungipod-associated zymosan required an average time of 160±111 minutes ( Figure S3B ) . These kinetic measurements were based on the actual time that an individual zymosan particle took to make the indicated transition between states ( i . e . , time from initial binding of a zymosan until a fungipod was first seen associated with that particle ) . The movies used to obtain this kinetic information varied in length but ∼90% were >160 minutes duration ( movie durations in minutes: minimum , 85 . 2; maximum , 975; mean , 368; standard deviation , 210 ) . Zymosan particles frequently remained bound to the DC surface for many hours without fungipod formation or internalization accounting for 63 . 9% of the zymosans observed . Direct internalization was inefficient accounting for only 14 . 5% of bound zymosans . Of the remaining surface bound zymosans , another 21 . 6% made fungipods . Of these fungipod-associated zymosans , 14 . 6% were internalized ( Figure S3C ) . The ∼20% of surface-bound zymosans that formed fungipods were non-productive for direct phagocytosis from the plasma membrane . Coiling phagocytosis has been described in the internalization of Legionella pneumophila [35] and also for fungal particles [36] , [37] . This process is morphologically defined by the presence of monolateral phagocytic membrane engulfment and overlapping phagocytic membrane extensions seen in thin section TEM rather than classical bilateral phagocytic engulfment [38] . We have documented both of these morphologies in TEM thin sections of zymosan attached to DC ( Figure 6D , E ) in addition to apparent bilateral engulfment ( Figure 6F ) . Interestingly , we also find that zymosan particles are frequently associated with smaller wedge-like projections of membrane that contain actin and cortactin ( Figure 6G; Video S10 ) . These wedge structures are consistent with the expected monolateral engulfment in coiling phagocytosis and were observed with or without coincident fungipods . While anti-CD206 coated 5 µm beads do generate fungipods , 1 µm beads coated at the same surface density of anti-CD206 are not capable of generating fungipodial protrusions despite avid binding to the DC membrane ( Figure 6H ) . This result implies that the DC can distinguish between particles with identical composition but different size , and fungipodial protrusions are only generated as part of a response against large particles . We co-cultured DC with log-phase live Candida species yeasts for four hours and measured the percentage of cells producing fungipods in response to surface bound Candida or zymosan ( as a positive control ) ( Figure 7A ) . As previously shown , zymosan exposure evoked a strong fungipod response with 56 . 9% of particle bearing DC responding ( N = 65 ) . C . parapsilosis also triggered strong fungipod formation with 40 . 4% of DC responding ( N = 47 ) ( Figure 7B ) . C . tropicalis binding to DC resulted in fungipod formation only rarely ( 7 . 5% responding , N = 67 ) ( Figure 7C ) . We observed no fungipods on DC bound by C . albicans ( N = 82 ) . Phagocytosed C . albicans persists in the phagosome and forms intracellular pseudohyphae that can grow inside phagocytes leading to their destruction [39] . This raised the possibility that C . albicans might induce fungipods but the interacting DC might be destroyed prior to four hours of co-culture . We similarly failed to observe fungipods in DC/C . albicans co-cultures of one or two hours duration ( data not shown ) , suggesting that DC lysis does not explain the lack of fungipods formed in response to C . albicans . To test whether Candida species yeasts might be actively encouraging or inhibiting fungipod formation ( i . e . , through secreted factors ) , we fixed early log-phase cultures of the three Candida species and applied these yeast particles to DC . These fixed particles were almost entirely composed of yeast-form cells . The identical trend in fungipod production was the same for fixed Candida as for live cells ( Figure 7D ) . Some increased responsiveness in the fixed Candida experiments was observed , but this was a global effect and is likely explained by the use of different monocyte donors in these two sets of experiments . The similarity in trends between live and fixed yeasts suggests that differential fungipod responses to Candida pathogens is due to intrinsic yeast characteristics , not acutely active microbial processes that influence fungipod biogenesis .
The distal fungipod's close contact site with the zymosan particle often contained regions of greater electron density along the apposed membrane visible in TEM thin sections ( Figure 1F , G ) . We also observed these membrane densities on zymosan-containing phagosome walls . Interestingly , the densities displayed a clustered , knobbed pattern projecting outward from the cell . The DC membrane is strongly enriched in CD206 at the zymosan contact site ( Figure 5J ) . Therefore , the membrane densities observed by TEM at this site may represent concentrations of CD206 , perhaps in membrane microdomains as has been observed previously for another transmembrane C-type lectin , DC-SIGN [7] . The cytoplasmic domain of CD206 contains a tyrosine-based internalization motif responsible for mediating endocytosis via clathrin coated pits [18] . Indeed , we have observed pits at the fungipod/zymosan contact and strong clathrin light chain localization in distal fungipods ( Figure 1G; Figure 5K ) . CD206-driven clathrin patches may assemble at zymosan contact sites . Dynamin recruitment to clathrin patches coordinates actin polymerization involved in vesicle scission and propulsion into the cytoplasm [29] , [30] , [31] . Frustrated clathrin-mediated endocytosis may generate a signaling platform as hypothesized below . Dynamin can recruit cortactin to the membrane leading to actin polymerization . In addition to this localization , cortactin activity can be regulated by Src-family kinases ( SFK ) [21] . Neither the SFK inhibitor PP2 nor the broad-spectrum protein tyrosine kinase inhibitor genistein inhibited fungipods suggesting that cortactin tyrosine phosphorylation is dispensable for fungipod biogenesis ( data not shown ) . Cortactin activity does not always require tyrosine phosphorylation ( i . e . , for actin pedestal formation ) [23] . Consistent with the hypothesis that CD206 concentration at zymosan contact sites catalyzes the development of clathrin patches leading to dynamin recruitment , we have observed dynamin enrichment in small membrane protrusions next to zymosan particles . Since dynamin binds cortactin [31] and this promotes actin polymerization , the size and duration of clathrin/dynamin/cortactin complex recruitment might be important in driving the formation of fungipods ( Figure 8A ) . Finally , cortactin's ability to stabilize Arp2/3 branch points [19] and bundle actin filaments [40] , [41] may influence the durability and stiffness of dendritic actin networks in the distal fungipod . We have summarized our data regarding the mechanism of fungipod formation ( Figure S4A ) . Heinsbroek , et al have observed a sequential engagement of C-type lectins by zymosan and C . albicans in murine thioglycollate-elicited macrophages [42] . They reported that Dectin-1 was responsible for immediate binding and internalization of fungal particles while CD206 associated only later with phagosomes leading to MCP-1 and TNF-α production . Our experiments in a different species and cell type have shown that zymosan acquisition by human immature DC is highly dependent on CD206 as binding is blocked by mannan and anti-CD206 ( Figure S2A ) . However , this early role for CD206 in our system does not preclude a later role in signaling from accumulations of CD206 in phagosomes or fungipod-associated membranes . An important caveat in this comparison with Heinsbroek , et al is that we have not observed fungipods on murine bone-marrow derived immature DC stimulated with zymosan or Candida species , and murine immature DC phagocytose zymosan approximately 1–2 orders of magnitude faster than their human counterparts ( data not shown ) . Rapid internalization in murine cells may not allow sufficient time or available membrane surface area for fungipod extension . Apparent differences between murine and human DC handling of yeast particles suggests that there may be significant underlying differences in the function of and signaling by C-type lectins between mouse and human DC . Several features of the zymosan-induced fungipods described in this report bear similarity to actin comet tails associated with Listeria monocytogenes , Shigella flexneri , vaccinia virus and rocketing vesicles [43] , [44] , [45] . The overall size and shape of fungipodial actin structures as well as their propensity to elongate with rotation are mirrored by tapered F-actin comet tails that arc in either right or left handed fashion through the cytoplasm behind motile intracellular pathogens [46] . The speed of Listeria or ActA-coated bead propulsion is roughly similar to our report of 225±55 nm/second for fungipodial rearward flow [47] . The rearward mobile bands visible by DIC in the distal fungipod are similar to actin densities in comet tail bands seen behind larger ( >3 µm ) actin rocketing beads [48] . The distal fungipod contains copious amounts of cortactin , which is also found in actin comet tails . Interestingly , Listeria actin comet tails contain cortactin throughout but do not contain phosphotyrosine epitopes in the tail suggesting lack of cortactin tyrosine phosphorylation as another point of similarity between fungipods and Listeria comet tails [49] . This similarity of structure , composition and behavior may point to underlying similarities in mechanism of formation between actin comet tails and fungipods . We asked whether our observations of growth in the distal fungipod are comparable to what is understood about actin polymerization and actin rocketing systems in vivo and in vitro . Our measurements of rearward flow in the distal fungipod imply a dendritic actin growth speed of ∼200 nm/second . Assuming actin growth at the distal tip in a 1 µm diameter circular contact with filament barbed ends placed at 40 nm intervals [50] , [51] , [52] and monomer unit elongation length of 2 . 7 nm [53] , our rearward flow rate requires polymerization of ∼3 . 6×104 actin monomers/second at the tip . Previous reports have provided the following information: cytoplasmic G-actin concentration of 12 µM , ATP-actin barbed end assembly kon = 11 . 6 µM−1 second−1 and disassembly koff = 1 . 4 second−1 [54] , [55] . Given these values and assuming the same polymerization surface described above , “free running” actin assembly/disassembly could achieve “free running” rates of ∼6 . 8×104 actin monomers/second . Thus , actin polymerization rates required to support the growth of the distal fungipod are in the range of what is possible . What could be the functional relevance of fungipods to DC function and microbe internalization in general ? On the basis of our observations , we propose the following areas of functional significance: 1 ) Improved binding/retention of particles , 2 ) Promotion of phagocytosis ( including coiling phagocytosis ) , 3 ) Participation in a size discrimination program in phagocytes ( Figure 8B; Figure S4B ) . Fungipodial protrusions may promote phagocytosis of yeasts by improving long-term retention . DC experience long contact times with zymosan particles prior to internalization , and many bound particles are not phagocytosed despite hours of contact ( Figure 1K; Figure 4I; Video S6; Figure S3A ) . It is attractive to speculate that fungipods might be rescuing some fraction of non-internalized , surface-bound zymosans from their most likely fate of non-productive surface-association with the DC . This could occur if the surface of the yeast particle was non-uniformly stimulatory for phagocytosis and interaction with a fungipod could reposition the particle or allow improved exploration of its surface by the DC ( i . e . , via the membrane wedges we have observed in Figure 6G ) . Our analyses show that zymosans attached to fungipods have a probability of internalization equivalent to that of their non-fungipod associated counterparts ( Figure S3C ) . Because of this , it is possible that fungipods do not influence phagocytosis or even that they delay , but do not absolutely prevent , phagocytosis . Distinguishing between these competing interpretations would help to elucidate the functional role of fungipods , but it will require future development of experimental approaches to specifically ablate fungipods without influencing phagocytosis . Previous reports have identified “coiling phagocytosis” , a monolateral engulfment that acts to internalize bacteria and yeast via a single pseudopod that wraps around the particle and creates a phagosome . We have identified examples of coiling phagocytosis occurring under conditions where fungipods are present ( Figure 6E , F ) . Of course , we cannot be sure from thin section TEM data that these particular zymosan particles were associated with fungipods . Furthermore , we often see wedge-shaped membrane projections associated with surface bound zymosan particles that fit very closely the type of monolateral engulfing structure that one would expect to see in coiling phagocytosis ( Figure 6G; Video S10 ) . Consistent with a role in phagocytosis , these membrane wedges are rich in F-actin and cortactin ( Figure 6G ) . Professional phagocytes such as macrophages and immature DC must be able to recognize and respond to pathogens with a wide range of sizes from viruses of <100 nm diameter to much larger extracellular pathogens ( i . e . , helminthes , filamentous fungi and Leishmania promastigotes ) with sizes actually greater than the phagocyte itself . Typically particles >0 . 5–1 µm diameter elicit phagocytic activity [56] with an optimum at 2–3 µm [57] . While one still observes phagocytosis of zymosan particles in immature DC , they are larger than the optimum particle size . A possible fungipod ontogeny could include generation of fungipodial protrusions from pseudopods or membrane ruffles that are frustrated in their attempts to engulf the large particle for a prolonged period of time . If fact , we do sometimes see small nodules of apparently ruffling membrane next to zymosan particles visible in DIC and these nodules can eventually become fungipods ( Video S2 ) . We found that yeast-sized mannan or anti-CD206 coated beads ( 5 µm ) induced fungipod formation while similar particles of 1 µm diameter did not suggesting that fungipods are part of a size discrimination capability of immature DC . According to our model of fungipod formation ( Figure 8A ) , a patch of clathrin is stabilized by CD206 ligation under a zymosan particle . The particle size dependence of fungipods may represent the integration of this contact site size in the form of the number of effector proteins ( i . e . , cortactin ) recruited to the contact site . A critical contact site area may exist such that the amount of cortactin recruited may only become sufficient to generate a fungipod once the critical contact area is surpassed . Interestingly , while phosphoinositide 3-kinase ( PI3K ) is often required for engulfment of large particles ( >3 µm ) , we have found that the PI3K inhibitor LY294002 has no effect on fungipod formation ( data not shown ) . Pathogenic fungi , such as C . albicans , can form large structures that may be difficult or impossible to internalize [58] . Likewise , some nematodes , including parasitic worms , possess chitinous mouthparts and egg shells that could be recognized by CD206 but are clearly too large to engulf . It is interesting to note that recently published intravital imaging of dermal DC interacting with Leishmania major promastigotes demonstrated the formation of long pseudopodial DC protrusions contacting promastigotes in vivo [59] . Promastigotes of Leishmania species are typically larger than 10 µm , they display mannan on their surface [60] , and they interact with innate immune cells via CD206 [61] . Protrusive structures such as the fungipod may be used in innate immune reactions to larger extracellular pathogen structures . We observed that DC interaction with Candida species leads to fungipod formation suggesting that fungipods are involved in recognition of this medically significant genus of fungal pathogens . However , the fungipod response generated against Candida species was clearly dissimilar among species as a robust response occurred against C . parapsilosis , a weak response against C . tropicalis and no response against C . albicans . This specificity is somewhat surprising since Candida cell walls are considered to be quite comparable to S . cerevisiae cell walls in structure and composition , and the cell wall polysaccharides produced by these three highly related Candida yeasts are presumably quite similar . Subtle differences in cell wall polysaccharide structure might underlie the species-specific fungipod response to Candida . We observed the same trend of fungipod responsiveness to different species of Candida that had been fixed . This rules out active encouragement or inhibition of fungipods by the yeast and is consistent with intrinsic differences in cell wall composition or structure being the cause of the observed differential fungipod response among Candida species .
PBMC were isolated from human peripheral blood buffy coats purchased from New York Blood Center ( New York , NY ) . Monocytes were isolated by adherence on tissue culture treated plastic flasks . Immature dendritic cells were prepared by culturing monocytes with 500 U/ml human IL-4 and 800 U/ml human GM-CSF ( Peprotech , Rocky Hill , NJ ) in RPMI-1640 medium with 10% heat inactivated FBS in glass-bottom MatTek dishes ( MatTek Corp . , Ashland , MA ) for 6 days . Immature macrophages were produced via the same procedure but with omission of IL-4 . DC and macrophages were activated with 250 ng/ml LPS ( Sigma , St . Louis , MO ) for 24 hours prior to use . Unlabeled , formalin killed zymosan was obtained from Invitrogen ( Carlsbad , CA ) and resuspended as a PBS stock . Zymosan was used at a final concentration 20 µg/ml ( unless otherwise noted ) after 3×15 second vortexing ( max speed ) and 3×15 second bath sonication to achieve monodispersion . Cells were incubated with zymosan for 4 hours , 37°C unless otherwise noted . Live , wild-type S . cerevisiae was obtained from log phase cultures and used at comparable density to zymosan . Candida species were obtained from the ATCC and grown to log phase culture in YM broth at 30°C then added to DC co-cultures at 5×105 yeast/ml for 4 hours at 37°C . Quantification of fungipods and zymosan was done from 3D image stacks by a single investigator who was blinded to the experimental conditions of these images . Samples prepared for all light microscopic observations except CD206 staining were fixed 20 minutes with 37°C 2 . 5% glutaraldehyde ( Electron Microscopy Sciences , Hatfield , PA ) in PBS , pH 7 . This fixation provided optimal preservation of fungipod structures , but was not compatible with CD206 immunostaining . For CD206 fluorescence imaging , samples were fixed 20 minutes with 37°C 4% paraformaldehyde ( Electron Microscopy Sciences , Hatfield , PA ) in PBS , pH 7 . Wide field light microscopy was performed on an Olympus IX81 inverted microscope with a 60x , 1 . 4 NA oil objective lens , stage with z-axis stepper motor control , and objective-based autofocus ( Olympus , Center Valley , PA ) . A 37°C , CO2 controlled stage insert ( Warner Instruments , Hamden , CT ) was used for live cell imaging . DIC images , except those accompanying confocal fluorescence images , were taken using the DIC optical train of this microscope . A 100 W Hg arc lamp provided epifluorescence illumination . Filters and dichroic mirrors ( Chroma , Rockingham , VT ) for Alexafluor-488 imaging were as follows: excitation , 488/10; emission , 535/25; dichroic , 475/25 . For DiI imaging we used the following: excitation , 535/50; emission , 605/40; dichroic , 530/20 . Images were captured using an air-cooled SensiCam QE CCD camera ( Cooke Corp . , Romulus , MI ) driven by Metamorph ( Molecular Devices , Downingtown , PA ) . Laser scanning confocal microscopy was performed on a Zeiss 510 Meta inverted instrument using a 63x , 1 . 4 NA oil objective lens . Samples were illuminated with 488 nm and 543 nm lines from a 30 mW Ar ion laser and a 1 mW He-Ne laser , respectively . We used a UV/488/543/633 main dichroic mirror and a NFT545 secondary dichroic in cases of dual color imaging . Alexafluor-488 emission was collected using a LP505 emission filter , and rhodamine fluorescence was collected with a LP560 filter . Data were collected in 1024×1024 pixel format and 12-bit depth , with non-interlaced , descanned , multitracked scanning and 4 scan averaging . Z-axis steps were taken in increments of 200 nm . Primary antibodies used were as follows: cortactin ( 4F11; Millipore , Temecula , CA ) , clathrin light chain ( CON . 1; Santa Cruz Biotechnology , Santa Cruz , CA ) , CD206 ( “anti-hMMR”; R&D Systems , Minneapolis , MN ) . Antibody staining was done with 10 µg/ml primary antibody , 30 minutes , 25°C . Secondary antibodies ( anti-mouse or goat IgG , as appropriate ) labeled with Alexafluor-488 ( Invitrogen , Carlsbad , CA ) were used at 1 µg/ml , 30 minutes , 25°C . F-actin was stained with rhodamine-phalloidin ( Invitrogen ) according to the manufacturer's instructions . Membrane staining with DiI-C18 ( Sigma , St . Louis , MO ) was done at 1 µg/ml . Samples were prepared by fixation in 2 . 5% glutaraldehyde in 0 . 1 M Cacodylate ( pH 7 . 3 ) followed by 1% OsO4/Cacodylate , dehydration in graded ethanol , critical point drying ( CPD 030; Bal-Tec , Vienna , Austria ) , and sputter coating ( Polaron E-5100; Quorum Technologies; East Sussex , UK ) . Observations were performed using a JEOL 6300 SEM with an Orion Digital Micrography System . Samples were prepared by fixation in 2 . 5% glutaraldehyde , 1% tannic acid , 0 . 1 M Cacodylate ( pH 7 . 3 ) , stained with 2% Uranyl acetate , dehydrated in graded ethanol , and embedded in Epon . 60 nm sections were cut and stained with 2% Uranyl acetate followed by Sato lead stain . Observations were performed using a Technai 12 TEM with a Gatan Multiscan 794 digital camera . Blocking reagents were used at the following concentrations: Mannan ( 10 mg/ml; Sigma , St . Louis , MO ) , Laminarin ( 5 mg/ml; Sigma ) , anti-Dectin-1 polyclonal antibody ( 10 µg/ml; “anti-hdectin-1/CLEC7A” , R&D Systems , Minneapolis , MN ) , anti-CD206 polyclonal antibody ( 50 µg/ml; “anti-hMMR” , R&D Systems ) . Syk Inhibitor II ( EMD Biosciences , Gibbstown , NJ ) was used at 1 µM . For blocking and inhibitor experiments , cells were exposed to the agent at 30 minutes , 37°C prior to the addition of zymosan . However , cytochalasin D and nocodazol were used acutely at 1 µM . Nominal 5 µm ( 4 . 58±0 . 07 µm ) and 1 µm ( 1 . 053±0 . 01 µm ) polystyrene beads were obtained from Polysciences ( Warrington , PA ) . Ligands were passively adsorbed on beads using equivalent total surface area of beads in all reactions . After washing with 50 mM bicarbonate buffer ( pH 9 ) , adsorption was done in 100 µl total volume ( same buffer ) at 25°C overnight . For mannan and laminarin , the adsorption reaction concentration was 10 mg/ml . For anti-CD206 polyclonal antibody ( R&D systems , Minneapolis , MN ) the concentration was 50 µg/ml . For negative controls , beads were coated in 1 mg/ml bovine serum albumin ( Sigma , St . Louis , MO ) , 1 mg/ml chicken egg ovalbumin ( Sigma ) or neat fetal bovine serum . Beads were washed in PBS and used immediately . Chitin particles ( 1–10 µm ) were prepared by probe sonication and centrifugation as previously described [11] , and sizes of individual cell-associated particles were confirmed as ∼5 µm by DIC microscopy . UniprotKB accession numbers for human proteins referenced in these data are as follows: DC-SIGN ( CD209 ) , A8MVQ9; Mannose Receptor ( MRC1 , CD206 ) , P22897; Dectin-1 ( CLEC7A ) , Q9BXN2; Cortactin , Q96H99; Syk , P43405; Clathrin Light Chain A & B , P09496 & P09497; Dynamin 1 & 2 , Q05193 & P50570 .
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Yeasts are normal microbial commensals of humans and a significant source of opportunistic infections , especially in immunocompromised individuals . We report a novel cellular protrusive structure , the fungipod , which participates in the host-microbe interaction between human immature dendritic cells ( DC ) and yeasts . The fungipod's structure is based on and propelled by a robust process of local actin cytoskeleton growth at the DC-yeast contact site , and this cytoskeletal remodeling results in a durable tubular structure over 10 µm long connecting the dorsal DC membrane and yeast . The fungal cell wall polysaccharides mannan and chitin trigger fungipod formation by stimulating the carbohydrate pattern recognition receptor CD206 . Fungipods are part of a specific response to large particulate objects ( i . e . , yeast ) , and they may promote the human immature DC's relatively poor phagocytosis of yeast . The human fungal pathogen , Candida parapsilosis , induces a strong fungipod response from DC , and this response is highly species specific since the related pathogens Candida albicans and Candida tropicalis induce fungipods rarely . Our work highlights a novel cell biological element of fungal recognition by the innate immune system .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"microbiology/immunity",
"to",
"infections",
"infectious",
"diseases/fungal",
"infections",
"microbiology/innate",
"immunity",
"immunology/innate",
"immunity",
"cell",
"biology/cytoskeleton"
] |
2010
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A Novel Pseudopodial Component of the Dendritic Cell Anti-Fungal Response: The Fungipod
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Scale-free networks are generically defined by a power-law distribution of node connectivities . Vastly different graph topologies fit this law , ranging from the assortative , with frequent similar-degree node connections , to a modular structure . Using a metric to determine the extent of modularity , we examined the yeast protein network and found it to be significantly self-dissimilar . By orthologous node categorization , we established the evolutionary trend in the network , from an “emerging” assortative network to a present-day modular topology . The evolving topology fits a generic connectivity distribution but with a progressive enrichment in intramodule hubs that avoid each other . Primeval tolerance to random node failure is shown to evolve toward resilience to hub failure , thus removing the fragility often ascribed to scale-free networks . This trend is algorithmically reproduced by adopting a connectivity accretion law that disfavors like-degree connections for large-degree nodes . The selective advantage of this trend relates to the need to prevent a failed hub from inducing failure in an adjacent hub . The molecular basis for the evolutionary trend is likely rooted in the high-entropy penalty entailed in the association of two intramodular hubs .
Scale-free networks have been proposed as universal models to describe diverse complex systems such as the Internet , social interactions , and metabolic and proteomic networks [1 , 2] . The scale-free “topology” is defined by a power-law distribution: A ( n ) ∝ n−γ , where A ( n ) is the abundance of n-degree nodes and γ is a positive exponent . It has been recently noted that such a generic definition does not determine a unique graph topology [3 , 4] . Rather , topologies ranging from the assortative [3 , 5] , with frequent like-degree node connections , to the highly dis-assortative [5] , with like-degree nodes avoiding each other , may fit the same connectivity scaling law [3] . In a purely operational sense , a highly self-dissimilar network is hereby regarded as modular in the sense that high-degree nodes tend to avoid each other [6] , and , thus , highly interconnected regions are loosely connected to each other . The definition hinges on the assumption that highly interconnected regions are organized around hubs ( the nodes with high degree of connectivity ) which would be then characterized as intramodular [3 , 4] . To determine the graph topology of the yeast protein network [6–10] beyond the power-law distribution and its evolution from a primeval network , we make use of a metric indicative of the degree of graph modularity [3] . The metric is informative of network structure because it increases with the frequency of like-degree connections , and decreases as the graph topology approaches a modular organization in the sense defined above . It should be noted that there is no inherent contradiction in having a scale-free network endowed with a modular topology that reflects a self-dissimilar or dis-assortive structure , since the characterization of scale-free network is solely based on degree distribution [3 , 6 , 9] . We found that the present-day network is actually a self-dissimilar graph , most often linking nodes of dissimilar degrees , thus revealing a marked avoidance of intramodular hub connections in accordance with previous observations [6] . By contrast , ancestors of the network obtained through orthologous categorization of the yeast open reading frames ( ORFs ) [8] are progressively more assortative as we regress toward the network of ancient proteins . The assortative topology brings the ancient network closer to a physical system , where assortativity becomes a generic attribute of the statistical mechanics of phase transitions , and thus an emerging property more readily attainable than modularity [11] . The robustness of the present-day network is found to differ from typical scale-free attributes , since it minimizes its vulnerability to hub failure and not to random node failure [2] , with the former being more likely in protein interaction networks , as shown below . The evolution toward self-dissimilarity is shown to be reproducible through propagation laws of connectivity accretion that promote progressive increase in modularity . Finally , the molecular basis for the observed trend toward a scarcity of like-degree node connections is delineated .
The metric S ( G ) ( 0 ≤ S ( G ) ≤ 1 ) , for a graph G with scale-free degree distribution is defined by [3]: where E ( G ) is the set of graph edges , ( i , j ) is a generic edge linking nodes i and j , Xi , Xj are the respective node degrees ( connectivities ) , and smax ( G ) is the maximum over all s ( H ) -values , where H is a graph with the same connectivity distribution as G obtained by connectivity rewiring . This distribution-preserving rewiring is constructed following [3 , 6] . For a given scaling degree distribution , the metric is informative of the graph structure , reaching its maximum value ( S ( G ) = 1 ) in the case where edges are most frequently connecting similar-degree nodes and decreases as the frequency of dissimilar-degree connections increases [3 , 6] . Thus , a low S ( G ) -value is indicative of graph modularity in the sense defined above , because the expected frequency of hub–hub connections is low and because connections involving hubs are always dominant contributors to the sum defining S ( G ) ( Equation 1 ) . Using this metric , we determined the modularity along the natural evolution of the yeast protein interaction network . Node ancestry classes are defined through orthologous representativity in other genomes informative of the yeast evolution ( Methods ) . Ancestry classes are labeled using binary vectors [8] and defined based on the existence of orthologs in other fungi ( 00011 ) ( 36% of yeast proteome ) , in all other eukaryotes diverging earlier than fungi ( 00111 ) ( 19% ) , in eubacteria ( 01111 ) ( 9 . 5% ) , in archaea but not in eubacteria ( 10111 ) ( 8% ) , in all ancestral groups ( 11111 ) ( 3 . 5% ) , and exclusively in yeast ( 00001 ) ( 24% ) . Thus , a binary vector denotes an ancestry class of proteins . The ancestry is given by the extent of ortholog representativity . Thus , the binary vector indicates from the right entry ( yeast ) to the left ( progressively more distant life domains ) the ortholog representativity of the proteins , with nth entry = 1 if an ortholog of the protein exists in life domain n , and = 0 otherwise . Thus , the network evolution from the ancient-protein ( 11111 ) network is retraced by trimming the present-day network through progressive removal of ancestry classes , starting with the most recent ( 00001 ) . Although the network still contains false-positive and false-negative data in spite of state-of-the-art curation ( Methods ) , the impact of these factors is likely randomly distributed across classes [8] and thus will not significantly affect our conclusions . The trimming of the present-day network following the schedule imposed by ancestry is based on the assumption that a gene arising at a certain point in evolutionary time in an ancestral organism will be detectable in all species diverging thereafter . The ancestry of a yeast protein is thus defined by the number of orthologous ORFs [8 , 12] . Thus , no effort is placed in our study in reconstructing the ancestral sequence , a daunting task at the proteomic scale , but rather in assessing its ancestry by genomic comparison . Gene loss or interaction loss due to deleterious evolutionary pressure is possible after speciation , although very difficult to assess and typically neglected in related evolutionary models [8 , 12] . The present-day and ancestral networks all fit the scale-free connectivity scaling ( Figure 1A ) . However , their graph topologies are radically different . The ancient protein network possesses a high probability of connection between similar-degree nodes , as indicated by the large S ( G ) -value , and thus , it is significantly scale-free and assortative . This topology evolved into the scale-rich self-dissimilar graph ( S ( G ) = 0 . 32 ) found at the present time ( Figure 1B ) . In contrast with its ancestors , the present-time network tends to connect higher-degree nodes to lower-degree ones , as revealed by the low S ( G ) -value . Thus , while the ancestral network is actually endowed with the “emergent” properties commonly ascribed to scale-freeness [1 , 2] , such as robustness to random failure , assortativity , and hub-like core , the present-day network is far less generic , more modular [9] , and more robust to hub failures . This is evidenced by the dearth of inter-hub edges subsumed in its lower S ( G ) -value . The selective advantage of this trend relates to the need to prevent a failed hub from inducing failure in an adjacent hub , as shown below . There are 319 nodes with a present-day degree X > 8 incorporated along the evolution of the network that starts at the ancient network ( cf . [8] ) . All such nodes may be characterized as intramodular hubs [13] that avoid each other and make up for the increased level of scale-freeness in the network topology ( Figure 1B ) . The molecular basis for this like-degree avoidance is described below . We tested the sensitivity of the results to persistent noise in interactomic data ( see Methods for curation details ) . Thus , in Figure 1B , we contrasted the previously reported behavior of the scale-free metric against the results from progressive trimming of a comprehensive interactome of protein complexes in which ephemeral interactions and high-throughput artifacts have been filtered out [14] . The S-values differ by less than 9% along the entire evolutionary span . Furthermore , the trend toward higher modularity ( lower S-value ) appears to be commensurate with organismal complexity ( Figure 1B ) , as we incorporate the S ( G ) -values calculated for the interactomes of Caernohabditis elegans ( worm ) [15] and drosophila ( fruit fly ) [16] . The dynamics of node removal associated to the evolutionary regression is indicated in Figure 1C , where the percentage of node removal associated with each of the four successive trimming iterations is computed for each node connectivity class in the present-day network . The node removal becomes more severe for the nodes of low connectivity and less pronounced as we approach a higher degree of centrality , in accord with the likely higher level of ancestry of high-degree nodes [17] . The trend toward increasing modularity associated with evolutionary change was further validated by disproving the null hypothesis that this trend holds irrespective of network topology . Thus , in several computer experiments ( cf . [3 , 6] ) we randomly rewired the present-day network while preserving the present-day node-degree distribution indicated in Figure 1A . We then successively trimmed the rewired networks following the orthologous classification scheme and computed S ( G ) -values corresponding to the successive trimmings . The results are shown in Figure 1D . We clearly see that the monotonic and dramatic increase in modularity observed for the real yeast network along the ancient → present-day evolution is not a generic network property , but very much depends on the specifics of the network topology that subsume the biological information . Alternatively , we also randomly rewired the present-day network this time without preserving the degree distribution and randomly and successively trimmed it , removing an equal number of nodes as in the orthologous classification procedure . Again , no trend toward decreasing modularity could be associated with the trimming or , conversely , no clear trend toward increasing modularity is found upon network growth . An alternative indicator of modularity put forth by Newman [18] has been also utilized to better describe the evolutionary trend . Newman's approach not only provides a measure of topological dissimilarity but also identifies or separates the dominant or tightest module , and ultimately—through iteration of the separation procedure—provides a modular partition of the network . The initial modular partition of the network is dictated by the spectrum of a symmetric graph-related matrix . Thus , the dominant moduleM℘ is associated with the largest positive eigenvalue , λ1 , of the symmetric matrix B defined as: where A is the adjacency matrix describing the edge set E ( G ) ( Aij = 1 if nodes i and j are connected , Aij = 0 otherwise ) and m = ½ΣjXj is total number of edges in the network . The dominant moduleM℘ is univocally defined by the characteristic function χM℘ ( j ) = ½ ( sj ( u1 ) +1 ) , where u1 is the eigenvector of B associated with λ1 and sj ( u1 ) = 1 if the j-th coordinate of u1 is positive and = −1 otherwise . In set-theory notation: χM℘−1 ( {1} ) =M℘ . This constructive procedure reveals the most densely connected group of nodes with only sparser connections to the rest of the graph and may be further iterated on G\M℘ , etc . , until a full modular partition of G is achieved . A similar definition of the module is provided in [10] . A modularity parameter Q is then defined as an indicator of the number of nodes falling within modules minus the expected number for a random rewiring of the network , normalized to the total number of nodes in the network . Thus , Q is given by: where the dummy index n ranges over all eigenvalues , unT is the transposed eigenvector of B associated with eigenvalue λn , and s = ( sj ( u1 ) ) . The trend toward increasing modularity associated with evolutionary change in the yeast network evolution is then verified adopting the Q-measure , as shown in Figure 1E: in the ancient network , 39% of the nodes were contained in a module and this number increases to 54% in the present-day network . The dominant module in the ancient network comprises all its 19 ribosomal proteins ( see also Protocol S1 ) . This network prevails until class 00111 is incorporated , at which time the signaling module dominates and prevails as dominant in the present-day topology . The topological differentiation resulting from connectivity accretion concurrent with progressive incorporation of node classes in the order ( 11111 ) → ( 01111 ) → ( 00111 ) → ( 00011 ) → ( 00001 ) may be algorithmically reproduced . Thus , the primeval network of ancient nodes–proteins may be abstractly developed , i . e . , without reference to concrete molecular features of the node , in a manner entirely consistent with the S ( G ) behavior shown in Figure 1B . The algorithmic behavior of network evolution is determined by the probability P ( Xn ) = G ( n ) p ( Xn ) that node n with degree Xn would acquire a new connection . The p-factor is associated with the rate of connectivity development , while G penalizes like-degree connections that would increase assortativity . The p-factor relates to a preferential attachment law [1 , 17] in the sense that the probability that a node develops a new connection depends on the number of its pre-existing connections , satisfying: Two accretion laws have been investigated . While heuristic in nature , their accurate reproduction of the evolving network topology makes them worthy of examination: Both laws have optimized parameters ( Figure 2 ) and satisfy the limit Equation 4 . To prevent similar-degree node connections , nodes are “tagged for kinship” at every stage of network propagation taking into account the order assigned at that stage . This order is obtained by preserving the order arbitrarily assigned in the primeval network while incorporating new nodes in consecutive order . To define the accretion rules algorithmically , let n1 < n2 < … be an ordered set of nodes at a specific time in the network development; Gn denote the n-centered subgraph , that is , a subgraph containing node n , all nodes connected to n , and the connecting edges; C ( n ) = {nodes connected to n}; and {Gn} is a minimal covering of G satisfying G = ∪nGn . Then , we may define ξn = Minimumn′∈C ( n ) |Xn − Xn′| . Node n is “tagged for kinship” with probability exp ( −ξn ) provided no node n′ ∈ C ( n ) with n′ < n has been tagged for kinship . A node n tagged for kinship at a particular stage of network development is assigned the kinship penalty factor In case of close kinship ( ξn = 0 ) , we get G ( n ) = 0 . The creation of an internal connection linking node n with another node already tagged to develop a connection is governed by probability where Ln = Maximumn′∈A ( G ) |Xn − Xn′| , and A ( G ) = nodes tagged to develop a connection at the particular stage of network development . If node n is tagged to develop a connection , and an internal connection develops , then the new edge connects n to existing node n* , with the latter satisfying: n*∈A ( G ) ; Ln= |Xn−Xn*| . The algorithmic network development that best fits natural evolution ( Figure 2 ) is given by accretion law ( I ) modulated by precluding kinship connections according to Equations 4 and 5 . While law ( II ) also produces a good fit , it does not portray the sigmoidal behavior of S ( G ) followed by natural evolution . Network development with an accretion law reflecting preferential attachment ( G ( n ) ≡ 1 , law ( I ) ) does not significantly increase its self-dissimilarity relative to the differentiating algorithms that enhance modularity . What sort of selective advantage is associated with evolving toward higher self-dissimilarity or dis-assortativity ? We shall show that this trend increases resilience to node failure which is not random , contrary to general assumption [2] . We first note that node failure may result from a loss of the functionally competent structure in favor of a misfolded state . The latter tends to aggregate into a generic aberrant state dominated by the backbone generic information , rather than by the side-chain information that encodes for the native state [19 , 20] . We cannot assert that misfolding is the sole reason for node failure but it certainly appears to be the dominant one in the light of the results presented below . Soluble proteins with high levels of backbone exposure are prone to aberrant aggregation [20] , and thus likely to “fail” since they would be removed from their normal interactive context by relinquishing their native fold . Since , as shown in Figure 3A , intramodular hubs possess a higher extent of backbone exposure in their native soluble structure ( the extreme case of this exposure is represented by native disorder ) [16 , 20 , 21] , we may conclude that failure propensity likely correlates with centrality , at least in intramodular organization . This finding prompts us to ask the question: Why would the avoidance of hub–hub connections bring about resilience to hub failure ? Since hubs are characterized by their extent of backbone exposure , they are highly reliant on binding partnerships to preserve their structural integrity [16] . Thus , by distorting its protein–protein interface , a misfolded binding partner is likely to promote the hub failure . Hence , to prevent a failed hub from inducing failure in another hub , it becomes necessary to minimize the probability that the binding partner of a hub is also a hub . This is precisely the trend reported in Figure 1B . Thus , we showed that , unlike robustness to random failure , present-day resilience to hub failure is a non-emergent evolutionary trend achieved by enhancing the dis-assortativity of the graph under the generic scale-free degree distribution ( Figure 1A and 1B ) . Hence , the widespread notion that scale-free networks are vulnerable in this sense does not hold in this particular case . The lower level of connectivity among nodes of similar degree in the present-day network [6] has a molecular basis that may be delineated and prompts us to invoke conformational entropy penalties . As indicated previously , there are 319 present-day hubs incorporated along the evolution of the network . Of such nodes , 37 are represented in PDB complexes ( Protocol S1 ) and shown to contain an extent of backbone exposure in over 50% of the molecule ( Methods ) . Typically , high intramodular centrality implies that protein associations entail considerable induced fit , since the extent of backbone exposure of such hub proteins is significant and thus so is their conformational plasticity [16 , 21] . To quantify this trend , we established a correlation between present-day connectivity and extent of backbone exposure on PDB-reported proteins incorporated to the ancient network ( Figure 3A , Pearson correlation coefficient r = 0 . 78 ) . This class of nodes is the complement in yeast proteome of class ( 11111 ) , and thus it is denoted “\ ( 11111 ) ” . We now examine the molecular characteristics of the associations involving proteins in class \ ( 11111 ) , that is , in the complement of the set of oldest proteins , or in the set of proteins incorporated to the ancestral network . This analysis is needed to rationalize the topological difference between the ancient and present-day network . Induced fit entails a considerable entropic cost associated with the structural adaptation , decreasing the stability of the protein complexes [19] . Thus , induced fits form in the ephemeral complexes typically found in signal-transduction events . On the other hand , a prohibitively high entropic cost would make it unlikely that protein associations would occur if both partners must undergo induced fit . This is reflected in the probability distribution f ( Y , Y′ ) of binding partnerships between pairs of proteins in class \ ( 11111 ) with backbone exposures Y and Y′ ( f ( Y , Y′ ) dY′ = probability of connections between proteins with backbone exposure Y and proteins in the range [Y′ , Y′ + dY′] ) . Proteins with high backbone exposure typically associate with those with low backbone exposure , in an anticorrelated manner ( Figure 3B and 3C ) . Thus , direct comparison of Figures 2 and 3B–3C reveals that high degree nodes in class \ ( 11111 ) are unlikely to connect with nodes of comparable degree because of the high entropic cost associated with two concurrent induced fits . This anticorrelation ( Pearson coefficient r = −0 . 69 ) provides a molecular basis for the modularity and self-dissimiliarity of the present-day network . To extend the validity of the anticorrelation to the full class \ ( 11111 ) , we also adopted a sequence-based predictor of backbone exposure , taking advantage of a tight correlation [16] between extent of backbone exposure and native disorder content , and of the fact that the latter may be predicted directly from sequence [21] ( Methods ) . As backbone exposure in hubs from class \ ( 11111 ) increases to accommodate interaction partnerships in the evolving network ( Figure 3A ) , their likelihood of mutual interaction decreases . This trend is reflected in the present-day Y-Y′ anticorrelation ( r = −0 . 72 ) for class \ ( 11111 ) , which evolved from a Y-Y′ correlation ( r = +0 . 66 ) in the ancient network ( Figure 4 ) . This qualitative change reflects the increasing entropy cost of the reciprocal induced fits required to establish hub–hub associations in the proteins incorporated to the ancient class . Thus , the qualitative evolutionary change described at the molecular level ( Figure 4 ) fits the network's seemingly algorithmic progression toward modularity .
Using a metric to quantify the extent of modularity , we examined the evolution of the yeast protein network and found significant topological differences along evolutionary time that reflect a considerable increase in modularity concurrent with evolutionary change . Thus , aided by orthologous node categorization to trace network evolution [8] , we established a trend from an “emerging” assortative network [5] to the present-day modular topology [3] . This evolution implies a progressive enrichment in intramodular hubs that avoid each other ( cf . [6] ) , thus increasing resilience to hub failure . This trend is algorithmically reproducible through a network-growth law that disfavors like-degree connections . The molecular basis for the evolutionary trend toward higher modularity is rooted in the high-entropy cost of the reciprocal induced fits arising from the association of any two intramodular hubs , an event likely to entail structural adaptation in both proteins . Thus , the avoidance of like-degree of nodes of high connectivity is directly related to the extent of backbone exposure and conformational plasticity of hubs , making it entropically costly for them to adapt to binding partners . This molecular justification of modularity may be complemented by an evolutionary observation . As shown in [8] , proteins tend to interact with partners with the same level of ancestry more frequently than with those outside their ancestry class . Thus , the probability that an ancient hub from class ( 11111 ) interacts with another hub from the same class is higher than the probability that it would interact with a more recent hub . This effect may in part account for the higher assortativity of the primeval network and for the evolutionary trend toward higher modularity reported in this work . However , a countereffect is also apparent since , by the same token , the probability that a hub from class ( 11111 ) interacts with a low-degree node in the same class is also higher than the probability that it interacts with a low-degree node from a more recent class . The relative contribution of each effect is actually subsumed in the computation of evolving modularity reported in this work . In an alternative molecular approach [22] , it was proposed that the number of interactions of a protein is proportional to the number of exposed hydrophobic residues on its surface . This finding would imply that hubs would need to be so hydrophobic that they would hardly qualify as soluble proteins or they would need to be enormous to accommodate all of their binding partners . Furthermore , if this were the case , hub–hub connections would be highly favored through hydrophobic associations , while in known networks this is clearly not the case [6] . Rather , the structural or molecular characteristic of intramodular hubs [17 , 21] and the attribute that enables them to avoid each other in the network is their likelihood of conformational plasticity and—in the extreme case—native disorder , as demonstrated in this work . Lacking expression , localization , and developmental coordinates , the protein interaction network provides an incomplete large-scale description of protein–protein associations . Such a study would likely require integration of the interactome and the transcriptome . Thus , the avoidance of like-degree hub connections shown in this work may often materialize in a lack of spatial or temporal correlation between the nodes , a subject of forthcoming work .
Ancestors of the present-day yeast network were obtained by progressive trimming realized through exclusion of node ancestry classes [8] . Node ancestry classes were determined based on across-species ortholog grouping of yeast proteins . Thus , the primeval network is restricted to nodes with orthologs in all domains of life , while the present-day network incorporates all yeast proteins regardless of their level of ancestry . In a preliminary network curation , connections in the present-day network were only included if independently identified in two sources: Comprehensive Yeast Genome Database from the Munich Information Center of Protein Sequences ( http://mips . gsf . de/proj/yeast/CYGD/db/index . html ) [23] , and reliable subsets of high-throughput screening data [24] . In a second level of curation , the data collected was cross-validated using the APID database that integrates five different repositories for protein interactions including more up-to-date two-hybrid high-throughput data [25] . Finally , the interactomic data was filtered through iPfam representativity ( homologous PDB interactivity ) [26] . We used iPfam as a database of structurally reported interactions and mapped all interacting Pfam domains onto yeast ORFs using the HMM ( hidden Markov model ) -profile based mapping available from the Pfam MySQL database . We then retained only the interactions between two ORFs whenever both ORFs contained Pfam domains that are seen to interact in iPfam . The resulting dataset comprises an intersection of iPfam and the APID-curated interactome . The annotation with Pfam domains entails a substantial filtering ( from 14 , 437 APID-based interactions to 6 , 971 interactions ) and hence represents a high-confidence network . Orthologous classification and grouping of the annotated yeast ORFs ( http://www . yeastgenome . org/ ) were determined from the clusters of ortholog groups [27] . Network representations were performed using standard routines from the program PAJEK [28] . Backbone exposure for node n , denoted Yn , is given as a percentage of contour length of the protein corresponding to under-protected residues , as defined below . The data were obtained from 488 yeast proteins ( out of 6 , 199 ) reported in PDB complexes and four natively disordered yeast proteins [21] . The extent of backbone exposure at a particular residue was determined by counting the number of nonpolar carbonaceous side-chain groups contained within a 6 . 2 Å radius sphere ( ∼thickness of three water layers ) centered at the α-carbon [17] . The extent of backbone shielding , η , within a structured region averaged over a nonredundant curated PDB database ( 1 , 662 proteins , free from redundancy and homology ) is η = 14 . 2 , with Gaussian dispersion = 7 . 2 . Thus , a residue or backbone site with η < 7 is regarded as exposed . The statistics vary as other desolvation radii in the range 6Å < r < 7Å are adopted , but the tails of the distribution identify the same exposed residues . The structural integrity of soluble proteins requires that most backbone amides and carbonyls be protected from hydration . Thus , residues with absent backbone coordinates in a PDB entry ( natively disordered [21 , 29] ) are regarded as exposed and so are residues from entirely disordered proteins . We adopt an established relationship between backbone exposure , η , and a structural parameter , λD , that can be reliably determined from sequence: the propensity for inherent structural disorder in a region of a protein domain [17 , 29] . The latter parameter is assessed with a high degree of accuracy by the program PONDR-VLXT , a neural-network predictor of native disorder [29] . Thus , a disorder score λD ( 0 ≤ λD ≤ 1 ) is assigned to each residue within a sliding window . This value represents the predicted propensity of the residue to be in a disordered region ( λD = 1 indicates full certainty ) . Only 6% of >1 , 100 nonhomologous PDB proteins give false positive predictions of disorder [17 , 29] . The correlation between propensity for disorder and wrapping implies that it is possible to predict backbone exposure directly from sequence . The correlation was originally established between the PONDR-VLXT score at a particular residue site and the extent of intramolecular protection , ρ , of the backbone hydrogen bond engaging that residue ( if any ) . The latter quantity is operationally defined as ρ = η + η′ , where η and η′ correspond to the two residues paired by the hydrogen bond . The strong correlation implies that we can infer the existence of residues with backbone exposure from the PONDR-VLXT score with 94% accuracy for regions with λD > 0 . 35 . The correlation implies that the propensity to adopt a natively disordered state becomes pronounced for proteins that , because of their chain composition , cannot fulfill a minimal protection of their backbone hydrogen bonds .
The SwissProt ( http://www . pir . uniprot . org/ ) numbers for the following yeast proteins/domains are in parentheses: SH3 Domain ( P32790 ) , Cytochrome c ( Q753F4 ) , Actin ( P60010 ) , Myosin V ( Q04439 ) , Ubiquitin ( P61864 ) , Calmodulin ( P06787 ) and Rad14 ( P28519 ) .
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The protein interaction network or interactome emerged as a powerful descriptor in the large-scale phenotypic studies of the post-genomic era . A major concern in such analysis is the integration of interactomic information with other phenotypic descriptors such as expression profile , co-localization , developmental phase , and large-scale protein–structure data . The latter aspect of the integration is the focus of this contribution . We investigate the molecular basis of network robustness to node failure in the most thoroughly characterized interactome , the yeast network . Node failure is by no means a random occurrence across the network as often claimed , but likely to arise in the node-proteins which are structurally the most vulnerable , that is , the ones most prone to misfolding and to form aberrant associations , including aggregates . Thus , network robustness mandates that such nodes not be directly connected , as failure in one hub is likely to induce failure in an adjacent hub . This observation led us to investigate the molecular basis for the avoidance of connections between highly central proteins and to delineate the graph topology resulting thereof . We show how this topology arose in present-day networks and how it differs from the more generic emerging topology of the ancestral network .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods",
"Supporting",
"Information"
] |
[
"yeast",
"and",
"fungi",
"computational",
"biology",
"biophysics",
"genetics",
"and",
"genomics",
"saccharomyces"
] |
2007
|
Molecular Basis for Evolving Modularity in the Yeast Protein Interaction Network
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Human T-Cell Lymphotropic Virus Type 1 ( HTLV-1 ) is the etiological agent of adult T-cell leukemia ( ATL ) and HTLV-1-associated myelopathy/tropical spastic paraparesis ( HAM/TSP ) . It has been estimated that 10–20 million people are infected worldwide , but no successful treatment is available . Recently , the epidemiology of this virus was addressed in blood donors from Maputo , showing rates from 0 . 9 to 1 . 2% . However , the origin and impact of HTLV endemic in this population is unknown . To assess the HTLV-1 molecular epidemiology in Mozambique and to investigate their relationship with HTLV-1 lineages circulating worldwide . Blood donors and HIV patients were screened for HTLV antibodies by using enzyme immunoassay , followed by Western Blot . PCR and sequencing of HTLV-1 LTR region were applied and genetic HTLV-1 subtypes were assigned by the neighbor-joining method . The mean genetic distance of Mozambican HTLV-1 lineages among the genetic clusters were determined . Human mitochondrial ( mt ) DNA analysis was performed and individuals classified in mtDNA haplogroups . LTR HTLV-1 analysis demonstrated that all isolates belong to the Transcontinental subgroup of the Cosmopolitan subtype . Mozambican HTLV-1 sequences had a high inter-strain genetic distance , reflecting in three major clusters . One cluster is associated with the South Africa sequences , one is related with Middle East and India strains and the third is a specific Mozambican cluster . Interestingly , 83 . 3% of HIV/HTLV-1 co-infection was observed in the Mozambican cluster . The human mtDNA haplotypes revealed that all belong to the African macrohaplogroup L with frequencies representatives of the country . The Mozambican HTLV-1 genetic diversity detected in this study reveals that although the strains belong to the most prevalent and worldwide distributed Transcontinental subgroup of the Cosmopolitan subtype , there is a high HTLV diversity that could be correlated with at least 3 different HTLV-1 introductions in the country . The significant rate of HTLV-1a/HIV-1C co-infection , particularly in the Mozambican cluster , has important implications for the controls programs of both viruses .
Human T-cell lymphotropic virus type 1 was the first oncogenic human retrovirus to be identified in 1980 [1] In 1982 , the second type , HTLV-2 , was discovered [2] . These two human viruses originated independently through zoonotic infections from lineages of simian T-lymphotropic virus ( STLV-1 and STLV-2 ) . These inter-species transmission events have been occurring in Africa up to recent times [3]–[4] . In 2005 , Wolfe ND [5] and Calattini S [6] , reported the discovery of the third and fourth HTLV types ( HTLV type 3 and 4 ) in asymptomatic Cameroonese hunters . Some studies had revealed Western blot profiles compatible with HTLV-1 and HTLV-2 in those individuals suggesting an appreciable cross-reaction between these viruses [5] , [7] . HTLV-1 has a remarkable genetic stability when compared with other retroviruses such as HIV ( Human Immunodeficiency Virus ) . However , based on the nucleotide diversity of its LTR region , HTLV-1 can be grouped in 6 major genetic subtypes ( a–f ) , most of them linked to some geographic regions [5] , [8]–[12] All subtypes are present in Africa with different prevalences , except for HTLV-1c that has , so far , only been identified in Melanesia [13] . The genetic lineages of HTLV-1a subtype , also known as Cosmopolitan group , are found all over the world . This group can be further divided in distinct sub-groups , which are characteristics of geographical localization of HTLV-1 infections . The HTLV-1a Cosmopolitan subgroups identified as driving the HTLV infection in Africa were HTLV-1aD or North African subgroup , which is prevalent in Senegal , Guinea Bissau , Morocco , and the transcontinental subgroup in South Africa [14] . HTLV-1 is the etiological agent of Adult T-Cell Leukemia/Lymphoma ( ATL ) and Tropical Spastic Paraparesis/HTLV-1-associated Myelopathy ( TSP/HAM ) . Efforts to disrupt its transmission have been taken in some countries , including the screening of blood donors for the presence of HTLV antibodies . HTLV screenings performed in many African countries have been limited to sero-epidemiological surveys studies using ELISA and Western Blot criteria for HTLV typing [15]–[16] . Recently , the epidemiology of this virus was addressed in donors attending the blood bank of the Maputo city showing rates from 0 . 9 to 1 . 2% [17]–[18] . Additionally , a study conducted in 1999 showed that four TSP/HTLV patients from Mozambique were infected with Cosmopolitan HTLV-1 subtype A [19] . It has recently been shown that the prevalence of HTLV among HIV-infected patients in Mozambique is 4 . 5% , much higher than in blood donors [20] . Mozambique is one of the countries in sub-Saharan Africa with the highest HIV prevalence rates in adult population . The HIV seroprevalence among pregnant women in Mozambique rise in the proportion of infected women of 14 . 0% in 2002 , 17 . 8% in 2003 , 16 . 5% in 2004 , and 20 . 2% in 2005 [21] . In another serosurvey performed in 2007 , a prevalence of HIV infection around 18% was found in women attending antenatal services [22] . HIV-1 subtype C is the major variant of the HIV/AIDS epidemic in the country [23] . However , the status of HIV and HTLV co-infections in Mozambique is still poorly documented , as well as , the subtypes/subgroups of these viruses . The aim of this study was to investigate the molecular identities of HTLV in Mozambique and therefore to gain insights to HTLV infection in the country , focusing on a set of HTLV seropositive blood donors and HTLV/HIV co-infected individuals from Maputo city .
Both Co-infection Protocol ( 148/CNBS/05 ) as well the Blood Bank Study ( 78/CNBS/06 ) were reviewed by National Center in Bioethics in Health ( CNBS ) located in MOH , Maputo , Mozambique . All subjects provided written informed consent and were included in the study . HTLV-1 positive samples were originated from a cross-sectional study conducted among 2019 repeat blood donors at the Maputo Central Hospital blood bank in Maputo city between August and December 2006 , where the prevalence of the infection is 0 . 9% [18] . HTLV/HIV positive samples were originated from a cross-sectional survey conducted among HIV-positive adult individuals naive to Highly Active Antiretroviral Therapy ( HAART ) attending an HIV Outpatient Clinic in Maputo , between March and June of 2006 , with prevalence of HTLV/HIV co-infection of 4 . 5% . Demographic information is on Gudo et al . [18] . All twenty-five specimens , 14 from blood donors and 11 from HTLV/HIV co-infected individuals , were positive by HTLV enzyme immunoassays [Murex HTLV I+II , ( Abbott/Murex , Wiesbaden , Germany ) and Vironostika HTLVI/II ( bioMérieux bv , Boxtel , Netherlands] , and confirmed by HTLV BLOT 2 . 4 ( Genelabs Diagnostics , Singapura ) . The HIV positive individuals were screened by two rapid tests: Determine HIV 1/2 test ( Abbott Laboratories , Tokyo , Japan ) and Unigold HIV test ( Trinity Biotech , Ireland ) . High molecular weight DNA was extracted from whole-blood samples of all 25 subjects using QIAamp DNA Blood Mini kit ( Qiagen ) . The PCR for HTLV typing was carried out as described [24] . The HTLV-1 subtyping was performed with amplification of 672 bp fragment from the LTR region under conditions published previously [25] using Pfu DNA polymerase ( Stratagene ) . Human mitocondrial ( mt ) DNA hypervariable segment I ( HVS-I ) ( 302 bp ) was accessed by PCR using conditions formerly described [26] . The amplicons were separated on a 2% agarose gel and visualized under UV light after ethidium bromide staining . PCR products corresponding to HTLV-1 LTR and mtDNA HVS-I regions were purified using QIAamp PCR purification kit ( Qiagen ) according to the manufacturer's instructions . The amplicons were directly sequenced on both strands using the BigDye Terminator v3 . 1 Cycle Sequencing Kit , with a 3730 Automated DNA Sequencer ( Applied Biosystems , USA ) and the bioinformatics pipeline Chromapipe [27] . LTR sequences were editing and alignment using BioEdit v5 . 0 . 9 . ( Department of Microbiology , North Carolina State University , USA ) and Clustal W programs , respectively . Neighbor-joining ( NJ ) tree was build by PAUP* software version 4 . 0b10 [28] . The HKY model with gamma distribution was selected using the Modeltest 3 . 7 software [29] . The NJ tree was evaluated by bootstrap analysis of 1000 replicates . The mean genetic distance among the Mozambican HTLV-1 clusters were determined using nucleotide p-distance model and standard error estimated by a bootstrap procedures ( 1000 replicates ) on Mega4 software [30] . Mozambican HTLV-1 sequences are available at GeneBank ( accession number GU194504-GU194528 ) . The mtDNA sequences were classified in haplogroups in agreement with the HSV-I nucleotide polymorphisms described [26] , [31]–[32] . The HVS-I nucleotide sequences are available at GeneBank ( accession number FJ888491-FJ888504; HM775388- HM775395 ) .
The results of screening for anti-HTLV-1+2 antibodies and confirmed by a Western blot assay ( HTLV BLOT 2 . 4 , Genelabs Diagnostics , Switzerland ) are in Figure 1 . Most of blood donors with reactivity to antigens encoded by the gag and env genes were considered as infected by HTLV , according to the instructions provided by the manufacturer . Two samples were negative to p53 band and one to P19 . All HTLV positive samples in our study population were reactive to rgp46-HTLV-I and typed as solely infected with HTLV-1 by Western blot with no reaction to recombinant rgp46-HTLV-II . The genetic analysis of 25 Mozambican LTR sequences , using 32 reference sequences representing all HTLV-1 subtypes and sub-groups , as well as African HTLV-1 sequences , clearly demonstrated that all isolates belong to the Transcontinental subgroup of the Cosmopolitan subtype ( Figure 2 ) . These new sequences from Mozambique showed an inter-strain genetic distance ranging from 0 . 0 to 3 . 4% with an overall mean distance of 1 . 6% and standard error ( SE ) estimated of 0 . 3 . This variability is reflected in the grouping of Mozambican LTR sequences in three major clusters ( Figure 2 ) . Thirteen from 25 Mozambican ( MZ ) sequences are close together with sequences from South Africa ( SA ) forming a consistent branch , supported by a bootstrap value of 71% . This cluster , named MZ/SA , has an inter-strain genetic distance of 0 . 0–2 . 1% ( 01 . 1%; SE = 0 . 2 ) and is characterized by the polymorphism C386T ( ATK reference sequence ) . Four sequences with inter-strain genetic distance ranging from 1 . 0–2 . 1% , mean diversity of 1 . 4% ( SE = 0 . 3 ) are related with the sequence HE ( GenBank S76263 ) from Israel , which belongs to Middle East cluster as showed by Ohkura and others , 1999 . Another genetic analysis including more HTLV-1 sequences from this geographical region , based on 490 pb , showed that those 4 Mozambican sequences forming a monophyletic group with sequences from Middle East ( ME ) : KUW3 , SAS , IRN4 , Abl and India: AP15 , TNA ( data not shown ) . The cluster named MZ/ME , is supported by a bootstrap value of 76% and sequences are characterized by a 6 nt deletion between 182–186 nt positions and present the substitutions A240G and A575C . Of note , 6 Mozambican distinct sequences ( 16 MZ , 217 MZ , 526 MZ , 1180 MZ , 284 MZ , 541 MZ ) with mean genetic distance of 0 . 8% ( 0 . 0–01 . 8%; SE = 0 . 2 ) determined the MZ cluster with a high bootstrap support of 89% and characterized by three nucleotide changes: C365T , T573C , and C574T . Some of these HTLV infected individuals ( 11/25 = 44 . 0% ) were HIV-1 co-infected ( Figure 1 ) . In fact , the HIV-1 subtype found in all HTLV co-infected individuals , the HIV-1 subtype C , is the major HIV subtype circulating in Maputo , as shown by Abreu et al . , 2008 [23] . A slight correlation of HIV/HTLV co-infection with cluster MZ could be observed ( 5/6 = 83 . 3% ) . MZ/ME and MZ/SA clusters have HIV/HTLV co-infection frequencies of 25 . 0% and 45 . 4% , respectively ( Figure 2 ) . In order to check the representativeness of this sampling in relation to Mozambican population , mtDNA haplogroup analysis was conducted . This analysis , based on HSV-I sequence motifs , revealed that all those Mozambican individuals belong to African macrohaplogroup L , including the sub-haplogroups L1a , L1c , L1d , L2a , L3d and L3e . The African haplotypes were detected at different frequencies of L1a ( 28% ) , L1c ( 8% ) , L1d ( 12% ) , L2a ( 36% ) , L3d ( 12% ) , L3e ( 4% ) and indistinctly distributed within the 3 HTLV-1 LTR clusters described above .
The two pandemic human retroviruses , HIV and HTLV , were originated as zoonoses in Africa at very distinct times . HTLV is an ancient virus while HIV emerged in the early 20th century [33]–[34] . The present study characterized the HTLV-1 strains in blood donors and HTLV/HIV co-infected individuals from Maputo city , Mozambique , as well as , determined the genetic background of those individuals . The HSV-I mtDNA haplogroup analysis from HTLV-1 infected individuals revealed that all the Mozambican sequences belong to African sub-Saharan haplogroups . It was not possible to establish a correlation between HTLV-1 lineages and a specific ethnics group , since diverse mtDNA haplotypes were observed in the three HTLV-1LTR clusters . All African sub-haplogroups detected in this study ( L1a , L1c , L1d , L2a , L3d and L3e ) were reported as the major frequencies in Mozambique [21]; [25] . In fact , sub-haplogroups L2a and L1a were detected at the highest frequencies ( 36% and 28% , respectively ) , as reported in previous studies of Mozambican population . An mtDNA analysis of 109 unrelated individuals , corresponding to 30 ethnic groups , showed the L2a and L1a sub-haplogroups with the highest frequencies of 47% and 16% , respectively [21] . A larger analysis , using 307 samples from 16 different population groups from Mozambique and boundary areas , also revealed the same haplogroups distribution , with L1a and L2a ( 28% and 27% , respectively ) as the major sub-haplogroups , differently from other geographical regions in Africa [35] . Haplogrup composition and their frequencies are characteristic of main African regions . In South Africa , there is a predominance of mtDNA haplogroup L1d/k , but L2a and L1a are in low frequencies . Central and West Africa , also present high frequencies of haplotypes [L1c and L2* ( L2 other than L2a1 ) , respectively] , which in Mozambique is less representative [35] . Therefore , we can conclude that the Mozambican HTLV-1 cohort considered in our study is representative of the country population . The sub-Saharan Africa is considered to be endemic for HTLV-1 infection , with overall seroprevalence rates in Western and Central African countries being similar to the ones observed in Eastern and Southern African countries [17] , [18] , [36]–[38] . However , there is lack of data concerning the HTLV molecular epidemiology in Eastern African countries , contrasting with studies in Western countries and South Africa [14] , [39] . In spite of HAM/TSP cases have been reported in Africa [3] , including Mozambique [19] , there is no information on the clinical impact of HTLV-1 in African population . The risk of HTLV-I-associated diseases among carriers differs substantially across geographic areas and according to other population characteristics . ATL is reported to be prevalent among individuals in Japan , while Brazilian patients tend to present the HAM/TSP disease , and in Jamaica these two HTLV-1 related illness are quite frequent , suggesting that particular genetic backgrounds may play a role in the disease development [40] . Most of the genetic variability of HTLV-1 is represented in Africa , where the virus was originated [41] . Mozambique is located on the southeast region of the continent , and the presence of HTLV-1 has also been reported in countries that share borders with Mozambique [40] . The characterization of HTLV-1 Transcontinental subgroup as the prevalent subgroup driving the HTLV epidemics in Mozambique , and South Africa [38] shows that there are , at least , two major endemic lineages of Cosmopolitan HTLV-1 in Africa . One , dominating the Northwestern part of the continent , driven by HTLV-1aD trans-Saharan lineage , as proposed by Zehender [14] and a second lineage in Southern countries , determined by Transcontinental HTLV-1 subgroup . The genetic relationship of HTLV-1 Transcontinental subgroup strains from South Africa and Mozambique suggests a common origin of HTLV-1 in both countries , probably due to their common peopling and migration patterns and their intense commercial/migratory linkage and vicinity . It was interesting to find a cluster joining Mozambique , Middle East and India strains that can also be explained by a noticeable Indian population in Maputo city due to the Indians people migration started in the 19th century as well as , consequence of the migratory movements between India and Austral Africa ( 1860 ) [42] . However , the characterization of a cluster defined only by Mozambican strains suggests the existence of HTLV-1 focus in the country . In fact , the reproduction of Mozambican mtDNA haplogroup pattern on the HTLV-1 cohort analyzed in this study , may explain the particularities of HTLV-1 infection in the country . Mozambican mtDNA haplogroup pattern have components from North and South , with a high frequency of moderns haplogroups ( L2 ) , haplogroups implicated in Bantu expansion ( L1a and L3 ) , but also the ancient haplogroup L1d , characteristic of the extinct Khoisan-speaking ethnic group [31] , [35] , is present . Due to the diversity of mtDNA haplogroup composite , we could speculate that both ancient and recent introductions of HTLV-1 infection are responsible for the current picture , modulates by breastfeeding and sexual/injecting drug transmission ways , respectively . Of concern , the significant rate of HTLV-1a/HIV-1subtype C co-infection , particularly in the MZ cluster , demonstrates the dynamic of the two viruses . In fact , the HIV-1/HTLV-1 co-infection posses a great challenge in AIDS follow up tests such as CD4 enumeration related to the prognosis [18] , and the need for implementation of public health control measures , as well clinical protocols focusing on both HIV-1 and HTLV-1 in Mozambique .
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Human T-cell lymphotropic virus type 1 ( HTLV-1 ) is the causative agent of Adult T-Cell Leukemia/Lymphoma ( ATL ) , the Tropical Spastic Paraparesis/HTLV-1-associated Myelopathy ( TSP/HAM ) and other inflammatory diseases , including dermatitis , uveitis , and myositis . It is estimated that 2–8% of the infected persons will develop a HTLV-1-associated disease during their lifetimes , frequently TSP/HAM . Thus far , there is not a specific treatment to this progressive and chronic disease . HTLV-1 has means of three transmission: ( i ) from mother to child during prolonged breastfeeding , ( ii ) between sexual partners and ( iii ) through blood transfusion . HTLV-1 has been characterized in 7 subtypes and the geographical distribution and the clinical impact of this infection is not well known , mainly in African population . HTLV-1 is endemic in sub-Saharan Africa . Mozambique is a country of southeastern Africa where TSP/HAM cases were reported . Recently , our group estimated the HTLV prevalence among Mozambican blood donors as 0 . 9% . In this work we performed a genetic analysis of HTLV-1 in blood donors and HIV/HTLV co-infected patients from Maputo , Mozambique . Our results showed the presence of three HTLV-1 clusters within the Cosmopolitan/Transcontinental subtype/subgroup . The differential rates of HIV-1/HTLV-1 co-infection in the three HTLV-1 clusters demonstrated the dynamic of the two viruses and the need for implementation of control measures focusing on both retroviruses .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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"infectious",
"diseases/sexually",
"transmitted",
"diseases",
"infectious",
"diseases/infectious",
"diseases",
"of",
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2011
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Genetic Characterization of Human T-Cell Lymphotropic Virus Type 1 in Mozambique: Transcontinental Lineages Drive the HTLV-1 Endemic
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The diversity and importance of the role played by RNAs in the regulation and development of the cell are now well-known and well-documented . This broad range of functions is achieved through specific structures that have been ( presumably ) optimized through evolution . State-of-the-art methods , such as McCaskill's algorithm , use a statistical mechanics framework based on the computation of the partition function over the canonical ensemble of all possible secondary structures on a given sequence . Although secondary structure predictions from thermodynamics-based algorithms are not as accurate as methods employing comparative genomics , the former methods are the only available tools to investigate novel RNAs , such as the many RNAs of unknown function recently reported by the ENCODE consortium . In this paper , we generalize the McCaskill partition function algorithm to sum over the grand canonical ensemble of all secondary structures of all mutants of the given sequence . Specifically , our new program , RNAmutants , simultaneously computes for each integer k the minimum free energy structure MFE ( k ) and the partition function Z ( k ) over all secondary structures of all k-point mutants , even allowing the user to specify certain positions required not to mutate and certain positions required to base-pair or remain unpaired . This technically important extension allows us to study the resilience of an RNA molecule to pointwise mutations . By computing the mutation profile of a sequence , a novel graphical representation of the mutational tendency of nucleotide positions , we analyze the deleterious nature of mutating specific nucleotide positions or groups of positions . We have successfully applied RNAmutants to investigate deleterious mutations ( mutations that radically modify the secondary structure ) in the Hepatitis C virus cis-acting replication element and to evaluate the evolutionary pressure applied on different regions of the HIV trans-activation response element . In particular , we show qualitative agreement between published Hepatitis C and HIV experimental mutagenesis studies and our analysis of deleterious mutations using RNAmutants . Our work also predicts other deleterious mutations , which could be verified experimentally . Finally , we provide evidence that the 3′ UTR of the GB RNA virus C has been optimized to preserve evolutionarily conserved stem regions from a deleterious effect of pointwise mutations . We hope that there will be long-term potential applications of RNAmutants in de novo RNA design and drug design against RNA viruses . This work also suggests potential applications for large-scale exploration of the RNA sequence-structure network . Binary distributions are available at http://RNAmutants . csail . mit . edu/ .
RNA's ubiquitous role in regulation and development is now understood to be much more important than previously believed . Apart from messenger RNA ( mRNA ) , transfer RNA ( tRNA ) and ribosomal RNA ( rRNA ) , there are many important enzymatic and regulatory functions of RNA , and it seems clear that we are far from having discovered all non-coding RNA ( ncRNA ) genes ( Non-coding RNA [1] , [2] is functional RNA that is transcribed , yet does not code for a protein ) . Indeed , according to the ENCODE Consortium [3] , RNA is “pervasively expressed” in the human genome , with approximately 15% of genomic DNA being transcribed , much of it into RNA of no known function . The functional diversity of non-coding RNA is enormous , ranging from translating mRNA into proteins via the genetic code ( tRNA ) , to catalyzing the peptidyltransferase reaction in appending an amino acid to the growing peptide ( rRNA [4] ) , to directing the chemical modifications of specific ribosomal nucleotides ( snoRNA [5] ) , to the down-regulation of protein product ( miRNA [6] ) , to gene up- or down-regulation by transcriptional and translational modification ( riboswitches [7] ) , to the regulation of alternative splicing ( [8] ) . To achieve their function , non-coding RNAs ( except for small RNAs such as miRNA ) require a structure well suited to their role . If we assume that ncRNA sequences have been adapted , or optimized , by evolution to fulfill a specific function , it is natural to believe that their structures have been also optimized or at least conserved . This observation is the basis for a family of methods for secondary structure determination using multiple sequence alignment and comparative sequence analysis [9]–[12] . RNA is also a molecule governed by fundamental physical laws , and thus folds according to thermodynamic and kinetic principles . Algorithms using experimentally derived free energy parameters [13] for secondary structure prediction have been successfully designed , implemented and applied in mfold [14] and RNAfold [15] . As a consequence , a series of methods combining thermodynamic principles with evolutionary information [16]–[19] has appeared in the last few years . RNA molecules are not static , forever frozen in a native structure , but rather transition from one low energy structure to another ( slightly different ) low energy structure , due to thermal fluctuations . In his seminal work , J . S . McCaskill [20] introduced an algorithm to compute the partition function over all secondary structures as well as the base pairing probabilities . This approach has been significantly extended by Ding and Lawrence [21] , who sampled secondary structures from the low energy ensemble . There is a growing interest in understanding which nucleotides of structurally important ncRNA are inessential , and may be modified with no phenotypic change , and which nucleotides play a critical role in structure , hence function . Indeed , mutagenesis studies are a popular technique for investigating the structure and function of both RNA and protein . In silico exploration of deleterious mutations in RNA secondary structure have thus far been carried out by exhaustive studies , where an available tool , such as mfold [22] , Vienna RNA Package [23] , or Sfold [24] , etc . is applied successively to each 1-point mutant , then to each 2-point mutant , etc . depending on sequence length and available computational time; see , for instance Barash [25] . Clearly , this exhaustive technique cannot be used to study the effect of many pointwise mutations in a large sequence . In contrast , the current paper describes an efficient algorithm , RNAmutants , to investigate the minimum free energy structure MFEk and Boltzmann low energy ensemble εk of all secondary structures of all k-point mutants , for each value of k . In addition to detecting deleterious mutations , RNAmutants could lead to a better understanding of fast-mutating RNA viruses . By understanding fundamental properties of functional RNAs and their robustness to mutation , there may be ultimate applications of our work to the areas of RNA gene discovery and RNA drug design . In this paper , we describe a new thermodynamics-based method for the investigation of the mutational secondary structure landscape of a given RNA sequence . State-of-the-art thermodynamics-based , single-molecule methods such as McCaskill's algorithm , use a statistical mechanics framework based on the computation of the partition function over the canonical ensemble of all possible secondary structures on a given sequence . Unfortunately , methods such as Zuker's algorithm for minimum free energy structure [14] , McCaskill's algorithm for the partition function [20] , and the sampling method of Ding and Lawrence [24] , do not permit any modification of the input sequence during their execution and thus cannot investigate the mutation landscape of a sequence , except by exhaustive enumeration of all mutated sequences . Indeed , the highly original work on neutral networks due to Peter Schuster and the Vienna group [26]–[28] reposes on such experiments where RNAfold is applied to all 4n many RNA sequences of length n . The theory of neutral network is still an active area of research—see the recent review of Cowperthwaite and Meyers [29] . It follows that RNAmutants could be useful for further studies of these networks . Consequently , except for small exhaustive enumeration studies , such as in the work of Barash [25] , no group has been able to answer questions like the following . What is energetically the most favorable secondary structure adopted by an arbitrary k-point mutant , possibly subject to preserving the location of specific binding sites and possibly constrained by requiring certain positions to be paired resp . unpaired ? If an RNA molecule is under evolutionary pressure to adopt a low energy structure , subject to certain constraints ( binding site , catalytic core ) , then which positions are most likely to be mutated and what is the consensus sequence and secondary structure of the low energy ensemble . There may be objections to what may seem to be yet another thermodynamics-based RNA structure algorithm that we present in this paper , since it is known that RNA secondary structure prediction algorithms that incorporate comparative genomics ( multiple structural alignments ) generally predict structure more accurately than do single-molecule , thermodynamics-based algorithms such as mfold , RNAfold , and Sfold . See work of Gardner and Giegerich [30] , who show for instance the more accurate performance of Pfold , a program of Knudsen and Hein [18] that depends on an explicit evolutionary model and a probabilistic model for structures . There are two answers to this objection . First , our program RNAmutants performs computations and admits biological applications that no other software can realize , regardless of whether the software is based thermodynamics or comparative genomics . Second , recent findings of the encode project consortium [3] indicate that the human genome is “pervasively expressed , ” with many RNA transcripts of unknown function having no homology to known RNA families . While comparative genomics has successfully been used to investigate the structure and evolution of RNAs for which reliable multiple alignments exist , only thermodynamics-based methods can be applied to novel RNAs , such as those reported by the encode consortium . Given the existence of highly reliable , multiple structural alignments of RNAs of the same class , it makes sense to apply comparative genomics methods , such as Pfold of Knudsen and Hein [18] , the phylogenetic stochastic context-free grammar ( phylo-SCFG ) program EvoFold of Pedersen et al . [11] , or the Bayesian MCMC program SimulFold of Meyer and Miklós [12] . In the absence of highly reliable multiple alignments , such as with the raw data of the encode consortium , thermodynamics-based algorithms are not just the only alternative , but such algorithms in general perform rather well . Indeed , on average , the predicted MFE structure contains 73% of known base-pairs when tested on domains of fewer than 700 nt; cf . Mathews et al . [31] . In previous work [32] , we introduced a novel formal grammar framework ( AMSAG ) to compute the δ-superoptimal structure . By δ-superoptimal structure , we mean the minimum free energy ( MFE ) structure among all sequences ω′ with a string edit cost of at most δ from the input sequence ω ( i . e . , ω′ such that d ( ω , ω′ ) ≤δ for a given edit distance d ) . Hence , in principle AMSAG can handle any edit operation ( e . g . , mutation , insertion , and deletion ) . However , in addition to the difficulty of estimating good edit costs , the time required to compute the δ-superoptimal structures can be prohibitive , even for small values of δ . To overcome these problems , in subsequent work [33] , we refined the problem by restricting our sequence search space to k-mutants ( i . e . , sequences differing of exactly k mutations with the input sequence ) . This simplification allowed us to design and implement an efficient algorithm to compute the partition function over all secondary structures of all k -point mutants , with respect to the Nussinov energy model [34] . ( The Nussinov energy model ascribes an energy of −1 per base pair , while ignoring any destabilization due to loops . In contrast , the Turner energy model [13] ascribes experimentally measured , context-dependent free energies for base stacking , as well as positive , destabilizing free energies for various types of loops: hairpins , bulges , internal loops and multiloops . It is well-known that the Nussinov energy model is too simplistic to permit reasonable applications of the kind presented in this paper . ) However , due to AMSAG's generality , it is technically difficult to incorporate the full Turner energy model [13] into the AMSAG framework . In order to circumvent these difficulties , we have designed new multiple recursions , allowing for a technical breakthrough to develop RNAmutants , a unified algorithm to compute the minimum free energy structure MFE ( k ) and partition function Z ( k ) over all k-point mutants of a given RNA sequence , even admitting constraints of two forms—sequence identity constraints ( certain positions , such as those known to be important for protein binding are not allowed to mutate ) , and structural constraints ( certain positions are required to pair or to be unpaired ) . RNAmutants uses the state-of-the-art Turner energy model [13] without dangles . ( A dangle is a single-stranded nucleotide , occurring either 5′ or 3′ to a base pair . This energy model corresponds to RNAfold -d 0 in the Vienna RNA Package . In a first implementation with dangles , the computational overhead caused by including dangles was so prohibitive that we decided not to implement them in the final version of RNAmutants . ) Using our partition function , we explore the mutation landscape of a given RNA sequence by sampling not from the uniform distribution of k-point mutants , but rather from the Boltzmann distribution of low energy k-point mutants . RNAmutants naturally extends the classical RNA secondary structure model . Instead of considering the set of secondary structures that can be built on the input sequence alone , as do mfold , RNAfold , and Sfold , we consider all secondary structures of all sequences with at most k mutations . In other words , given an RNA sequence of length n and an integer kmax≤n , we compute the partition function Zk over all secondary structures of all k-point mutants , for all 0≤k≤kmax . When k = 0 , we obtain McCaskill's partition function . The approach is illustrated in Figure 1 . We then extend the range of techniques developed in previous work [32] , [33] for mutant RNAs and present a sampling algorithm allowing us to sample mutant sequences , together with their sampled secondary structure , from the low energy ensemble . A novelty of our algorithm is to sample mutations according to their weight in the Boltzmann ensemble . This result generalizes the RNA secondary structure sampling algorithm of Ding and Lawrence [21] . From sampling , we derive a novel method to predict mutations disrupting the secondary structure of the original sequence ( a . k . a . deleterious mutations ) . Here , we provide a technical breakthrough far beyond brute force computational techniques in the work of Barash [25] and of Shu et al . [35] . Since there are , or roughly nk , many k-point mutants of an RNA sequence of length n , any method relying on exhaustive listing of all k-point mutants has only a limited range of applicability . We tested our algorithms on six different families of RNA sequences from Hepatitis C and Human Immunodeficiency viruses available in the Rfam database [9] , as well as the 3′ UTR of GB virus C . We then compared our results with experimental studies [36]–[39] , to investigate the robustness of RNA structures and the nature of deleterious mutations . We performed five types of computational experiments , thus showing the range of possibilities afforded by RNAmutants . First , we demonstrate the computational efficiency of RNAmutants by computing the partition function over all possible mutants ( i . e . , all k-mutants , for 0≤k≤n , where n is sequence length ) , and by sampling we estimate the probability of mutation of each nucleotide of the given sequence . Second , we analyze the robustness of RNA structures to point-wise mutations of the wild-type sequence , over a collection of 2806 sequences taken from five different families of RNA elements from hepatitis C virus ( HCV ) and human immunodeficiency virus ( HIV ) . From our analysis of HCV and HIV , we make some observations concerning possible application to RNA gene discovery and drug design . Third , using previously published experimental results [39] , we evaluate the accuracy of our predictions of deleterious mutation predictions for the hepatitis C virus cis-acting replication element ( HCV CRE ) . We suggest new possible mutation sites which have not been previously detected or tested . Fourth , we show how our techniques can be used to identify regions that have been constrained during evolution to conserve patterns preserving the ( functional ) structure of a given RNA . In this fashion we can predict nucleotide sites likely to be under purifying selective pressure . Taken altogether , our applications of RNAmutants provide a better identification and understanding of those critical areas of an RNA secondary structure . Finally , by scanning of the 3′ UTR of the GB RNA virus C with a fixed size frame , we show how RNAmutants can be used to perform genome-scale analysis and offer a novel insight inside the genome structure that cannot be achieved through other approaches . More specifically , we provide evidence that the sequence has been optimized to preserve evolutionarily conserved stem regions from a deleterious effect of pointwise mutations .
We build our algorithms upon the seminal McCaskill's recursions [20] . Hence , for the benefit of the reader , we give a brief presentation of McCaskill's algorithm . Given RNA nucleotide sequence a1 , … , an , we will use the standard notation to denote the free energy of a hairpin , to denote the free energy of an internal loop ( combining the cases of stacked base pair , bulge , and proper internal loop ) , while the free energy for a multiloop containing Nb base pairs and Nu unpaired bases is given by the affine approximation a+b Nb+c Nu . For RNA sequence a1 , … , an , for all 1≤i≤j≤n , the McCaskill partition function Z ( i , j ) is defined by ΣS e−E ( S ) /RT , where the sum is taken over all secondary structures S of a[i , j] , E ( S ) is the free energy of secondary structure S , R is the universal gas constant , and I is absolute temperature . Definition 1 ( McCaskill's partition function ) Before continuing , we remark here that in our implementation of McCaskill's algorithm and its far-reaching extension , RNAmutants , we parse the free energy parameters from tables of mfold 2 . 3 for all temperatures 0 to 100 in degrees Celsius ( for reliable free energies at temperatures other than 37 °C ) . RNAmutants also allows the user to choose to apply the newer mfold 3 . 0 energy parameters at 37 °C . Affine parameters a , b , and c for multiloops are taken from mfold tables as well . With this , we have the unconstrained partition function ( 1 ) The constrained partition function closed by base pair ( i , j ) is given by ( 2 ) The multiloop partition function with a single component and where position ii is required to base-pair in the interval [i , j] is given by ( 3 ) Finally , the multiloop partition function with one or more components , having no requirement that position i base-pair in the interval [i , j] is given by ( 4 ) See Figure 2 for a pictorial representation of the recursions of McCaskill's ( original ) algorithm [20]; note that the recursions are are not quite the same as those given in [15] . We now turn to our mutational partition function and show how to generalize the original McCaskill's recursions . In the following , a base pair between nucleotide ai and aj is denoted by the ordered pair ( i , j ) . When we wish to consider the nucleotides of this base pair , we write 〈x , y〉 , where x = ai , y = aj . In short , round brackets connote nucleotide positions , while angle brackets connote nucleotides . Since we consider mutations , we need to introduce energy parameters for hairpins , stacked base pairs , bulges , and internal loops , in which nucleotides and sometimes their neighboring nucleotides are explicitly given . Parameters for multiloops remain unchanged . This is done in the following definition . Definition 2 ( Generalized free energy parameters ) Let x , x′ , y , y′ , u , u′ , v , v′ denote nucleotides , and ℓ , ℓ1 , ℓ2 denote lengths . Free energy parameters used in the functions in Definition 2 come from the most current nearest-neighbor model described in [13] . Our recursions require the following notation . Let denote the set of RNA nucleotides A , C , G , and U , and let denote the set of Watson–Crick and wobble pairs AU , UA , GC , CG , GU , and UG . The number of k-point mutants of a given RNA sequence of length n is clearly equal toWe use the Kronecker delta function , defined byAs well , let σx , y = 1−δx , y . As we will see in the following , these notations allow to keep the structure of the McCaskill algorithm [20] unchanged and thus generalize its principle . In consequence , we use the same partition function arrays given in definition 1 , but extend them to keep track of the number of mutations k and the nucleotides x and y at the extremities of the sequence ( i . e . , at index i and j ) . In other words , we add the fields k , x , and y to the partition function arrays . We now begin the recursion equations . Given RNA sequence a1 , … , an , the k-point mutant partition function for interval [i , j] with nucleotide x at position i ( ai = x ) and nucleotide y at position j ( aj = y ) is given by ( 5 ) where . In the sequel , we show how to compute ZB . To compute ZB , we need first to compute the partition functions for hairpins , for stacked base pairs , for bulges , for internal loops , for multiloops of exactly one component , and form multiloops of at least one component . The partition function for a hairpin is given by ( 6 ) where . The partition function for a stacked base pair is given by ( 7 ) The partition function for a bulge is computed by summing over all possible opening base pairs 〈u , v〉∈B at one extremity of the bulge , over all bulge sizes b , and over the number m of mutations in the bulge . The location of the bulge ( left or right ) must be distinguished . To simplify the notations we let Δ denote j−i−3−θ . ( 8 ) where and . The recursion associated with an internal loop is an extension of that for bulges . We sum over all possible base pairs 〈u , v〉∈B at the extremity of the internal loop and consider all possible nucleotides x′ , u′ , v′ , y′ adjacent to the base pairs defining the loop . All possible lengths for the left ( ℓ1 ) and right ( ℓ2 ) portions of the internal loop are considered , and we distribute 0≤m≤min ( ℓ1 + ℓ2 , k ) mutations within the loop , the remaining mutations left for the component closed by ( u , v ) . Since there are special energy parameters for 1×1 , 1×2 , 2×1 ( and 2×2 ) internal loops , these cases are treated independently; i . e . , when x′ = u′ or v′ = y′ . For readability , we suppress these latter loop details , although they are handled correctly in the program RNAmutants . Denote Δ′ = j−i−7−θ . The partition function for internal loops is given by ( 9 ) where . We now focus on the formation of multiloops , first considering the computation of ZM1 for multiloops having a single component . The definition of ZM1 ( k , i , j , x , y ) requires that position i base-pair in the interval [i , j] , so we consider all intermediate positions i<r≤j which might base-pair with i , and distribute the required k mutations among the component closed by ( i , r ) and the unpaired bases in the interval [r+1 , j] . This yields ( 10 ) Now we consider the partition function ZM ( k , i , j , x , y ) for multiloops of one or more components , without the requirement that position i base-pair . There are two cases to consider . First , we determine an intermediate position i≤r<j for which there is a multiloop with exactly one component closed by base pair ( r , s ) for some r<s≤j , and all bases in the intervals [i , r−1] and [s+1 , j] are unpaired . This case is handled by a recursive call to ZM1 , where we distribute the k mutations among the intervals [i , r−1] and [r , j] . In the second case , we determine a multiloop of one component closed by a base pair of the form ( r , s ) where i<r<s<j and recursively consider the multiloop on the interval [i , r−1] . Again , k many mutations must be distributed between the left and right multiloops . This yields the following ( 11 ) where and . We can now formalize the recursion for the constrained partition function ZB ( k , i , j , x , y ) closed by base pair ( i , j ) . This function is defined by ( 12 ) where . For a given RNA sequence of length n , we define the partition function for k-point mutants byFinally , given a length n RNA sequence , the ( complete ) partition function for mutants is given byFigure 2 illustrates these recursive equations using Feynman diagrams . Drawing on analogous notions from thermodynamics , we may consider McCaskill's partition function [20] to be over the canonical ensemble of all secondary structures of a given RNA sequence , while the ( complete ) mutant partition function is over the grand canonical ensemble of all secondary structures of all mutants of the given sequence . The computation of the complete partition partition of the grand canonical ensemble of a sequence of length n is achieved in time O ( n5 ) and space O ( n3 ) . Compared to the original complexity of the McCaskill partition function algorithm ( O ( n3 ) in time and O ( n2 ) in space ) , the increase of the complexity in space can be imputed to the necessity to add a parameter in the dynamic array to memorize the exact number of mutations occurring between two index i and j . While the increase in the time complexity results from the enumeration of all configurations obtained from the concatenation of these two arrays in Equation 11 . In practice the enumeration of the eight index at the extremities of the internal loops in Equation 9 generates a large constant overweighting this recursion . The growth of the weight of this phenomena in the time complexity saturates once more than eight mutations are performed since no more mutation can be performed in the configuration . However , the constant remains large and for usual RNA sequence lengths ( few hundreds ) the time complexity may be dominated by this term . Curves illustrating time performances of RNAmutants in function of the number of mutations performed for a fixed size input or of the length of the input sequence are given in Figure 3 . Figure 3A shows the time required for each value of k for a 37 nucleotide sequence ( Hepatitis C virus stem-loop IV ) . Statistics have been computed for the 110 sequences of the Rfam seed of the Hepatitis C virus stem-loop IV . Figure 3B shows the time required to compute the complete partition function over all mutants of a given length N . We computed the statistics over five random sequences of size 0≤N≤37 . The experimental complexity progressively converges toward the theoretical bound of O ( n5 ) . The gap observed between the two curves for small values of N can be explained by ( i ) the combinatorial explosion of the internal loops configurations detailed above and ( ii ) the fact that the maximum length of internal loops is not reached . ( This upper bound is usually set to 30 and is used to justify a time complexity of O ( n5 ) . ) The sampling procedure follows the classical stochastic backtracking method introduced by Ding and Lawrence [21] . Complexity improvements using the boustrophedon technique recently introduced by Ponty [40] may also be adapted , but for purposes of clarity , such improvements are not discussed here . ( In work of Ding and Lawrence [21] , sampling RNA secondary structures , given the McCaskill partition function , has worst-case run time O ( n2 ) , where n is RNA sequence length . In contrast , Ponty [40] shows how the boustrophedon sampling method requires run time O ( n log n ) in the worst case . In addition , Ponty proves an average-case run time improvement from O ( n √n ) to O ( n log n ) . ) The main novelty of our sampling algorithm is that in addition to a sample secondary structure traditionally output by RNA sampling algorithms [21] , [40] , [41] , it also outputs a sample k-mutant RNA sequence . Indeed , the algorithm will output of a series of sequences with k mutations , together with secondary structures for these sequences . Once the partition function is computed and the dynamic programming tables are filled , we proceed to a stochastic backtracking using the values stored in the arrays , together with the equations given in the previous section , to ( randomly ) decide which parameters will be used for each recursive calls . The algorithm uses three functions to sample each basic type of secondary structure motif ( e . g . , exterior loop , stem and multiloop ) . An overview of the complete procedure is given in Figure 4 . The process starts by randomly choosing the initial parameters x ( the leftmost nucleotide ) and y ( the rightmost nucleotide ) and eventually k ( number of mutations ) . In contrast , if desired , for fixed value of k , one can sample precisely the Boltzmann weighted k-point mutants . The probability of such a configuration is given by . Then , we sample the exterior loop with the function sampleExteriorLoop and recursively call the function sampleStem to build each type of loop ( i . e . hairpin , stacked pair , bulge and internal loop ) . An exception is for multiloops which use the function sampleMultiLoop . The recursions end each time when a hairpin is created inside the function sampleStem . A deleterious mutation in RNA is a nucleotide mutation which alters the structure or function of the molecule . For example , the catalytic core of the Tetrahymena thermophila group I intron contains a well-defined guanosine binding pocket , whose geometry depends on the secondary and tertiary structure adopted by the intron . Disruption of binding ability caused by a mutation leading to a different structure would be termed deleterious . The prediction of deleterious mutations has recently emerged as a useful and promising research direction [25] , [35] . With the exception of the present paper , all current techniques rely on exhaustively enumerating all possible pointwise mutants , followed by the application of available software such as mfold [22] , RNAfold [23] , or Sfold [24] . Unlike the approach using RNAmutants , such approaches are limited and cannot be applied to long sequences and/or with more than one or two mutations . Consequently , such traditional approaches could well miss potentially critical mutations or groups of mutations . Our method is described as follows . Given a wild-type sequence and its native structure ( by native structure , we mean either the secondary structure inferred from the X-ray crystal , or in the absence of crystal structures , the secondary structure inferred by comparative sequence analysis . Often we take the Rfam consensus structure as the native structure ) , we use RNAmutants to sample an ensemble of 1000 k-point mutant sequences and their structures , for each value of k , from 0 to the maximum number of mutation allowed , denoted by kmax . ( If not stipulated as part of the input , then kmax = n . ) To ensure the pertinence of our approach , we first verify that the centroid secondary structure at level 0 ( i . e . , no mutation ) is close to the native structure . Here , by centroid structure , not to be confused with Rfam consensus structure , we mean the secondary structure consisting of those base pairs , whose frequency of occurrence in the sampled set is strictly greater than 0 . 5 . Then , at each level 1≤k≤kmax , we probe the samples and extract the sequence and structure such that the base pair associated with the mutation does not belong to the native structure . Alternative experiments or more flexible criteria can be adopted , but the latter seemed to give the best compromise between the number of candidates and the relevance of the structural deterioration . We measured the deleterious effect of a base pair in the mutant structure , which does not occur in the native structure , by using a value called the break number . The break number is computed as the number of base pairs that must be removed from the native structure to prevent the formation of a pseudoknot or base triple , if we force the presence of the base pair created by the mutation . In this fashion we quantify the deleterious effect induced by the newly created base pair . A break number of 0 indicates that the new base pair is compatible with the native structure and does not create any pseudoknot or base triple . In lieu of measuring break number , we could have computed the base pair distance between mutant and native structure; however , two topologically very similar structures can have large base pair distance . For instance , both of the structures GGGGGGGGACCCCCCCC GGGGGGGGACCCCCCCC ( ( ( ( ( ( . . . . . ) ) ) ) ) ) . ( ( ( ( ( ( . . . . ) ) ) ) ) ) are very similar , and both have free energy of −13 . 80 kcal/mol , yet their base pair distance is 12 . For this reason , we introduce and use break number . Deleterious mutations extracted from the sample set are ranked according to their deleterious effect , i . e . , in decreasing order , sorted by break number . A ranking based on the frequency of occurrence of the mutation would not have been necessarily a wise choice . Indeed , this approach would have highlighted those mutations that lower folding energy , since these would the largest weight in the Boltzmann ensemble . Deleterious mutations that break the native structure do not necessarily improve the MFE in the first steps and hence would appear with a lower frequency in the sample set .
We illustrate in this section the computational efficiency of RNAmutants by exploring the full mutation landscape of a family of RNA sequences ( i . e . , we compute the partition function for all 0≤k≤n ) . By sampling , we estimate the probability of mutation of each nucleotide by evaluating its effect on the thermodynamic stability of the structure of all k-mutants . Additionally , we compute the MFE and the free ensemble energy for all k-mutants . We tested our software on 110 sequences of Hepatitis C virus stem-loop IV ( HCV SLIV ) , each comprising 37 nucleotides , taken from the seed alignment of Rfam [9] . For each sequence , we compute the ( complete ) partition function over all possible mutants . In the case of the Hepatitis C virus stem loop IV , this represents a total of ( ≈ 1 . 9×1013 ) sequences . Then , for each sequence and each value of 1≤k≤37 , we sample 1 , 000 k-point mutants and structures . Per HCV SLIV sequence , this procedure requires about 3 h on a 2 . 6 GHz AMD 64 byte processor with 250 Mb . The same operation is of course impossible using any classical software such as mfold [14] or RNAfold [15] . We show the results in Figure 5 . Figure 5A depicts the mutation profile , which gives the probability of mutation of a residue at a level k ( i . e . , among all k-point mutants ) . Here , the profile is displayed as a 37 × 37 matrix with position in the sequence ( sequence index ) on the x-axis and the level k on the y-axis . The probability of mutation observed over samples is represented as a gray level . A probability of 1 is displayed as a black entry while a probability of 0 is displayed as white . Below the matrix , we also give the sequence logo and the consensus secondary structure from the Rfam seed alignment . The mutation profile allows us to identify fragile and robust positions in the sequence . In the case of Hepatitis C virus stem-loop IV ( HCV SLIV ) , the secondary structure given by the consensus Rfam seed alignment is a single stem with a tight hairpin loop , without any structural irregularity such as a bulge or internal loop . Such a secondary structure for HCV SLIV is energetically favorable and cannot be drastically improved . Thus , the mutations will tend to conserve the structure and improve the base stacking free energies , while preserving the same base-paired positions . Since the stacking of GC base pairs provides the lowest stacking free energy , all non-GC base pairs will tend to be substituted by GC in the first steps . The sequence logo in Figure 5A confirms this intuition , showing that positions with a clear preference for the nucleotide U , and base-paired with a G in the consensus structure are the first to mutate . Subsequently the nucleotide A tends to be affected , while C and G are relatively well conserved . All columns present a strictly monotone gradient of color from white to black , thus suggesting that preferred mutation sites are independent and ordered . In addition , the mutation profile shows an alternation of white columns ( groups of residues which start to mutate with small value of k ) and black columns ( groups of residues which mutate late ) . Here , it appears that base-paired positions evolve simultaneously ( see , for instance , the motif AU at index 13–14 and UA at index 22–23 ) , presenting examples of compensatory mutations . This phenomenon reveals a stability in the base-pairing of the regions involved , certain to be of interest in RNA design . In Figure 5B we plot the superposed curves of k-superoptimal free energies and k-mutant ensemble free energies , as computed by RNAmutants; the x-axis represents the number of mutations and the y-axis the energy in kcal/mol . Here , k-superoptimal free energy is defined as the minimum free energy ( MFE ) over all mutants having k mutations [33] , while k-mutant ensemble free energy is defined by −RI log ( Zk ) . ( In work of Waldispühl et al . [32] and Clote et al . [33] , the k-superoptimal structure is defined to be the MFE structure over all ≤k-point mutants , while in the present paper , it is defined to be the MFE structure over all k-point mutants . The current usage seems more appropriate . ) These results provide a novel insight into preferential mutation sites as well as structural impacts caused by mutations . We now analyze the curves of Figure 5B . While the ensemble free energy curve resembles a parabola , the superoptimal free energy curve shows three distinct regions ( k≤5 , then 6≤k≤17 , and 18≤k ) , each having a linear appearance . Each region reflects the phenomenon described above . From k = 0 to k = 5 , the GU base pairs are progressively substituted by GC and the slope is roughly equal to the difference of the stacking free energies associated with both base pairs . Then , the region from k = 6 to k = 17 is associated with the substitution of AU base pairs by GC , which now requires 2 mutations with a smaller gain of energy . Other optimizations , such as the reordering of nucleotides G and C inside the stem , only bring minor energy improvements and are then performed in the last region ( 18≤k ) which presents a flat free energy profile . The characteristic shape of the superoptimal energy curve may be of interest for characterizing sequences that require an optimal secondary structure . Interestingly , the 5-nucleotide hairpin is very well conserved over sample centroid structures ( base pairs with a frequency >0 . 5 in the sample set—data not shown ) , even for large values of k . Indeed , a tetraloop hairpin might have been expected , due to the energy bonuses assigned to GNRA-tetraloops . This suggests that evolutionary pressure might have designed the sequence to prevent any slippage in the formation of the helix . Since the secondary structure is conserved throughout the sampled ensemble , the following questions arise . What function is required by those structural motifs that are preserved in the sampled ensemble ? Why did evolution not select a thermodynamically more stable secondary structure in such cases ? Our ability to compute , for the first time , the complete mutation landscape for a given RNA sequence , makes RNAmutants a fundamental tool to address such questions . By using RNAmutants in computational experiments , such as those just described , we can determine putative functionally important motifs and structures that can be subsequently tested experimentally . RNAmutants could lead to important breakthroughs in our understanding of the remarkable combination of robustness and fragility of RNA structures [42] . Estimating how robust a secondary structure is to mutations can be of interest for the characterization of functional RNAs . Here , by sampling structures , we evaluate the conservation of the Rfam consensus structure in the k-mutant ensembles , and compare the results obtained from five different families of RNA from Hepatitis C and HIV viruses . These computational experiments highlight major differences between these RNA families and suggest potential application in RNA design . The method proposed here first samples 1 , 000 k-point mutant sequences and structures for 0≤k≤5 . To quantify robustness , we compute two notions of distance . First , for each sampled structure S , we compute the base pair distance between S and the native secondary structure S0 , and thus determine the average over all sampled structures , called average distance in the following . Second , we compute the base pair distance between S and the sample centroid Sc , where the latter is defined to consist of those base pairs occurring in strictly more than half the sampled structures . ( In work of Ding et al . [43] , the sample centroid is called the Boltzmann centroid , when sampling over all secondary structures using Sfold [24] . ) This distance is called the centroid distance in the following . A small average distance means that most sampled structures are identical to the native structure ( this entails a small centroid distance as well ) . A large average distance with a small centroid distance indicates that the core of the native structure is conserved in the sampled structures , while most sampled structures differ from the native structure with respect to a number of base pairs . In this case , the nonnative base pairs in the samples are not well conserved over the ensemble of sampled structures , hence do not appear in the centroid structure . In contrast , a large centroid distance indicates that the same nonnative base pairs are present ( or missing ) in the majority of sampled structures . To benchmark robustness , we used ( seed ) multiple sequence alignments from Rfam [9] . We selected five RNA elements associated with Hepatitis C and human immunodeficiency viruses , each of which is reasonably well predicted by the nearest neighbors energy model , using RNAmutants with 0 mutations , or ( equivalently ) mfold [22] or RNAfold [23] without dangles . The resulting dataset contains a total of 2 , 806 sequences . By native secondary structure , we mean the Rfam consensus structure from the multiple sequence alignment . Results are given in Table 1 . The structures sampled from the RNA elements of Hepatitis C virus are close to the native structure , while those of human immunodeficiency virus have more base pairs than the native structure . Nevertheless , the centroid structure for samples generated by RNAmutants is reasonably close to the native structure; i . e . , centroid distance for RNA elements from HIV is small . The Hepatitis C virus stem-loop IV ( HCV SLIV ) is accurately predicted by minimum free energy methods , i . e . , Zuker algorithm [14] , and despite its small size ( 35 nucleotides ) and large number of base pairs ( 15 ) , HCV SLIV is also very well conserved in the ensemble of mutants generated by RNAmutants . These results suggest that the RNA nucleotide sequence of HCV SLIV has been thermodynamically optimized and is robust with respect to mutations . In contrast , the secondary structure of sampled mutants of Hepatitis C virus cis-acting replication element ( HCV CRE ) is increasingly divergent as the number of mutations increases . The secondary structure of wild-type HCV CRE sequence is very well predicted by energy minimization methods . The centroid structure of samples generated by RNAmutants , for one to three mutations , changes little and remains very close to the native structure , even if most of the sampled structures have more base pairs than that of the native structure . However , when four or more mutations are allowed , another structure , significantly different from the native one , emerges from the ensemble generated by RNAmutants . This result suggests that HCV CRE has been optimized to resist only a few mutations . This remark suggests the use of RNAmutants to detect those sequences whose structure is locally optimized . At level 0 ( no mutation allowed ) , in spite of a higher average distance , the centroid structure of the ensemble of samples of HIV RNA elements remains close to the native structure . Interestingly , average distance remains approximately constant when the number of mutations increases . This number even decreases for human immunodeficiency virus primer binding site ( HIV PBS ) . Here again , analysis of mutants generated by RNAmutants seems to confirm the optimization of these sequences to support some functional secondary structure . We note that with similar characteristics ( length and number of base pairs ) the human immunodeficiency virus frameshit signal ( HIV FE ) structure appears to be more robust with respect to mutations than is the Hepatitis C virus cis-acting replication element ( HCV CRE ) . The centroid structure of human immunodeficiency virus primer binding site ( HIV PBS ) seems well conserved . In analogy to the phenomenon observed for the Hepatitis C virus cis-acting replication element ( HIV CRE ) , the average distance increase suggests that an alternate structure will emerge as the number of mutations increases . Summarizing , we feel that the combination of average and centroid distance measurements is a reasonable tool to estimate the robustness of a structure under mutational variation of a sequence . Remarkably , the average and centroid distances are very well conserved in human immunodeficiency virus gag stem loop 3 ( HIV GSL3 ) , in spite of the huge hairpin loop ( 69 nucleotides ) and very small stem ( 8 base pairs ) . One must bear in mind that Rfam consensus structures indicate only those base pairs that are inferred by covariation . It follows that many base pairs may not appear in the centroid structure , such as the 69-nt hairpin . Each of the 8 base pairs in HIV GSL3 is a GC pair , which means that this stem region is not optimized by RNAmutants for the small numbers of mutations k . This supports the idea that the mutation robustness of the hairpin loop sequence is optimized . In this section , we predict deleterious mutations in Hepatitis C virus cis-acting replication element using the method described in section Using sampling to predict deleterious mutations in Methods . We confirm our results by comparing our predictions with previously published experimental results [39] . Moreover , our computational experiments suggest new deleterious mutations which have not been predicted or tested before . We performed computational experiments with Hepatitis C virus cis-acting replication element ( HCV CRE ) , known to be essential for viral replication . Figure 6 depicts the secondary structure of HCV CRE . To validate our predictive results , we used mutagenesis data from experiments of You et al . [39] . To simplify exposition and enhance clarity of results , we focus our investigation on the prediction of single point deleterious mutations ( i . e . , kmax = 1 ) , although of course RNAmutants can be used to infer deleterious noncontiguous groups of mutation sites . Results are given in Figure 7 . The top line gives the native secondary structure of the RNA element , while the following lines contain 1-point mutants sampled by RNAmutants . For each pointwise mutant , we display the base pair associated with the mutation , the mutation type ( index and nucleotide substitution ) , the index and the type of the nucleotide that can be associated with the concerned base pair , the frequency of this mutation and the break number . Here , the HCV CRE sequence has a length of 47 nucleotides , which is slightly shorter than those given in the Rfam multiple alignment . Also , we note a shift of 53 positions between the index of our sequence and those used in [39] . Our results predict the mutation U33G ( U86G according to the notation used in [39] ) to be the most deleterious . This prediction is confirmed by [39] . In this study , You et al . observed that the mutant C84A/U86G is not viable , while C84A/U86A is still functional . Additionally , it was observed [39] that the mutation U86G is responsible for the alteration of the upper helix ( subsequence from nucleotide 8 to 31 in Figure 7 ) and hence deleterious . However , their results also showed that the single point mutation U86G is still viable , suggesting that this mutation must be supported by C84A to be deleterious . In fact , C84A is suspected to alter the stability of the upper helix , amplifying the ability of U86G to disrupt the structure . The slight overestimation of the deleterious potential of U86G is due to the quality of the energy model used by RNAmutants . Without dangles , the centroid structure is effectively altered by U86G , while with dangles , the mutation C84A is required to disrupt the upper helix ( data not shown ) . The difference is then due to the absence of dangles in the energy model of RNAmutants . However , the deleterious effect of U86G is correctly detected . The non-viability of other mutants studied in [39] ( U71C , C74U , A75U/G76C/C77U , C77U , C90A/A92G , A92G , and C90A ) is not attributed to a significant alteration of the native secondary structure . RNAmutants predicts a few other deleterious mutations ( with a lower impact ) —these are discussed in the following . The next four deleterious mutations can be grouped in a cluster involving the base pairs ( 11 , 35 ) and ( 11 , 36 ) . When looking at the 348 sequences in the Rfam seed alignment , it appears that 30 sequences have the mutation C36U , 3 the mutation C35U and 1 the mutation A11G . EMBL accession numbers for the Rfam sequences and the mutations found are shown in Table 2 . The 33 sequences mutating at index 35 and 36 have also several other significant mutations . Most of these mutations are similar . Assuming that these mutants are viable , this suggests that some of these additional mutations offset the deleterious effect of C35G or C36G . A complete analysis of all these sequences would be too demanding , but we can illustrate this phenomenon by looking at the 3 sequences associated with C35G . Three mutations ( A15U , C25G , and A39G ) are found simultaneously in all occurrences of C35U . The mutations C25G and A15U are located at the extremities of the hairpin loop in the native structure , and more specifically , C25G creates a potential base pair with nucleotide U at index 14 ( and potentially also with nucleotide U at index 15 ) . We conclude that these two mutations tend to stabilize the upper helix and counterbalance the deleterious effect of C35G . The role of A39G remains more obscure . In [39] , You et al . observed that this mutation ( A92G in the paper ) is lethal . However , structure probing did not reveal any irregularity in the cleavage product of this RNA , suggesting that the sequence of the bulge is affected rather than the global structure . A potential structural use of this mutation would be to prevent the creation of base pairs supporting the disruption of the upper helix through C35G . An analysis using the thermodynamic model with RNAmutants tends to support this hypothesis . An interesting case is for A11G which occurs , in a single sequence ( AF054264 . 1:326–376 ) from the Rfam seed alignment , together with A1G . This has been reported as one of the clones used in [44] . From this study , it remains unclear how replication is affected by these mutations; however , the possibility of a deleterious effect of A11G is potentiality ( indirectly ) supported by this work . The following group of predicted deleterious mutations involves the nucleotide C at position 29 , either directly ( C29G ) or indirectly through a base pair ( C19G , U16G , C12G ) . If no specific analysis has been performed for this mutation , it appears that C29G can be found in two nonviable mutants ( 5BSL3 . 2 mutA and 5BSL3 . 2 mutB ) in [39] . The deleterious nature of C29G has not been validated , but the destabilization effect of this mutation in the upper helix is suggestive . Other minor mutations which do not disrupt the native structure are identified . With a break number of 0 , these mutations cannot be considered as deleterious . However , some of them have been detected to alter replication ( C74U and C77U or C21U and C24U in our notation ) in [39] . One potential explanation suggested by our results is that the local structure of the hairpin loop is affected , rather than that of the global secondary structure . In this section , we show how RNAmutants can be used to detect regions of the sequence which have been optimized during evolution . We restrict mutations to a 3-nucleotide frame and slide the latter on sequences . The frames associated with an alteration of the functional structure in 3-mutants are most likely optimized to preserve the structure , and are thus identify under a purifying selective pressure . Our results reveal critical regions in the trans-activation response element of the human immunodeficiency virus and suggest applications for RNA drug design . For this study , we use sequences of human immunodeficiency virus trans-activation response ( HIV TAR ) from the HIV-1 genome . The Rfam seed alignment contains 426 sequences of length 57 nt with an average identity of 91% . This RNA element is critical for the trans-activation of the viral protomer and virus replication . The TAR hairpin acts as a binding site for the Tat protein and this interaction stimulates the activity of the long terminal repeat promoter . Previous studies have shown that the 3 nt bulge from index 22 to 24 is essential for binding [36] . Moreover , the 3D structure of the 6 nt apical loop ( index 29 to 34 ) is indispensable for trans-activation of the viral protomer and virus replication [37] . This RNA element is a potentially important drug target [38] . Its consensus secondary structure is shown in Figure 6 . We are interested in detecting regions which have been selected during evolution to preserve a specific pattern , for structural or functional purposes . For each sequence in the dataset , we slide an open frame and allow mutations in this region only . Then , we sample structures from this model , and measure the centroid and average distances . Here , the size of the open frame is chosen to fit the length of the bulge ( i . e . , three nucleotides ) . Larger frame sizes would result in an attenuation of the signal ( data not shown ) . For each starting position of the frame ( 1 to 55 ) , we compute the mean centroid distance and mean average distance for each sequence in the dataset . These curves are displayed in Figure 8 . The secondary structure annotation is given at the bottom of each of these three graphs ( one for each number of mutations in the open frame ) . The secondary structure can be decomposed into four distinct patterns which are: ( 1 ) a pairing ( denoted S1 for stem 1 ) between regions ( 17 , 21 ) and ( 39 , 43 ) , ( 2 ) a bulge at index ( 22 , 24 ) , ( 3 ) another pairing ( denoted S2 for stem 2 ) between regions ( 25 , 28 ) and ( 35 , 38 ) , and ( 3 ) a hairpin at index ( 29 , 34 ) . We look first at the curves with a single mutation inside the frame—see Figure 8A . A clear signal appears at index 35–36 and 40–41: Both curves ( average and centroid ) show a clear peak at these positions . The regions associated with this signal correspond exactly to the 3′-end regions involved in the two stems S1 and S2 . We observe a mirror effect when the frame matches the 5′-end regions; two other peaks emerge at index 18 and 25–26 . Interestingly , the magnitude of these two peaks is significantly lower than those of the first ones , indicating that the 3′ regions have been potentially under a higher selective pressure . When two mutations are allowed inside the frame ( see Figure 8B ) , the phenomenon observed above is amplified . The asymmetry between the two paired regions of S2 is almost cancelled , but not for those of S1 . In addition , a clear signal now appears when the frame matches the bulge . It may also be interpreted as a signal indicating that this region has been constrained along evolution . Finally , when three mutations are performed inside the frame ( see Figure 8C ) , the signals mentioned before can still be identified , but tend to be washed out by the noise . Indeed , when all positions in the frame mutate , the sequence is so denatured that the conservation of the secondary structure would require an optimization of the surrounding sequence . This remark is related to the observation given below for the hairpin region . Additionally , two clear peaks now appear when the frame matches the paired region of the stem S2 . This may be a correction of the weakness of the signal observed in the previous graph ( Figure 8B ) . It also confirms that both these regions may have been optimized to base-pair . For these three graphs , it is remarkable to notice that mutations inside the subsequence of the hairpin never really affect the global structure of the RNA element . It may be suggested that the sequence outside the hairpin has been optimized to prohibit any stable interaction with the central region in order to stabilize the secondary structure and facilitate the formation of the complex 3D motifs observed in [37] . According to these observations , four sequence optimizations may have been performed for these sequences . The first two are for the regions paired to each other through the stem S1 and S2 . This may be justified by the need for these sequences to pair to each other in order to stabilize the bulge and the hairpin lying between them . It also appears that the sequence of the bulge cannot tolerate two mutations . Our analysis suggests that evolutionary pressure has selected these nucleotides to facilitate the formation of the bulge required for the binding . Finally , the global structure does not seem to be affected by mutations inside the hairpin loop . As it has been said before , this suggests an optimization of the surrounding sequence to stabilize this loop and allow the formation of a complex 3D motif inside the apical loop . These results suggest that a method combining RNA binding predictors [45] , [46] and secondary structure prediction software [14] , [15] with RNAmutants could be a successful and promising approach for the prediction and design of functional RNAs . Scan of the 3′ UTR of GB virus C reveals how evolution shaped the sequence . We conclude the results section with a series of computational experiments on the 3′ UTR of the GB virus C ( GBV-C ) . By scanning this RNA sequence , we show how RNAmutants can provide evidence that different regions have been optimized to conserve RNA secondary structure even in the presence of pointwise mutations . In particular , we show that the sequence has been designed to prevent deleterious effects of mutations on the evolutionarily conserved stem-loops . This work suggests potential large-scale applications of RNAmutants for genome-wide scanning purposes . In recent years the structure of RNA viruses in the family of Flaviviridae has received particular attention [47] . Here , we focus on the 3′ UTR of the Hepatitis G virus ( GB virus ) , a single-stranded positive-strand RNA virus with GenBank/EMBL accession number AB013500 , whose secondary structure has been determined using both thermodynamics and evolutionarily information [48] . This 311 nt sequence has the advantage of containing a balanced number of nucleotides located within regions having an evolutionarily conserved secondary structure ( 167 nt ) , as well as outside of any region having conserved secondary structure ( 144 nt ) . The conserved secondary structure is composed of seven stem-loops numbered from SLI to SLVII . We aim to study how evolution shaped this sequence , and to provide some evidence that certain regions have been thermodynamically optimized . In a manner similar to that of Vienna Package program RNAplfold [49] , we scanned the 3′ UTR GBV-C RNA sequence with a moving window of fixed size , and analyzed the distribution of mutations and base pairs in k-mutant ensembles of each window . Sliding a window of size L over this sequence , we extracted 311−L+1 subsequences and ran RNAmutants to sample mutated sequences and their secondary structures . Here , we use the following notation . Let ω denote the complete sequence of the 3′ UTR of GBV-C ( length N = 311 ) , and let WiL denote the subsequence of size L starting at index i . Let SWiL ( k , ns ) denote the set of ns many k-mutant sequences and secondary structures computed from WiL . The full set of sequences scanned by RNAmutants is denoted by , and the sample set of ns k-mutants and structures computed from is denoted . The probability of a base pair ( i , j ) in is defined as the number of occurrences of ( i , j ) in the secondary structure samples divided by the number of samples computed for a sequence that can potentially form a base pair between indices i and j ( e . g . , WkL such that j−i<L ) . Formally ( 13 ) This measure , motivated by that from the Vienna Package program RNAplfold [49] , averages the frequency of occurrence of base pair ( i , j ) in the ensemble of k-point mutants , over all size L windows containing both i , j . For this set of computational experiments we chose a frame size L = 50 and chose the number ns of sampled k-mutant sequences and structures to be 1000 , for each k from 0 to 8 . These values were chosen to provide a good balance between the computation speed ( a bounded , yet somewhat deep search in mutation depth ) and maximal range j−i<L of base pair ( i , j ) . For comparison , the default value for window size used in RNAplfold is 70 . The first analysis aims to estimate the density of base pairs in the different regions—regions of evolutionarily conserved stems , denoted by stem region or inside region , and regions having no evolutionarily conserved stems , denoted by non-stem region or outside region . We clustered the base pair density values in five cases according to the location of each index i , j of base pair ( i , j ) : ( 1 ) i and j are two indices belonging to the same stem region , ( 2 ) i and j are in two different stem regions , ( 3 ) i is in a stem region and j in a non-stem region , ( 4 ) i is in a stem region and j is in a nonstem region , and ( 5 ) i and j do not belong to any stem region . Then , we plotted these base pair density values with respect to the number of mutations in samples . The results computed with the parameters given above ( L = 50 , ns = 100 , and 0≤k≤8 ) are shown in Figure 9 . Note that the count done in the denominator of Equation 13 respects the same classification constraints and ensures normalization of the estimator values . The figure shows very distinct behavior for base pairs occurring inside the same stem region ( 1 ) versus other possibilities ( 2–5 ) . As expected when no mutation is allowed ( i . e . , k = 0 ) , he base pair density appears to be higher for base pairs in stem regions . This means that these regions are more structured than the others . ( This argument does not suggest that nonstem regions are not structured but only that they are locally less optimized . ) However , when the number of mutations increases , all curves tend to reach an equilibrium , with approximately equal density for each of the five cases . While density for base pairs in the same stem , case 1 , decreases with an increasing number of mutations , density for the other four cases increases . This phenomenon suggests that selective pressure has been applied to ensure robustness of ( local ) structure in the 3′ UTR GBV-C RNA with respect to mutation . Putatively , an inflection in the curve of stem regions appears at roughly 4 mutations in the figure . This remark will take its importance later in the discussion . The next study aims to analyze the base pairing preferences of mutations regarding their location in the sequence . Using the same set of computational experiments , we investigated the distribution of base pairs ( i , j ) involving a mutation at one of their extremities ( i . e . , index i or j mutates ) . We computed the base pair probability mkL ( i , j ) restricted to these specific base pairs and normalized the results ( i . e . , we divided the base pair density by the number of mutations allowed in the sample set ) ( 14 ) Then , we clustered the results according to the same classification of base pairs as above and computed the base pair density in each cluster . Results are shown in Figure 10 . For clarity of discussion , in the left panel of this figure we plotted the curves associated with a mutation occurring in the stem region ( Figure 10A ) , while the right panel displays the curves associated with a mutation occurring outside the stem region ( Figure 10B ) . Figure 10A reveals that in the close neighborhood ( small number of mutations ) of the wild sequence , the mutations occurring in a stem region base-pair preferentially inside the same stem region . An increase in the number of mutations has very different consequences on the density of base pairs in the different clusters . In agreement with our previous observations , the number of mutations created inside the same stem region decreases . In contrast , if the densities increase in the two other cases , we observe a clear preference for creating a base pair outside any other stem region . Indeed , while the behavior of the two curves ( base-pairing in another distinct stem region , and outside ) have similar behavior for small number of mutations , it turns out that roughly beyond 4 mutations , more mutations tend to base-pair outside and “protect” as much as possible the cleavage between the stem regions . Symmetrically , when few mutations are performed outside the stem regions ( cf . Figure 10B ) , we observe a clear preference for base pairings in the same region , thus preserving the stems from destabilization by mutations occurring in the nonstem regions . However , in agreement with previous observations , larger numbers of mutations tend to progressively equilibrate the distributions by increasing the base pair density of mutations base pairing in stem regions . This observation suggests that non-stem regions have been constrained to prevent mutations from interacting with stems to disrupt the structure . We now investigate the distribution of mutations that increase the base pairing probability ( called base pair increasing mutations ) , versus those that decrease base pairing probability ( called base pair decreasing mutations ) . To evaluate the evolution of these probabilities from one level of mutation k to the next k+1 , we compare the local base pairing probabilities pk ( i , j ) computed from ( e . g . , sample set with k mutations ) with those computed from . Then , we estimate the difference pk+1 ( i , j ) −pk ( i , j ) , subsequently called the differential probability . We show the corresponding curves in Figure 11 , where the results have been once again classified into five clusters . The distribution of base pair increasing mutations ( cf . Figure 11A ) presents some interesting features . Indeed , when a single mutation is performed , we first observe a tendency to stabilize the structures already existing in and out the stem regions , thus conserving the existing structure of the full 3′ UTR GBV-C RNA sequence . However , afterward , an increased number of mutations tends to be more favorable to mutations strengthening the base pairs between a stem and a non-stem region . Simultaneously the probability of mutations favoring base pairs inside stem regions increases to a lesser extent . Interestingly , if the probability of base pair increasing mutations for bases occurring between two distinct stem regions seems also to increase for small values of k , it turns out that these probabilities tend to remain identical afterwards ( e . g . , the differential values decrease ) . The case of base pair decreasing mutations is in fact much more interesting since essentially only base pairs inside stem regions seem significantly affected by such mutations , although single mutations appear not to have any significant effect ( differential probability close to zero ) . The two next levels ( K = 2 , 3 ) present a remarkable peak which completely collapses for a further increasing number of mutations ( k≥4 ) . The negative values indicate that the probability of base pair decreasing mutations inside the stem are decreasing , and thus that stabilization occurs once a few mutations have been occurred to locally reorganize the structure . This clear signal could prove useful in detecting structured regions of a genome , and possibly help identify subsequences under evolutionary pressure . Interestingly , the change of sign of the differential base pairing probability in the same stem region happens for 4 mutations , which correlates with the putative inflection point in Figure 9 for the base pair density curve for the same cluster of base pairs . Finally , we study the distribution of mutations in the complete 3′ UTR GBV-C RNA sequence . In complement to the previous experiments performed with a frame size of 50 nucleotides and thus restricted to local considerations , we now also provide an insight on the influence of mutations , sampled from the Boltzmann k-point mutant ensemble , on the medium and long range base pairing by including statistics computed with larger frame sizes . Using the equation 14 , we estimate the mutation base pair probability in the sample set and derive the average mutation probability from these values . The average mutation probability at index i in a sample set of k-point mutants is defined as the sum of the mutation base pair probabilities mkL ( i , j ) ( i . e . , mkL ( i ) = Σj mkL ( i , j ) ) . Additionally , in order to investigate the influence of medium and long range base pairing on the mutation distribution , we also computed the values of mkL ( i ) restricted to base pairs ( i , j ) with |j−i|≥25 . Mutation profiles computed using this procedure are given in Figure 12 . The distribution of the mutations inside and outside stem regions is evaluated as the sum of the mutation probabilities mkL ( i ) in both regions normalized by the number of nucleotides in these regions ( 166 in stem regions and 144 outside ) . The numerical results given in Table 3 summarize these statistics for the general case as well as the case of mutations involved in a medium to long range base pair , i . e . , base pairs ( i , j ) whose extremities i , j are at a distance of at least 25 nucleotides . Average mutation rates for such medium to long range base pairs are depicted in Figure 12 . Since the threshold used to filter short range base pairs may seem arbitrary , for the sake of clarity of discussion , we include graphs representing the values obtained for all possible threshold values together with the ratio of samples satisfying the cut-off in the sample set . Figure 13 illustrates these statistics . The x-axis represents the minimal base pair length while the y-coordinates give the fraction of mutations in non-stem regions ( plain line ) and the fraction of samples satisfying the threshold ( dashed line ) . In this study , we used frame sizes of L = 50 , 100 , and 150 nucleotides and computed 1 , 000 samples with k = 1 mutation ( results with 2 mutations were also computed and produced the same results ) . Frame sizes larger than 150 nucleotides have not been considered since only few base pairs distanced at more than 150 nt appeared in our sample sets . ( See Figure 13 . As shown in the supplementary Figure 1 , the RNAfold dotplots of the full sequence confirmed this observation . ) The distribution of mutations between structured ( stem regions ) and nonstructured regions presents a small but significant bias in the general case . When the requirement on the minimal length of base pairs is applied , this signal is strong and surprisingly clear . This observation suggests that in the fitness model [29] , [50]–[52] , evolution has constrained medium and long range base pairing to favor mutations outside evolutionarily conserved stem regions . This remark automatically suggests the potential usefulness of RNAmutants in gene discovery based on clustering of RNAmutants statistics . This hypothesis is the subject of current research on larger scale studies .
|
Evolution is a central concept in biology . This phenomenon can be observed at all levels of the organization of life—from single molecules to multicellular organisms . Here , we focus our attention on the implication of evolution at the level of nucleic acid sequences . In this context , RNA sequences presumably have been optimized by evolution to achieve specific functions . These functions are supported by a structure that can be determined using thermodynamics-based models and energy minimization techniques . In this work , we develop efficient algorithms for predicting energetically favorable mutations and study their impact on the stability of the structure . We use these techniques to reveal sequences under evolutionary pressure and design new methods to predict lethal mutations . Applications of our tool lead to a better understanding of the mutational process of some key regulatory elements of two important pathogenic RNA viruses—human immunodeficiency virus and hepatitis C virus .
|
[
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] |
[
"computational",
"biology",
"biophysics/rna",
"structure"
] |
2008
|
Efficient Algorithms for Probing the RNA Mutation Landscape
|
Containment of Mycobacterium tuberculosis ( Mtb ) infection requires T cell recognition of infected macrophages . Mtb has evolved to tolerate , evade , and subvert host immunity . Despite a vigorous and sustained CD8+ T cell response during Mtb infection , CD8+ T cells make limited contribution to protection . Here , we ask whether the ability of Mtb-specific T cells to restrict Mtb growth is related to their capacity to recognize Mtb-infected macrophages . We derived CD8+ T cell lines that recognized the Mtb immunodominant epitope TB10 . 44−11 and compared them to CD4+ T cell lines that recognized Ag85b240-254 or ESAT63-17 . While the CD4+ T cells recognized Mtb-infected macrophages and inhibited Mtb growth in vitro , the TB10 . 4-specific CD8+ T cells neither recognized Mtb-infected macrophages nor restricted Mtb growth . TB10 . 4-specific CD8+ T cells recognized macrophages infected with Listeria monocytogenes expressing TB10 . 4 . However , over-expression of TB10 . 4 in Mtb did not confer recognition by TB10 . 4-specific CD8+ T cells . CD8+ T cells recognized macrophages pulsed with irradiated Mtb , indicating that macrophages can efficiently cross-present the TB10 . 4 protein and raising the possibility that viable bacilli might suppress cross-presentation . Importantly , polyclonal CD8+ T cells specific for Mtb antigens other than TB10 . 4 recognized Mtb-infected macrophages in a MHC-restricted manner . As TB10 . 4 elicits a dominant CD8+ T cell response that poorly recognizes Mtb-infected macrophages , we propose that TB10 . 4 acts as a decoy antigen . Moreover , it appears that this response overshadows subdominant CD8+ T cell response that can recognize Mtb-infected macrophages . The ability of Mtb to subvert the CD8+ T cell response may explain why CD8+ T cells make a disproportionately small contribution to host defense compared to CD4+ T cells . The selection of Mtb antigens for vaccines has focused on antigens that generate immunodominant responses . We propose that establishing whether vaccine-elicited , Mtb-specific T cells recognize Mtb-infected macrophages could be a useful criterion for preclinical vaccine development .
Unlike most disease-causing pathogens , Mycobacterium tuberculosis ( Mtb ) , the cause of tuberculosis ( TB ) , persists in humans because of its highly evolved ability to evade and subvert the host immunity [1] . Mtb subverts vesicular trafficking , prevents phagolysosome fusion , and replicates in an intracellular niche within macrophages , allowing it to evade detection by humoral immunity [2] . Mtb also delays the initiation and recruitment of T cell immunity to the lung , promoting the establishment of a persistent infection [3] . Despite these challenges , T cell immunity does occur and plays an essential role in controlling the infection in both mice and humans [3–5] . With 10 million new TB cases annually , an effective vaccine would offer a cost-effective way to prevent TB and attenuate this persistent global pandemic . Given the importance of T cells during host defense , strategies for TB vaccines largely aim at generating memory T cells rather than neutralizing antibodies . Most subunit vaccines incorporate immunodominant Mtb antigens , which elicit large T cell responses [6] . Several immunodominant antigens have been identified in the murine TB model , including Ag85a , Ag85b , CFP-10 , ESAT-6 and TB10 . 4 [7] . T cell responses to these antigens are also frequently detectable in Mtb-infected people , and these highly prevalent responses represent the basis for TB immunodiagnostic tests [8] . By incorporating these immunodominant antigens into vaccines , the expectation is that antigen-specific T cells will contain the infection before Mtb can establish a niche and evade host immunity [6] . T cell recognition of Mtb-infected macrophages is fundamental to containment of TB infection . Srivastava et al elegantly showed this by using mixed bone marrow ( BM ) chimeric mice made from wild type ( WT ) and major histocompatibility complex class II ( MHC class II ) deficient BM [9] . Following infection , polyclonal CD4+ T cells suppressed Mtb growth more efficiently in MHC class II-expressing cells than in MHC class II-deficient cells . This data convincingly argues that cognate recognition ( i . e . , T cell receptor ( TCR ) mediated recognition ) of infected cells by polyclonal CD4+ T cells limits bacterial growth . However , whether this protection comes from T cells recognizing immunodominant or subdominant antigens remains unknown . In fact , even though many presume that Mtb-infected cells present immunodominant antigens , the data validating this assumption is surprisingly inconsistent . While there is consensus that Mtb-infected cells present ESAT-6 , the data concerning Ag85b presentation is more complicated [10–13] . Ag85b240-254 elicits a CD4+ T cell response early after infection , but Mtb reduces Ag85b production within three weeks after in vivo infection [12] . Thus , while Ag85b240-254-specific CD4+ T cells can recognize dendritic cells ( DC ) from infected mice 14 days post infection [14] , there is little recognition of Mtb-infected cells by Ag85b240-254-specific CD4+ T cells in vivo by day 21 [12] . Furthermore , Mtb has other mechanisms to evade T cell recognition , including dysregulating MHC class II expression and inhibiting antigen presentation by stimulating antigen export by the infected antigen presenting cells ( APCs ) [1 , 12 , 13 , 15] . Whether the immunodominant antigens recognized by CD8+ T cells are presented by Mtb-infected macrophages remains unknown . Here , we investigated cognate T cell recognition of Mtb-infected macrophages by CD8+ T cells specific to the immunodominant antigen TB10 . 4 . TB10 . 4 ( EsxH ) is an ESAT-6-like protein secreted by the ESX-3 type VII secretion system , important in iron and zinc acquisition , and although its requirement for bacterial growth depends on the culture conditions , it is essential for Mtb growth in macrophages and virulence in vivo [16–18] . Following Mtb infection , TB10 . 4 is a target of CD4+ and CD8+ T cell responses in humans and mice [19–23] . In Mtb-infected mice , TB10 . 4 elicits immunodominant responses in both BALB/c and C57BL/6 mice , and 30–50% of lung CD8+ T cells are specific to single epitopes [19 , 20] . We previously isolated TB10 . 4-specific CD8 T cells using Kb/TB10 . 44−11 tetramers , followed by single cell sorting and PCR to identify the TB10 . 44−11-specific TCRs . These TCRs were cloned in retroviral vectors to produce retrogenic ( Rg ) mice [20] . Following transfer to Mtb-infected mice , naïve ( CD44loCD62Lhi ) TB10 . 44−11-specific Rg CD8 T cells became activated first in the LN and then trafficked to the lung , where they matured into effector CD8 T cells [20 , 24] . Whether these TB10 . 4-specific CD8+ T cells can mediate protection is unclear . Adoptive transfer of TB10 . 4-specific CD8+ T cells into Mtb-infected , immunocompromised mice reduces the bacterial burden and promotes host survival [20] . However , despite eliciting large numbers of TB10 . 4-specific CD8+ T cells , a vaccine incorporating the H-2 Kb-restricted epitope , TB10 . 44−11 , fails to protect mice from Mtb infection [24] . We hypothesize that the inability of TB10 . 4-specific CD8+ T cells to mediate protection is due to inefficient recognition of Mtb-infected macrophages . We used primary CD4+ and CD8+ T cells lines to investigate the recognition of Mtb-infected macrophages by T cells specific to Ag85b , ESAT-6 , or TB10 . 4 . Ag85b- and ESAT-6-specific CD4+ T cells recognized Mtb-infected macrophages , but under the same conditions , TB10-specific CD8+ T cells did not recognize infected macrophages or inhibit bacterial growth . This was true even upon examination of numerous conditions and permutations including length of infection , duration of T cell and macrophage co-culture , and multiplicity of infection . TB10 . 4-specific CD8+ T cells did recognize macrophages infected with recombinant Listeria monocytogenes expressing TB10 . 4 , but only if the bacilli could escape into the cytosol . However , overexpressing TB10 . 4 in Mtb did not confer recognition . Importantly , macrophages pulsed with irradiated bacteria efficiently cross-presented TB10 . 4 to CD8+ T cells , suggesting that live Mtb actively inhibited presentation . Interestingly , polyclonal CD8+ T cells specific for Mtb antigens other than TB10 . 4 recognized Mtb-infected macrophages in a MHC class I-restricted manner . Thus , while TB10 . 4-specific CD8+ T cells do not recognize Mtb-infected macrophages , there exist other CD8+ T cells that recognize subdominant antigens presented by Mtb-infected cells . Based on these data , we propose that TB10 . 4 is a decoy antigen: it elicits a massive and persistent CD8+ T cell response , which cannot recognize Mtb-infected macrophages . Such a decoy antigen may distract the CD8+ response from focusing on subdominant antigens presented by infected cells , leading to evasion from host immunity .
To study T cell recognition of Mtb-infected macrophages , we established antigen-specific T cell lines , which unlike T cell hybridomas , facilitate the study of T cell function as well as recognition . The TB10 . 44−11-specific CD8+ T cell line , referred to hereafter as TB10Rg3 , has a distinct TCR cloned originally from TB10 . 44−11-tetramer+ CD8+ T cells isolated from infected mice and expressed in retrogenic mice [20] . The Ag85b240-254-specific CD4+ T cell line , referred to hereafter as P25 cells , was derived from P25 TCR transgenic mice [25] . To confirm their antigen-specificity , we co-cultured the P25 or TB10Rg3 T cells with thioglycolate-elicited peritoneal macrophages ( TGPMs ) pulsed with or without their cognate peptides and then measured their expression of CD69 and Nur77 . While both CD69 and Nur77 are T cell activation markers , increases in Nur77 expression indicate TCR-mediated activation more specifically [26 , 27] . After co-culture with TGPMs pulsed with Ag85b240-254 peptide , Nur77 expression by P25 cells peaked after 2 hours ( Fig 1A and 1B ) , while CD69 expression continued to increase ( Fig 1C and 1D ) . TB10Rg3 T cells exhibited similar Nur77 and CD69 expression patterns after their co-culture with TGPMs pulsed with the TB10 . 44−11 peptide ( IMYNYPAM ) but not with a control peptide ( IMANAPAM ) ( Fig 1E–1H ) . Since the increase in Nur77 expression was transient , we next tested whether CD69 and IFNγ could be useful markers of antigen recognition for longer experiments . During 72 hours of co-culture with peptide-pulsed TGPMs , P25 and TB10Rg3 T cells continued to express CD69 and secreted IFNγ in a peptide dose-dependent manner ( Fig 1I–1L ) . P25 T cells also responded specifically to macrophages pulsed with Ag85b peptides and not TB10 . 4 peptides ( S1 Fig ) . These experiments show that P25 and TB10Rg3 T cells can recognize their cognate antigens presented by TGPMs , both in short-term and long-term co-culture assays . Given that a primary function of T cells during Mtb infection is to restrict bacterial growth , we determined whether these T cell lines could limit intracellular mycobacterial growth in vitro . We infected TGPMs with H37Rv , a virulent Mtb strain that expresses both TB10 . 4 and Ag85b in vitro [22 , 28] . To assess whether any bacterial growth inhibition observed was dependent on cognate recognition , we infected both MHC-matched ( i . e . , H-2b ) and mismatched ( i . e . , H-2k ) macrophages . T cells were added on day 1 post-infection , and the number of colony forming units ( CFU ) was assayed 96 hours later . In the absence of T cells , Mtb grew significantly ( p<0 . 01 ) ( Fig 2 ) . P25 T cells significantly inhibited intracellular bacterial growth in H37Rv-infected TGPMs ( p<0 . 0001 ) . Addition of Ag85b peptide to the infected macrophages did not further enhance the ability of P25 T cells to inhibit bacterial growth , suggesting that their activation was maximal . As expected , P25 T cells only inhibited bacterial growth in MHC-matched macrophages , indicating that growth inhibition mediated by T cells required cognate recognition under these conditions . In contrast , TB10Rg3 T cells did not inhibit bacterial growth ( Fig 2 ) . We considered whether the inability of TB10Rg3 to inhibit bacterial growth was due to a lack of recognition of the infected macrophages or a defect in the T cells’ effector functions . When Mtb-infected TGPMs were pulsed with the TB10 . 44−11 peptide for one hour prior to adding the T cells , TB10Rg3 T cells significantly reduced bacterial growth ( p<0 . 0001 ) ( Fig 2A ) . Thus , under the same conditions where P25 T cells significantly suppressed intracellular Mtb growth in a MHC-restricted manner , TB10Rg3 T cells failed to inhibit bacterial growth . We next considered whether the inability of TB10Rg3 to restrict intracellular bacterial growth was true for other TB10 . 44−11-specific CD8+ T cells . To obtain TCRs representative of other clonally expanded TB10 . 44−11-specific CD8+ T cells , TB10 . 44−11-tetramer+ CD8+ T cells from Mtb-infected C57BL/6 mice were single cell sorted , and both the TCR CDR3α and CDR3β regions were sequenced ( S2 Fig ) . Three TCRs representative of clonally expanded T cells ( TB10RgL , TB10RgR , and TB10RgQ ) were cloned ( S2 Fig ) . In addition , a fourth TCR ( TB10RgP ) , not previously identified by NexGen sequencing , was also cloned . All these TCRs responded specifically to the TB10 . 44−11 epitope and expressed TCRs distinct from TB10Rg3 ( S3 Fig ) . We generated T cell lines from these retrogenic mice , as described for TB10Rg3 . TB10RgP , TB10RgL , and TB10RgR did not inhibit bacterial growth ( Fig 2B ) . However , if the Mtb-infected macrophages were pulsed with the TB10 . 44−11 peptide before T cell addition , then TB10RgP , TB10RgL , and TB10RgR T cells all inhibited bacterial growth significantly ( Fig 2B ) . Since TB10 . 44−11-specific CD8+ T cells only inhibited bacterial growth when their cognate peptide was added to Mtb-infected macrophages , we conclude that , although they express the effector function required to restrict intracellular bacterial growth , these TB10 . 44−11-specific CD8+ T cells do not recognize Mtb-infected macrophages . To further investigate TB10Rg3 and P25 T cells recognition of Mtb-infected cells , we next investigated the kinetics of Mtb antigen presentation . After Mtb infection , TGPMs were cultured for various lengths of time before adding the T cells . To assay antigen presentation , we added the T cells for two hours and then measured Nur77 and CD69 ( see Fig 1 for kinetics ) . We first examined recognition on day 0 by adding T cells immediately after infection , and we infected macrophages at a high MOI ( average effective MOI of 1 . 57 to 1 . 62 ) to ensure that a large amount of bacterial and antigens may be present . P25 T cells recognized Mtb-infected macrophages based on the induction of Nur77 and CD69 ( Fig 3A and 3B ) . Under these conditions , there was no increase in Nur77 or CD69 expression by TB10Rg3 T cells ( Fig 3C and 3D ) . We next chose later time points , which might allow Mtb to adapt to the intracellular environment and potentially let the TB10 . 4 antigen accumulate . We first infected cells at a lower MOI ( average effective MOI of 0 . 2 to 0 . 8 ) . TB10Rg3 T cells were added to infected macrophages on days 1 , 3 , or 5 post-infection . Again , we did not observe any increase in Nur77 or CD69 expression ( Fig 3E and 3F ) . As a control for T cell health and function , we co-cultured TB10 . 44−11-peptide-pulsed- , uninfected-macrophages with the TB10Rg3 T cells and observed significant increases in their Nur77 and CD69 expression ( Fig 3 ) . We also considered whether a higher MOI may lead to more presentation . We infected macrophages at an average effective MOI of 1 . 65 to 5 . 98 and added TB10Rg3 T cells 1 or 2 days post infection . Again , we did not detect induction of Nur77 or CD69 expression when TB10Rg3 T cells were co-cultured with the highly infected macrophages ( S4 Fig ) . We did not examine recognition at high MOI on days later than day 2 because of excessive death of the macrophages . Instead , we explored an alternative approach to increasing antigen abundance , as described below . Despite assessing recognition on multiple days , we considered whether the short assay period ( i . e . 2 hours ) might not detect recognition of Mtb-infected macrophages by TB10Rg3 T cells , especially if presentation of TB10 . 4 is inefficient or asynchronous . Therefore , we used IFNγ production as a cumulative indicator of T cell activation during a 72-hour co-culture experiment . Since cytokine-driven activation ( e . g . , IL-12 , IL-18 ) can stimulate IFNγ production by T cells independently of TCR signaling , we used MHC-matched ( H-2b ) or mismatched ( H-2k ) TGPM to assess cognate recognition [27 , 29–31] . As the infectious dose ( MOI , multiplicity of infection ) increased , P25 T cells produced more IFNγ when co-cultured with MHC-matched , but not MHC-mismatched , Mtb-infected TGPMs ( Fig 3G ) . In contrast , TB10Rg3 T cells did not produce IFNγ when co-cultured with Mtb-infected TGPMs ( Fig 3H ) . As before , TB10Rg3 T cells produced IFNγ when co-cultured with uninfected macrophages pulsed with the TB10 . 44−11 peptide ( Fig 3H ) . We next used the TB10 . 44−11-specific CD8+ T cell lines TB10RgP , TB10RgL and TB10RgR to address whether TB10 . 44−11-specific CD8+ T cells other than TB10Rg3 can recognize Mtb-infected macrophages . While the TB10RgP , TB10RgL , and TB10RgR CD8+ T cell lines produced IFNγ when cultured with uninfected macrophages pulsed with the TB10 . 44−11 peptide , none produced IFNγ following a 72-hour co-culture with Mtb-infected macrophages ( Fig 3I ) . These data show that , regardless of the time point of T cell addition or the length of co-culture , P25 T cells , but not TB10 . 44−11-specific CD8+ T cells , recognized Mtb-infected macrophages , based on their increased Nur77 and CD69 expression as well as their IFNγ production . During in vivo infection , Mtb infects a variety of myeloid cells , and this diversity changes over the course of the infection [32–34] . We considered that lung myeloid cells from Mtb-infected mice are more physiologically relevant than TGPMs . Thus , we isolated MHC class II+ lung cells from Erdman-infected , RAG-1-deficient mice 4 weeks post-infection and tested their ability to present Mtb antigens to TB10Rg3 T cells . We used RAG-1-deficient mice because of the possibility that CD8+ T cells in the lungs of Mtb-infected , wild type mice may recognize and eliminate any lung cells presenting the TB10 . 4 antigen . Since Mtb downregulates Ag85b expression by 3 weeks post infection [11 , 12] , we used an ESAT-6-specific CD4+ T cell line derived from C7 transgenic mice , which we refer to as C7 T cells [10 , 35] . The immunodominant antigen ESAT-6 retains high levels of expression throughout infection and elicits a dominant CD4+ T cell response in C57BL/6 mice [11] . Due to the difficulty in obtaining large numbers of MHC class II+ cells from uninfected , RAG-1-deficient mice , we used TGPMs from age-matched , RAG-1-deficient mice as a source of uninfected , inflammatory macrophages . We stained C7 or TB10Rg3 T cells with 5uM of the proliferation dye eFluor450 ( eBioscience ) before co-culturing them with the lung myeloid cells . After 72 hours , we measured the T cell proliferation as a marker of T cell recognition . C7 T cells proliferated extensively when co-cultured with the infected lung myeloid cells but not when co-cultured with uninfected TGPMs ( Fig 4A and 4B ) . In contrast , TB10Rg3 T cells did not proliferate when co-cultured with the lung myeloid cells ( Fig 4C and 4D ) . To assess whether TB10Rg3 T cells could proliferate if TB10 . 4 was present , we pulsed the lung APCs with the TB10 . 44−11 peptide for 1 hour before adding the TB10Rg3 T cells . As predicted , TB10Rg3 T cells proliferated after 72 hours of co-culture with peptide-pulsed , lung myeloid cells ( Fig 4C and 4D ) . We considered the possibility that Mtb in lung myeloid cells may not grow well in vitro , leading to altered antigen abundance that could affect T cell recognition . To address this possibility , we measured the bacterial burden in the lung myeloid cells . There was a 3-fold increase in the bacterial numbers between the beginning ( d1 ) and the end ( d4 ) of the experiment , indicating that the bacteria remained viable ( Fig 4E ) . Together , these data indicate that , under the conditions in which C7 T cells recognized lung myeloid cells from Mtb-infected mice , TB10Rg3 T cells did not recognize these lung myeloid cells . We next investigated whether the location of the antigen might affect the presentation of TB10 . 4 since the MHC class I antigen presentation pathway primarily samples the cytosol , whereas Mtb is a classic phagosomal pathogen . TB10 . 4-specific CD8+ T cells are primed and expanded during Mtb infection , so the TB10 . 4 antigens must be cross-presented; however , whether Mtb-infected macrophages can competently cross-present mycobacterial antigens is unknown . We investigated these possibilities using △LLO or △ActA mutant strains of Listeria monocytogenes engineered to express the full length TB10 . 4 protein , hereafter referred to as △LLO . TB10 . 4 or △ActA . TB10 . 4 , respectively . Both are attenuated strains: the △LLO . TB10 . 4 mutant cannot escape from the vacuole , while the △ActA . TB10 . 4 mutant can escape from the vacuole but not from the cell . Hence , the TB10 . 4 protein made by the △LLO . TB10 . 4 strain will remain trapped in the phagosome , but the TB10 . 4 protein made by the △ActA . TB10 . 4 strain will gain access to the cytosol . TB10Rg3 T cells recognized △ActA . TB10 . 4-infected TGPMs based on an increased frequency of Nur77-expressing cells ( p<0 . 005 ) and the Nur77 MFI of all TB10Rg3 T cells ( p<0 . 005 ) ( Fig 5A–5C ) . Bafilomycin , which inhibits vacuolar acidification and impairs the entry of the △ActA . TB10 . 4 strain into the cytosol , diminished the frequency of Nur77-expressing cells ( p<0 . 005 ) and Nur77 MFI ( p<0 . 01 ) ( Fig 5A , top , Fig 5B and 5C ) . In contrast , TB10Rg3 T cells co-cultured with △LLO . TB10 . 4-infected TGPMs showed no increase in the frequency of Nur77-expressing cells or the Nur77 MFI ( Fig 5A bottom , Fig 5D and 5E ) . If recombinant listeriolysin ( rLLO ) , the protein missing from the △LLO . TB10 . 4 strain , was added to the infected macrophages , an increase in the frequency of Nur77-expressing TB10Rg3 T cells ( p<0 . 01 ) and the Nur77 MFI ( p<0 . 01 ) became apparent . We also determined whether P25 T cells recognized Ag85b-expressing Listeria monocytogenes using the recombinant Listeria strains △ActA . Ag85b and △LLO . Ag85b . Based on the propensity of MHC class II to present extracellular and vacuolar antigens , P25 cells recognized TGPMs infected with either △ActA . Ag85b or △LLO . Ag85b , based on an increase in the frequency of Nur77-expressing T cells and Nur77 MFI ( p<0 . 005 ) ( Fig 5G and 5H ) . These results show that 1 ) TGPMs can efficiently process the full length TB10 . 4 protein and present the TB10 . 44−11 epitope via MHC class I; 2 ) this process is more efficient when the bacteria are in the cytosol; and 3 ) TB10Rg3 T cells can efficiently recognize TB10 . 44−11 presented during a live infection . We considered several additional possibilities as to why the TB10Rg3 T cells did not recognize Mtb-infected macrophages . Antigen abundance can affect T cell recognition , so we next tested whether increasing the level of TB10 . 4 protein expression might enhance TB10Rg3 T cell recognition of Mtb-infected macrophages . Since Mtb secretes esxH ( TB10 . 4 ) together with esxG as a heterodimer [36] , we developed a recombinant strain of H37Rv ( esxGH-OE . Mtb ) , which overexpresses both esxG and esxH under the control of a tetON promoter . After tetracycline induction for 24 hours , we detected increased protein expression of TB10 . 4 ( esxH ) as well as increased mRNA expression of esxG and esxH ( Fig 6A and 6B ) . A second strain , Ag85b-OE . Mtb , was used to show the specificity of the anti-TB10 . 4 antibody . When Ag85b was induced , TB10 . 4 expression was unaltered ( Fig 6A ) . The expression of GroEL2 , a chaperonin protein , was used as loading control and for normalization of protein signal ( Fig 6A ) . When detecting the mRNA expression , we probed for sigA , which encodes for a Mtb RNA polymerase factor , and fbpB , which encodes for Ag85b , as well to verify that the esxGH-OE . Mtb strain specifically overexpressed esxGH and not other proteins ( Fig 6B ) . Prior to in vitro infection , we treated esxGH-OE . Mtb with or without tetracycline . The next day , TGPMs were infected with induced or uninduced esxGH-OE . Mtb . P25 T cells produced similar amounts of IFNγ when co-cultured with macrophages infected with either uninduced or induced esxGH-OE . Mtb , which was expected since Ag85b expression should not be altered ( Fig 6C ) . Despite increasing the production of TB10 . 4 by Mtb , TB10Rg3 T cells still did not recognize Mtb-infected macrophages ( Fig 6D ) . Although we cannot be certain that the induction of EsxGH leads to an increased amount of antigen delivered to the antigen processing pathway , this result suggests that antigen abundance is not limiting TB10 . 4-specific CD8+ T cell recognition of Mtb-infected macrophages . We also investigated whether Mtb may inhibit MHC class I expression by infected TGPMs . Mtb and TLR2 agonists inhibit IFNγ-induced MHC class II expression by bone marrow derived macrophages , and the mycobacterial PPE38 protein can inhibit MHC class I expression in RAW264 . 7 macrophages and TGPMs infected with Mycobacterium smegmatis [37 , 38] . Therefore , we asked whether Mtb impaired MHC class I expression in our in vitro infection system , especially since the TGPMs were not pre-activated with IFNγ prior to infection . We measured MHC class I and II expression by macrophages on each of the five days following infection . At baseline , uninfected TGPMs expressed high MHC class I , and Mtb infection did not alter MHC class I expression compared to the baseline ( Fig 6E and 6F; solid lines ) . IFNγ pretreatment of macrophages led to an increase in MHC class I expression by both uninfected and infected TGPMs , although the infected cells expressed slightly less MHC class I ( Fig 6E and 6F; dotted lines ) . As expected , the regulation of MHC class II was more sensitive to IFNγ . Uninfected TGPMs expressed low baseline levels of MHC class II ( Fig 6G and 6H; solid lines ) . IFNγ pretreatment resulted in a >100-fold increase in MHC class II median fluorescence intensity ( MFI ) in the uninfected TGPMs , which peaked on day 3 with a >2000-fold increase over the baseline ( Fig 6G and 6H; dotted lines ) . Mtb-infection alone did not significantly affect MHC class II expression , and consistent with previous studies , Mtb significantly impaired the induction of MHC class II by IFNγ pretreatment ( Fig 6G and 6H ) . These data show that , in our in vitro infection model where the TGPMs were unstimulated , Mtb infection did not inhibit class I and II MHC expression . Importantly , the differences in MHC class I or class II expression by Mtb-infected macrophages cannot explain why P25 T cells , but not TB10Rg3 T cells , recognized infected macrophages . Next , we hypothesized that Mtb may interfere with MHC class I presentation of mycobacterial antigens . Therefore , we tested the ability of the P25 and TB10Rg3 T cell lines to recognize TGPMs cultured with γ-irradiated , nonviable Mtb . Activation of pattern recognition receptors such as TLR2 and TLR4 by large amounts of dead bacteria might induce large amounts of IL-12 and IL-18 , resulting in cytokine-driven T cell activation . Taking this concern into consideration , we used MHC-mismatched TGPMs as a control . We pulsed macrophages with a dose titration of γ-irradiated Mtb , then added TB10Rg3 or P25 T cells , and measured IFNγ secretion by the T cells after 72 hours . Both P25 and TB10Rg3 T cells produced high amounts of IFNγ when cultured with MHC-matched ( i . e . , H-2b ) but not with MHC-mismatched ( i . e . , H-2k ) TGPMs , and this response was dose dependent ( Fig 6I and 6J ) . The ability of macrophages to process and present TB10 . 4 after phagocytosing γ-irradiated Mtb but not viable bacteria raises the possibility that live Mtb actively inhibit MHC class I presentation of TB10 . 4 . Along with the previous finding that TB10 . 44−11-specific CD8+ T cells make up ~40% of total lung CD8+ T cells during infection ( S5 Fig ) [20] , our finding that TB10Rg3 T cells do not recognize Mtb-infected macrophages suggests that TB10 . 4 may be a decoy antigen . This raises the question whether the inability to recognize Mtb-infected macrophages is a general feature of the CD8+ T cell response to Mtb , or if this is a unique feature of TB10 . 4-specific CD8+ T cells . Therefore , we determined whether polyclonal CD8+ T cells from the lungs of infected mice could recognize Mtb-infected macrophages . We carried out aerosol infection of C57BL/6 mice with Erdman , and , 6–8 weeks post infection , we purified polyclonal CD4+ or CD8+ T cells from their lungs and co-cultured them with Mtb-infected macrophages . After 72 hours of co-culture , polyclonal CD4+ T cells produced high amounts of IFNγ in a MHC-restricted manner ( Fig 7A ) . Interestingly , polyclonal CD8+ T cells also produced IFNγ in a MHC-restricted manner when co-cultured with Mtb-infected macrophages ( Fig 7B ) . Although the polyclonal CD8+ T cells were contaminated by less than the 1 . 5% CD4+ T cells , we conducted experiments using MHC I-/- macrophages and showed that the polyclonal CD8+ T cells produced significant IFNγ only in the presence of MHC I was intact , excluding the possibility that the IFNγ production was due to contaminating CD4+ T cells and showing that the Mtb-specific CD8+ T cells were Kb or Db restricted ( S6 Fig ) . These results indicate that other antigen-specific CD8+ T cells recognizing Mtb-infected macrophages do exist , and infected TGPMs can present Mtb antigens to CD8+ T cells . However , based on the high abundance of TB10 . 4-specific CD8+ T cells post infection ( S5 Fig ) , the non-TB10 . 4-specific , Mtb-specific CD8+ T cells may be dwarfed by the dominant TB10 . 4-specific CD8+ T cells . To better assess whether the IFNγ production by polyclonal CD8+ T cells arose predominantly from non-TB10 . 4-specific CD8+ T cells , we used the TB10 . 44−11-tetramer to separate TB10 . 4-specific and non-TB10 . 4-specific , polyclonal CD8+ T cells from the lungs of infected mice . After 72-hour co-culture with Mtb-infected macrophages , TB10 . 44−11-tetramer negative CD8+ ( non-TB10 . 4-specific CD8+ ) T cells produced significantly higher IFNγ compared to that of uninfected control ( p<0 . 005 ) , and the production was MHC class I restricted ( Fig 7C ) . In contrast , TB10 . 44−11-specific CD8+ T cells produced IFNγ in a non-MHC-restricted manner during co-culture with both uninfected and Mtb-infected macrophages ( Fig 7D ) . We cannot exclude the possibility that the tetramer isolation might have inadvertently activated the TB10 . 44−11-specific CD8+ T cells . Nevertheless , these data show that polyclonal , TB10 . 44−11-tetramer negative CD8+ T cells recognized Mtb-infected macrophages , supporting the notion of a subdominant T cell response that may be effective at detecting Mtb .
A complexity in defining T cell recognition is distinguishing cognate from non-cognate recognition . T cell IFNγ production , a common readout for recognition , can be stimulated by IL-12 and IL-18 , two cytokines secreted by Mtb-infected cells [27 , 29–31] . Even cognate recognition does not always signify recognition of infected cells . Uninfected macrophages and dendritic cells ( DCs ) can acquire exosomes , soluble proteins , apoptotic vesicles or necrotic debris containing non-viable bacilli or its antigens , and present these to T cells [13 , 39–41] . This detour pathway allows T cells to be activated by uninfected DCs [39 , 42] . Thus , T cell recognition of infected macrophages , which is central to our fundamental paradigm of TB pathogenesis , remains poorly defined . Our study advances the understanding of T cell recognition of Mtb-infected cells . By focusing on TCR-mediated recognition , our data show that T cells specific to immunodominant antigens vary in their ability to recognize Mtb-infected macrophages . Despite being a persistent and dominant population of CD8+ T cells in the lungs of Mtb-infected mice , TB10 . 44−11-specific CD8+ T cells do not recognize Mtb-infected macrophages . While we primarily used TGPMs , which have been used to model human macrophages [43 , 44] , we also showed that TB10 . 4-specific CD8+ T cells failed to recognize lung APCs from infected mice . Importantly , concurrent with our analysis of CD8+ T cells , we systematically assessed recognition of Mtb-infected macrophages by Ag85b-specific ( i . e . , P25 ) and ESAT-6-specific ( i . e . , C7 ) CD4+ T cells . Both recognized Mtb-infected macrophages and inhibited bacterial growth ( here and [10] ) . Thus , under conditions that activated Mtb-specific CD4+ T cells , no activation of TB10 . 4-specific CD8+ T cells occurred . This finding has many implications , among which the most important is that not all Mtb-specific T cells recognize Mtb-infected macrophages . These results led us to re-examine the evidence that CD8+ T cells recognize infected cells . In our evaluation of the literature , among the best evidence is: ( 1 ) direct ex vivo recognition of Mtb-infected macrophages and DC by CD4+ and CD8+ T cells [45–48]; ( 2 ) murine T cells’ cytolytic activity ( CTL ) of MTb-infected cells [49–51]; ( 3 ) human CD8+ T cells that recognize Mtb-infected DC [52–54] . However , these data have limitations . The murine studies never demonstrated cognate recognition , and the frequencies were lower than expected . The human studies only used DC and not macrophages and used a high MOI , raising concerns about death of infected cells and presentation of nonviable antigen . Nevertheless , these studies support the idea that CD8+ T cells recognize infected cells , but their frequency that recognize infected macrophages might be lower than we previously expected . Such a finding might explain why CD8+ T cells make a disproportionately small contribution to host defense , even though Mtb infection elicits a robust CD8+ T cell response . We investigated several mechanisms that might explain why TB10 . 4-specific CD8+ T cells do not recognize infected macrophages . One possibility is the access of the TB10 . 4 antigen to the MHC class I processing pathway . Mtb can disrupt the phagosomal membrane and translocate into the cytosol [55] , though this action often occurs later in infection and leads to necrosis of the macrophage [56] . We saw no evidence of recognition even at late time points such as days 4–5 post infection ( Fig 3 ) , when phagosomal disruption and bacterial translocation occurs [56] . The importance of antigen location became apparent during the Listeria infection experiments , where infected macrophages presented TB10 . 44−11 only when the bacteria could enter the cytosol ( i . e . , △ActA . TB10 but not △LLO . TB10 ) . The Listeria experiments also provided an additional insight . Lindenstrom et al report that vaccination with TB10 . 4 ( EsxH ) , which has a leucine at position 12 ( i . e . , IMYNYPAML ) , inefficiently generates TB10 . 4-specific CD8+ T cells [57] . However , vaccination with TB10 . 3 ( EsxR ) , a related antigen that also contains the same epitope followed by a methionine ( i . e . , IMYNYPAMM ) , elicits TB10 . 4-specific CD8+ T cells . Based on that finding , they conclude there is a processing defect that prevents the generation of the TB10 . 44−11 epitope from the TB10 . 4 protein . However , they also find that TB10 . 4-specific CD8+ T cells elicited by TB10 . 3 vaccination recognize splenocytes pulsed with the rTB10 . 4 proteins , showing that the full length TB10 . 4 protein can be processed and presented . These data indicate that the lack of vaccine-elicited TB10 . 4-specific CD8+ T cells is due to a problem with priming after vaccination instead of an inability to process the IMYNYPAM epitope . Moreover , our data using TB10 . 4-expressing Listeria show that TGPMs can process the full length TB10 . 4 protein and present the TB10 . 44−11 epitope . Therefore , we conclude that amino acid sequence of TB10 . 4 does not hinder its processing . The Listeria experiments also show the potential importance of antigen location and raise the possibility that sequestration of the TB10 . 4 antigen in the phagosome renders it inaccessible to the MHC class I presentation pathway . Low antigen abundance could also explain the lack of recognition . We have previously argued that there is limited amount of TB10 . 4 antigen presentation in the lungs of infected mice , leading to extreme bias in the TCR repertoire of the TB10 . 4-specific CD8+ T cell response and defects in the memory-recall response in vivo [20 , 24] . Importantly , bacterial TB10 . 4 levels are regulated by iron availability , and its expression varies in different growth medias [58] . Tinaztepe et al showed that , while H37Rv grown in 7H9 media reduced its TB10 . 4 expression in broth culture , the bacteria , during subsequent infection in BMDMs , required TB10 . 4 to remain virulent and survive in the BMDMs [58] . To test the possibility of low antigen abundance , we overexpressed EsxG and EsxH ( TB10 . 4 ) together but did not see greater T cell recognition of Mtb-infected macrophages , suggesting that abundance might not be the issue . Unexpectedly , macrophages pulsed with γ-irradiated Mtb were recognized by TB10 . 4-specific CD8+ T cells , raising the possibility that live Mtb actively inhibits MHC class I presentation of TB10 . 4 . This is particularly interesting since the presentation of CFP10 , another ESAT-6-like protein , by human DCs to CD8+ T cells requires viable Mtb; DCs given heat-killed bacteria do not present CFP10 to T cells [54] . While these data suggest that presentation requires active secretion of CFP10 [59 , 60] , the heat-killing process could have destroyed CFP10 , or there might not have been sufficient amounts of CFP10 available in the non-viable bacteria . In combination with our data showing polyclonal CD8+ T cells recognize Mtb infected macrophages , these data show that it is possible that certain antigens are presented by live Mtb while others are actively prevented from being sampled by MHC class I . A caveat to these studies is the amount of TB10 . 4 present in the γ-irradiated bacteria could differ from the live bacteria because of differences in the culture conditions . The H37Rv used for γ-irradiation was grown in the Glycerol-Alanine-Salt ( GAS ) minimal media whereas the H37Rv used for all other experiments was grown in 7H9 media supplemented with OADC , Tween80 and glycerol . As both media contain iron ( from the FBS in 7H9 media or from the ferric ammonium citrate in GAS media ) , and that iron is the key regulator of TB10 . 4 expression , we do not expect that differences in the culture media would interfere with the outcomes of the experiments . Ag85b is an immunodominant antigen with an epitope recognized by CD4+ T cells in C57BL/6 mice . In vivo data shows that Ag85b-specific CD4+ T cells can recognize Mtb-infected cells early during infection; however , recognition decreases after infection is established [12 , 14 , 15 , 61 , 62] . The inability of Ag85b-specific CD4+ T cells to efficiently recognize Mtb-infected bone-marrow derived macrophages ( BMDMs ) or bone-marrow derived dendritic cells ( BMDCs ) stems from a combination of reduced Ag85b expression by Mtb and because infected cells actively export Ag85b into the extracellular milieu [12 , 13] . In our experiments , we found that P25 T cells recognized Mtb-infected macrophages and inhibited bacterial growth in a MHC-restricted manner . A difference between the studies is the duration of macrophage and T cell co-culture . Grace et al examined 16–24 hours and found a lack of recognition , whereas our assays focused on 72–96 hours and detected recognition . Moreover , it is unknown whether Mtb-infected cells still exported antigens after the initial 24 hours of infection . Furthermore , the exported Ag85b could be taken up by infected cells during longer co-cultures , leading to their recognition by T cells . Finally , it is possible that cognate recognition of uninfected cells that present Ag85b could activate CD4+ T cells in a TCR-mediated manner , inducing IFNγ and indirectly controlling Mtb replication in macrophages . Nonetheless , under conditions that activate Mtb-specific CD4+ T cells , we could not observe activation of TB10 . 4-specific CD8+ T cells . The TB10 . 44−11 epitope has been extensively used to characterize CD8+ T cell responses in the mouse model of TB , and TB10 . 4-specific CD8+ T cell responses have also been characterized in people with tuberculosis [20 , 22 , 24 , 57 , 63–65] . The finding that TB10 . 4-specific CD8+ T cells do not recognize infected macrophages was unexpected , particularly since TB10 . 4-specific CD8+ T cells persist in the lungs of infected mice and become more dominant with time [19 , 20] . From these experiments , two questions warrant further investigation: 1 ) whether the CD8+ T cells specific to other epitopes of TB10 . 4 also inefficiently recognize infected macrophages , and 2 ) whether the species or the host genetic background influence recognition of infected cells . In retrospect , our findings may partially explain why eliciting TB10 . 4-specific CD8+ T cells by vaccination fails to protect mice against Mtb infection [24 , 57] . While vaccination with immunodominant antigens recognized by CD4+ T cells ( e . g . , Ag85b , ESAT-6 ) induce moderate protection [66 , 67] , we must consider the possibility that these antigens may not be the best stimulators of protective immunity . Ag85b-specific CD4+ T cells have variable efficacy , in large part due to its reduced expression by the bacterium as early as 3 weeks after infection [11 , 12] . However , by their nature , the recruitment of memory T cell responses specific for immunodominant antigens is only incrementally faster than the primary T cell response [10 , 24] . Thus , a crucial question for vaccine development is whether other Mtb antigens resemble TB10 . 4 , in that they elicit T cell responses that fail to recognize infected macrophages . We did detect polyclonal CD8+ T cells that recognized Mtb infected macrophages , corroborating a previous study showing that polyclonal CD8+ T cells from infected mice can lyse Mtb-infected cells [49] . These data indicate that there are antigens presented by Mtb-infected cells , even if those antigens may be subdominant compared to TB10 . 4 . Thus , future vaccine developments will benefit by identifying antigen targets based on their ability of being presented rather than only their immunogenicity . Priming of TB10 . 4-specific CD8+ T cells occurs early after Mtb infection in the lung draining lymph node ( LN ) [24 , 68] . Yet it is unknown whether priming of naïve T cells occurs via Mtb-infected DCs , or uninfected DCs that acquire antigen through uptake of apoptotic blebs containing Mtb proteins [39 , 42] , or by the transfer of antigen from cell to cell [41] . Priming by an uninfected cell can have detrimental consequences if the infected cell presents a different repertoire of Mtb antigens . Considering our findings , we propose that TB10 . 4 is a decoy antigen: TB10 . 4-specific CD8+ T cells expand in the LN during priming , accumulate in the lungs , but ineffectively recognize Mtb-infected macrophages . This raises the hypothesis that not all immune responses elicited by Mtb provide benefits to the host . Interestingly , Mtb genes encoding epitopes recognized by T cells are more highly conserved than other DNA elements , implying that T cell recognition of these Mtb epitopes may provide a survival advantage to the bacterium [69 , 70] . For example , T cell dependent inflammation may benefit Mtb by promoting transmission . Even though TB10 . 4 is more variable than most other antigens , our results support these genetic data [69 , 70] . Thus , Mtb focuses the CD8+ T cell response on the decoy antigen TB10 . 4 and distracts the immune response from other antigens that might be targets of protective immunity , successfully evading T cell immunity and enabling it to establish itself as persistent infection . Although we do not yet know whether the phenomenon we have described for TB10 . 4 will be true for other antigens , our data do reveal the hazards of selecting vaccine antigens based on immunogenicity and immunodominance alone . We propose that greater consideration should be given for the ability vaccine antigens to elicit T cells that recognize infected cells .
Studies involving animals were conducted following relevant guidelines and regulations , and the studies were approved by the Institutional Animal Care and Use Committee at the University of Massachusetts Medical School ( Animal Welfare A3306-01 ) , using the recommendations from the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and the Office of Laboratory Animal Welfare . C57BL/6J , Rag-1-deficient ( B6 . 129S7-Rag1tm1Mom ) , B10 ( C57BL/10J ) , B10 . BR ( B10 . BR-H2k2 H2-T18a/SgSnJJrep ) , P25 ( C57BL/6-Tg ( H-2Kb-Tcrα , Tcrβ ) P25Ktk/J ) [25 , 71] , mice were obtained from Jackson Laboratories ( Bar Harbor , ME ) . C57BL/6J and B10 mice were used for isolating MHC-matched TGPMs while B10 . BR mice were used for isolating MHC-mismatched TGPMs . C57BL/6 Kb-/-Db-/- ( MHC I-/- ) mice were a generous gift from Dr . Kenneth Rock ( University of Massachusetts Medical School , MA ) . C7 TCR transgenic mice were a generous gift from Dr . Eric Pamer ( Memorial Sloan Kettering Cancer Center , NY ) [35] . Thioglycolate-elicited peritoneal macrophages were obtained 4–5 days after intra-peritoneal injection of mice with 3% thioglycolate solution , as described [29] . 1×105 macrophages were plated per well . Macrophages were maintained in culture with RPMI 1640 media ( Invitrogen Life Technologies , ThermoFisher , Waltham , MA ) supplemented with 10 mM HEPES , 1 mM sodium pyruvate , 2 mM L-glutamine ( all from Invitrogen Life Technologies ) and 10% heat-inactivated fetal bovine serum ( HyClone , GE Healthcare Life Sciences , Pittsburgh , PA ) , referred hereafter as supplemented complete media . Retrogenic mice expressing TB10Rg3 TCR specific for the TB10 . 44−11 epitope were generated as previously described [20] . The TB10Rg3 CD8+ T cells were isolated from these mice , stimulated in vitro with irradiated splenocytes pulsed with the peptide TB10 . 44−11 in complete media containing IL-2 . P25 or C7 CD4+ T cells were isolated from transgenic P25 or C7 mice , respectively [25 , 35] . The P25 and C7 cells were stimulated in vitro with irradiated splenocytes pulsed with the Ag85b240-254 peptide or the ESAT-63−17 , respectively , in complete media containing IL-2 and anti-IL-4 . After the initial stimulation , these T cells were split every two days for 3–4 divisions and rested for two to three weeks . After the initial stimulation , the cells were cultured in complete media containing IL-2 and IL-7 . The following synthetic peptide epitopes were used as antigens: TB10 . 44−11 ( IMYNYPAM ) ; Ag85b240-254 ( FQDAYNAAGGHNAVF ) ; and ESAT-63−17 ( EQQWNFAGIEAAASA ) . We also generated a negative control peptide predicted to not bind to H-2 Kb: IMANAPAM . The peptides were obtained from New England Peptides ( Gardner , MA ) . As positive controls for assessing the function of macrophages to present antigen , uninfected macrophages and , in certain experiments , infected macrophages were pulsed with the peptides of interest . We pulsed macrophages by incubating 10uM of the peptides of interest with the macrophages in supplemented complete RPMI 1640 media for 1 hour . After incubation , the cells were washed 3 to 5 times with fresh supplemented complete RPMI 1640 media . The cells were then resuspended in supplemented complete RPMI 1640 media for experiments . H37Rv was grown as previously described [29] . Bacteria was grown in BD Difco Middlebrook 7H9 ( Thermo Fisher , Waltham , MA ) supplemented with 10% OADC ( Sigma-Aldrich , St . Louis , MO ) , 0 . 2% glycerol and 0 . 05% Tween-80 ( both from Thermo Fisher ) to an OD600 of 0 . 6–1 . 0 , washed in RPMI , opsonized with TB coat ( RPMI 1640 , 1% heat-inactivated FBS , 2% human serum , 0 . 05% Tween-80 ) , washed again and filtered through a 5 micron filter to remove bacterial clumps . The bacteria were counted using a Petroff-Hausser chamber . Infection was performed as previously described [29] . The final multiplicity of infection ( MOI ) , based on plating CFU , was 0 . 2–0 . 8 for all experiments , unless otherwise indicated . For CFU-based , bacterial growth inhibition assays , T cells were added at a ratio of 5 T cells to each macrophage . Four replicate wells were used for each condition . Cell cultures were lysed by adding 1/10th volume of with 10% Triton X-100 in PBS ( final concentration of 1% ) , and CFUs were determined by plating in serial dilutions of the lysates on Middlebrook 7H10 plates ( Hardy Diagnostics , Santa Maria , CA ) . CFUs were enumerated after culture for 21 days at 37°C and 5% CO2 . Aerosol infection of mice was done with the Erdman strain of Mtb using a Glas-Col aerosol-generation device . A bacterial aliquot was thawed , sonicated for 1 minute and then diluted in 0 . 9% NaCl-0 . 02% Tween-80 to 5 ml . The number of Mtb deposited in the lungs was determined for each experiment , by plating undiluted lung homogenate from a subset of the infected mice within 24 hours of infection . The inoculum varied between 37–120 CFU . For the ex vivo APC experiments , lung cells were isolated from Erdman-infected , RAG-1-deficient mice , 4-weeks post-infection , and the APCs were enriched by positive selection using anti-MHC class II+ microbeads ( Miltenyi Biotec , Bergisch Gladbach , Germany ) and the Miltenyi AutoMACS . On average , the isolated cells were 89% CD11c+ or CD11c+CD11b+ . The APCs were counted on a hemocytometer and plated at 1x105 per well in supplemented complete RPMI 1640 media . For the ex vivo CD4+ and CD8+ T cell experiments , single cell suspensions were isolated from the lungs of infected C57BL/6 mice , 6 to 8 weeks post-infection , as described [10] . Polyclonal CD4+ and CD8+ T cells were enriched by positive selection using Mouse CD4+ and Mouse CD8+ T cell isolation kits , respectively ( Miltenyi Biotec ) . After enrichment , average purity for polyclonal CD4+ and CD8+ T cells were 93% and 95% , respectively . For experiments investigating TB10 . 44−11-tetramer positive cells and polyclonal , non-TB10 . 4-specific , CD8+ T cells , the following isolation was done . Single cell suspensions from the lungs of infected mice were incubated with APC-conjugated , TB10 . 4−4-11-loaded , H-2b tetramers from the National Institute of Health Tetramer Core Facility ( Emory University Vaccine Center , Atlanta , GA ) . Tetramer positive CD8+ T cells were then selected via the AutoMACS separator by anti-APC microbeads ( Miltenyi Biotec ) . Average purity of TB10 . 44−11-tetramer positive , CD8+ T cells was 85% , with 1 . 4% contaminating CD4+ T cells . The tetramer negative population was subsequently washed and then enriched for polyclonal CD8+ T cells via Mouse CD8+ T cell isolation kit ( Miltenyi Biotec ) . Average purity of polyclonal , non-TB10 . 44−11-tetramer positive , CD8+ T cells was 75% with 0 . 8% contaminating CD4+ T cells and 13% contaminating TB10 . 44−11-tetramer positive CD8+ T cells . The T cells were counted using a hemocytometer and resuspended in supplemented complete RPMI 1640 media before being used in experiments . The recombinant Listeria strains have been previously described [24] . For in vitro infections , they were grown to an OD600 of 0 . 6–1 . 0 in BHI media ( Sigma Aldrich ) with 10 ug/ml chloramphenicol ( Sigma Aldrich ) at 30°C . Macrophages were infected with the Listeria strains using a MOI 50 , for 45 minutes . Extracellular bacteria were eliminated by adding 60 ug/ml gentamicin ( Sigma Aldrich ) for 20 minutes . Bacterial burden was assessed by lysing the infected macrophages with 1% TritonX-100 in PBS , and plating serial dilutions of the lysate on BHI agarose supplemented with 10ug/ml chloramphenicol ( Sigma Aldrich , St . Louis , MO ) . Recombinant listeriolysin ( Prospec , East Brunswick , NJ ) was added in some experiments at 2 ug/ml for 30 minutes , and any excess was washed away . Bafilomycin ( InvivoGen , San Diego , CA ) was added in some experiments at 5 uM for 30 minute , before being washed away . pJR1103 was cleaved with EcoRI-HF and SalI-HF [72] . mCherry preceded by the groEL2 promoter from H37Rv was inserted by HiFi Assembly . The resulting plasmid was cleaved with NdeI and NotI-HF . The esxGH gene from H37Rv , along with 12 upstream nucleotides , was inserted by HiFi Assembly following the plasmid-borne tetracycline-inducible promoter . All enzymes used above were purchased from New England Biolabs . The resulting plasmid ( pGB6 ) was electroporated into Mtb H37Rv and integrated at the L5 site . RNA was purified from induced and uninduced cultures using TRIzol ( ThermoFisher ) and chloroform extraction , followed by purification on Zymo columns . cDNA was produced with Superscript IV ( ThermoFisher ) , and quantitative PCR was performed using the iTaq SYBR Green Supermix ( Bio-Rad , Hercules , CA ) on an Applied Biosystems Viia 7 thermocycler . For confirmation of overexpression of TB10 . 4 ( esxH ) , western blot was performed . Strains were induced in mid log phase with anhydrotetracycline ( ATc ) for 24 hours . Bacterial pellets were washed and resuspended in PBS + protease inhibitor , then heat-inactivated for 2h @ 80–90°C and lysed by bead beating . Lysates were clarified by centrifugation and separated by electrophoresis ( ~2 μg protein per lane ) . Proteins were transferred onto nitrocellulose and probed with primary antibodies against TB10 . 4 ( esxH ) at 1:400 concentration ( Antibodies-Online , Atlanta , GA ) and GroEL2 ( Hsp60/65 ) at 1:1000 concentration ( GeneTex , Irving , CA ) . Anti-rabbit 680 ( for esxH ) and anti-mouse 800 ( for GroEL2 ) were used at 1:15 , 000 concentration ( both from LI-COR Biosciences , Lincoln , NE ) . PageRuler Plus Prestained ( ThermoFisher ) protein ladder was used . Antibody incubations were done in MB-070 blocking buffer ( Rockland , Limerick , PA ) . Infrared detection was done on a LI-COR Odyssey CLx . Quantification was performed using LI-COR ImageStudio software . Strain fbpB . OE-Mtb is a Mtb strain made to overexpress fbpB ( Ag85b ) protein and acts as a specificity control as ATc treatment of this strain is not expected to increase the TB10 . 4 protein . The following reagent was obtained through BEI Resources , NIAID , NIH: Mycobacterium tuberculosis , Strain H37Rv , Gamma-Irradiated Whole Cells , NR-14819 . The irradiated H37Rv was gently sonicated using a cup-horn sonicator at a low power to disperse bacterial clumps while limiting bacterial lysis . The number of bacteria , or antigen load , was approximated by measuring the turbidity at OD600 , and correlating it with live H37Rv ( OD600 = 1 is equivalent to 3 . 0x108 CFU/ml ) . To pulse TGPMs , diluted , sonicated , γ-irradiated H37Rv were added to adherent macrophages for one hour before repeatedly washing the cultures to remove residual extracellular bacteria . Subsequently , TB10Rg3 or P25 T cells were added at a ratio of 1 T cell to 1 macrophages . After 72 hours , the amount of IFNγ in the supernatants was measured using Mouse IFNγ ELISA MAX kits ( Biolegend , San Diego , CA ) . The following cell surface antigens were detected by flow cytometry using the following antibodies: mouse CD4 ( clone GK1 . 5 ) , CD8 ( clone 53–6 . 7 ) , CD3ε ( clone 145-2C11 ) , CD69 ( clone H1 . 2F3 ) , I-A/I-E ( clone M5/114 . 15 . 2 ) , and H-2Kb ( clone AF6-88 . 5 ) ( all from Biolegend ) . BV421- and APC-conjugated , TB10 . 44−11-loaded , H-2Kb tetramers were obtained from the National Institutes of Health Tetramer Core Facility ( Emory University Vaccine Center , Atlanta , GA ) . Zombie Violet Fixable viability dye ( Biolegend ) or the Live/Dead Fixable Far Red Dead Cell stain ( ThermoFisher ) were used for distinguishing live from dead cells . To stain for the Nur77 transcription factor , the Nur77 monoclonal antibody ( clone 12 . 14 ) was used in combination with the Foxp3 Transcription Factor Staining Buffer Set ( both from ThermoFisher ) by the manufacturer’s protocol . Live/dead viability staining and surface staining were done for 20 minutes at 4°C , and intracellular staining was done for 30 minutes at room temperature . Samples were then fixed with 1% paraformaldehyde/PBS for 1 hour before being analyzed by a MACSQuant flow cytometer ( Miltenyi Biotec ) . FlowJo Software ( Tree Star , Portland , OR ) was used to analyze the collected data . Single lymphocytes were gated by forward scatter versus height and side scatter for size and granularity , and dead cells were excluded . To compare the cellular expression of Nur77 and CD69 expression levels between time points , the MFI values were normalized as follows: experimental values were divided by the difference between the isotype control MFI ( minimum response ) and the peptide control MFI ( maximum response ) . Each figure represents a minimum of 2 similar experiments , with 2 to 4 biological replicates in each experiment . Data are represented as mean ± standard error of the mean ( SEM ) . For comparing two groups , a two-tailed , unpaired student’s t-test was used . For more than two groups , the data were analyzed using a one-way ANOVA . A p value < 0 . 05 was considered to be statistically significant . Analysis was performed using GraphPad Prism , Ver . 7 ( GraphPad Software , La Jolla , CA ) .
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Immunodominant antigens elicit a large majority of T cells during an infection , and it is presumed that these T cells go on to recognize infected cells . Immunodominant antigens produced by Mycobacterium tuberculosis ( Mtb ) have been incorporated into vaccines , but whether T cells specific for these antigens recognize Mtb-infected cells is inconsistent . One of these is TB10 . 4 ( EsxH ) , and after aerosol infection in mice , up to 40% of lung CD8 T cells recognize TB10 . 4 . Vaccination with TB10 . 44−11 peptide elicits a robust response and TB10 . 4-specific memory CD8 T cells develop . However , these mice are not protected against Mtb challenge . In trying to understand why this vaccine fails , we discovered that TB10 . 4-specific CD8 T cells do not recognize Mtb-infected macrophages in vitro . In contrast , under identical conditions , Ag85b-specific CD4 T cells recognize Mtb-infected macrophages and inhibit Mtb growth . In contrast , polyclonal CD4 and CD8 T cells from the lungs of infected mice recognize Mtb-infected macrophages , suggesting macrophages present antigens other than the immunodominant TB10 . 4 antigen . Thus , we conclude that TB10 . 4 is a decoy antigen that elicits a dominant CD8 T cell response that poorly recognizes Mtb-infected macrophages , and allows Mtb to evade CD8 immunity . Instead , this response may benefit Mtb by promoting inflammation . We propose that the poor ability of CD8 T cells to mediate protection might arise because the dominant T cells are unable to recognize infected macrophages . Identifying antigens that are presented by infected cells should be prioritized , as their inclusion in vaccines may enhance the expansion of T cells that mediate protection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"blood",
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"macrophages",
"organisms"
] |
2018
|
Mycobacterium tuberculosis-specific CD4+ and CD8+ T cells differ in their capacity to recognize infected macrophages
|
Crimean-Congo hemorrhagic fever virus ( CCHFV ) causes severe acute human disease with lethal outcome . The knowledge about the immune response for this human health threat is highly limited . In this study , we have screened the glycoprotein of CCHFV for novel linear B-cell epitopic regions using a microarray approach . The peptide library consisted of 168 synthesized 20mer peptides with 10 amino acid overlap covering the entire glycoprotein . Using both pooled and individual human sera from survivors of CCHF disease in Turkey five peptide epitopes situated in the mucin-like region and GP 38 ( G15-515 ) and GN G516-1037 region of the glycoprotein were identified as epitopes for a CCHF immune response . An epitope walk of the five peptides revealed a peptide sequence located in the GN region with high specificity and sensitivity . This peptide sequence , and a sequence downstream , reacted also against sera from survivors of CCHF disease in South Africa . The cross reactivity of these peptides with samples from a geographically distinct region where genetically diverse strains of the virus circulate , enabled the identification of unique peptide epitopes from the CCHF glycoprotein that could have application in development of diagnostic tools . In this study clinical samples from geographically distinct regions were used to identify conserved linear epitopic regions of the glycoprotein of CCHF .
Crimean-Congo hemorrhagic fever virus ( CCHFV ) is a tick-borne viral zoonosis distributed in Africa , Asia , eastern Europe and the Balkans . Ticks belonging to the genus Hyalomma are considered the principal vectors and the broad geographic distribution of the virus correlates with that of the vector [1 , 2] . More recently the virus has been identified as a cause of human disease in Spain [3] , Greece [4] , and India [5] . There is growing concern that the virus could emerge in other southern European countries which are within the distribution range of the vector [6 , 7] . The virus causes a disease that ranges in severity for reasons that are not clear [1 , 2] . Fatality rates vary depending on the severity of the disease and can be as high as 30% in some countries . Current diagnosis of CCHFV is based on the detection of viral RNA using RT-PCR , isolation of the virus and/or IgM/IgG detection [8 , 9] . Hence the detection of an antibody response against CCHFV is important for diagnosis as well as seroprevalence studies . Currently , most reagents used for development of diagnostic tools are dependent on culturing the virus within the confines of a biosafety level 4 facility . The biosafety considerations limit the number of laboratories which are able to prepare reagents [10] . The current perceived risk of spread of the virus to non-endemic regions highlights the importance of increasing diagnostic capacity and serological surveillance using safe , standardized reagents [9] . The viral genome consists of three RNA segments designated small ( S ) , medium ( M ) and large ( L ) . The S segment encodes the nucleocapsid , while the L segment translates into the RNA polymerase . The M segment encodes for a glycoprotein precursor that is post-translationally cleaved and generates mature GN and GC and a mucin-like domain [11] . Serological assays have been developed based on the recombinant nucleocapsid protein [8] but the use of glycoproteins has not been extensively investigated possibly due to the inherent challenges associated with preparing recombinant glycoproteins . However , peptides mimicking epitopic regions could have a potential as diagnostic tools and if the epitopes induce protective immunity they could play a role in vaccine development [12] . Goedhals et al . identified two possible epitopic regions in the GC of CCHFV [13] which could have potential in further development of serological assays and warrant further investigation . In this study we used a microarray technique creating a high throughput and cost effective method to screen B-cell peptide epitopes [14–16] covering the complete glycoprotein precursor of CCHFV . This is the first study using microarray technology to screen clinical samples from survivors of CCHF infections in South Africa and Turkey . This data was also further examined using serum samples from vaccinated individuals that received the Bulgarian vaccine . Interestingly , we have identified several specific peptide sequences which may have application in development of serological assays and also in vaccine development .
Overlapping peptides representing the complete glycoprotein precursor of a strain of CCHFV including the O-glycosylated mucin-like domain and GP38 at amino acid positions 15–515 , GN and NSM ( 515–1037 ) and GC ( 1037–1688 ) ( strain Turkey-Kelkit06 , uniprot #C7F6X8 ) were prepared by a modified automated Fmoc-SPPS ( Solid-Phase Peptide Synthesis ) methodology on a Syro II peptide synthesizer ( MultiSynTech , Witten , Germany ) as described previously [17 , 18] . All samples were screened against a peptide library representing a CCHFV isolate from Turkey to identify conserved epitopic regions between different lineages . Peptides were selectively enriched by covalent immobilization onto amine reactive N-hydroxy succinimide activated hydrogel coated MPX16 glass slides ( Schott Nexterion , SlideH ) with a BioRobotics MicroGrid II spotter ( Genomics Solution ) using Stealth 3B Micro Spotting Pins ( ArrayIt ) with approximately 6 nL per spot as described previously [17 , 18] . Printed glass slides were humidified for 30–60 min before N-hydroxy succinimide deactivation in blocking buffer ( 50 mM ethanolamine in 50 mM sodium borate , pH 8 . 5 ) for 30 min , then rinsed quickly in water and spun dry ( VWR , Galaxy MiniArray ) . The blocked glass slides were fitted into superstructures; 2/16/48 well ( FAST FRAME , Schleicher & Schuell ( Whatman ) ) to make separate identical peptide libraries . To each library either 500/100/10 μL PLI-P ( 0 . 5 M NaCl , 3 mM KCl , 1 . 5 mM , KH2PO4 , 6 . 5 mM Na2HPO4 , pH 7 . 4 , 3% bovine serum albumin ( BSA ) was added dependent on the superstructure . Blocked slides were fitted with a 2-well superstructure ( FAST FRAME , Schleicher & Schuell ( Whatman ) ) to form 2 wells . The wells were filled with 500 μL of glycosylation mixture ( 10 μL of 100 mM UDP-GalNAc ( Sigma-Aldrich ) , 10 μL of either 0 . 36 mg/mL mM GalNAc-transferase 2 [GalNAc-T2] ( SBH Biosciences ) or 0 . 46 mg/mL GalNAc-transferase 3 [GalNAc-T3] ( SBH Biosciences ) 10 mM and 480 μL HEPES buffer , placed in a humidification chamber , and incubated for two hours at room temperature ( RT ) . Slides were then washed with 0 . 1 M AcOH ( 2 x 5 min , shaking ) and PLI-P ( 5 min , shaking ) . Slides were again washed with phospate buffered saline ( PBS ) pH7 . 4 , rinsed thoroughly with water , dried by centrifugation , and were then ready to be immediately used in a subsequent lectin binding experiment [15 , 17] . Slides were incubated with biotinylated VVA lectin ( 1:500 dilution in PLI-P buffer ) for 1 hour at RT , followed by incubation with streptavidin-AlexaFluor 647 ( 1:1000 dilution in PLI-P ) for 1 hour . Pooled ( to conserve resources ) or individual CCHFV IgG antibody positive sera and CCHFV IgG negative sera or serum samples IgG positive for varicella zoster virus ( VZV ) , Epstein-Barr virus ( EBV ) , herpes simplex virus ( HSV ) and tick-borne encephalitis ( TBE ) were diluted ( 1:10 , 1:20 , 1:25 , 1:50 ) in incubation buffer PLI-P ( 0 . 5 M NaCl , 3 mM KCl , 1 . 5 mM KH2PO4 , 6 . 5 mM Na2HPO4 , pH = 7 . 4 , 3% BSA ) , added directly onto slide subarrays and incubated for minimum 1 h ( up to 2 days ) on a shaking plate with a slow rotation . Slides were washed three times using PBS . Positive reactors were detected using goat anti-human IgG-Cy3 ( Fc specific , 10μg/mL , Sigma-Aldrich ) diluted ( 1:1000 ) in PLI-P . After the final wash , slides were spun dry and scanned followed by image analysis . Slides were scanned using a ProScanArray microarray scanner ( Perkin Elmer ) equipped with laser for excitation at 543 nm and images were analyzed with Scan Array Express software . Spots were identified using automated spot finding with manual adjustments for irregularities in print . The final data was obtained from the mean spot Relative Fluorescence Units ( RFU ) from all replicate spots for each sample ( 3 or 5 spots ) . Spot intensities were determined by subtracting the median pixel intensity of the local background from the average pixel intensity within the spot . The quality control covered intra- and interchip quality analysis of replicates . For the selected peptides , serum samples with relative fluorescent values higher than two standard deviations over the mean of the control group were designated as positive . The samples were routinely heated at 56°C for 1 hour prior to handling . All antibody positive samples used in the study were from convalescent or vaccinated individuals and hence would be negative for CCHFV . All the data represented in this study are mean values from 3–5 replicates . The final data was obtained from the mean spot Relative Fluorescence Units ( RFU ) from all replicate spots for each sample ( 3 or 5 spots ) . Spot intensities were determined by subtracting the median pixel intensity of the local background from the average pixel intensity within the spot . The quality control covered intra- and interchip quality analysis of replicates . For the selected peptides , serum samples with relative fluorescent values higher than two standard deviations over the mean of the control group were designated as positive . A One-way-ANOVA ( Prism Graphpad 7 software ) was conducted as needed .
A scan peptide library consisting of 168 peptides ( 20mer with a 10 amino acid overlap , see S1 Table ) representing the glycoprotein precursor which includes the mucin-like domain , GP38 , and GN and GC region of a strain of CCHFV from Turkey , was incubated with pooled inactivated sera from survivors of CCHFV infection . Reactivity against peptides representing possible epitopic regions were identified ( Fig 1 ) . Samples with a relative fluorescent value greater than two standard deviations above the mean of the control group were designated as positive . Sera from survivors of CCHFV reacted significantly against fourteen peptides ( entry 1–14 , Table 1 ) . Although there was minor reactivity from control sera against some peptides ( entry 15–20 , Table 1 ) , the relative fluorescent values for these sera were significantly lower and these reactions were considered non-specific . From the results of this initial screen , 32 peptides were selected based on a combination of high reactivity of CCHFV positive sera and low reactivity of control sera ( S2 Table ) and narrowed down the selection to the five most promising peptide epitopes ( entry 6 , 7 , 12 , 13 and 14 , ( Table 1 ) ) . To further map the epitopes with respect to peptide sequence , a stepwise single-amino-acid epitope walk library of 20mers of these peptides was synthesized [15] . Six CCHFV IgG positive and two CCHFV IgG negative sera were analyzed individually , as shown in Fig 2 . Overall , serum reactivities were high ( >50% ) against each peptide 20mer sequence . Two different reactivity patterns were apparent when screening the epitopes ( colored in blue and orange in Fig 2 ) . Four serum samples ( designated CCHFV 1–4 , blue lines ) reacted strongly with early epitope walk ( EW ) peptides within libraries p24 and p55+56 . In contrast , serum samples designated CCHFV 5 and 6 ( orange ) reacted weakly with these peptides , or not at all , and reacted strongly with peptide 40 within library p55+56 and peptide 64 in p78 . The six serum samples reacted uniformly with p78 ( EW 57–61 ) and p96 ( EW 80–86 ) . No reactivity was detected using the serum samples that were negative for CCHF IgG antibody ( uninfected , black lines ) . High reactivity was seen against epitope walk ( EW ) residues 25–32 ( p55+56 ) for four serum samples ( CCHFV 1 , 2 , 3 and 4 ) , whereas the remaining samples ( orange ) reacted against EW peptide 40 ( p55+56 ) . All sera reacted against EW peptides 81–86 within p96 . High reactivity was seen against epitope walk ( EW ) residues 25–32 ( p55 ) for four serum samples CCHFV 1 , 2 , 3 and 4 ( blue ) , whereas the remaining samples ( orange ) reacted against EW peptides 40 ( p56 ) . All sera reacted against EW peptides p81-86 derived from p96 . Additional validation of p96 and its derived epitope walk peptides was performed with 30 sera from survivors of CCHFV from Turkey and 37 sera from individuals infected with other viral diseases such as TBE , VZV , EBV and HSV ( Fig 3 ) . The results demonstrate that the CCHFV infected individuals show high specificity and sensitivity ( 97% ) to epitope p96 . In addition , the activity of 10 sera from individuals vaccinated with the Bulgarian CCHFV vaccine [20] with p96 was analysed . This experiment also demonstrated that the vaccinated indivduals have significant reactivity towards this novel epitope p96 whereas the control sera ( n = 37 ) showed no reactivity . The amini acid similarity between strains from Turkey and Bulgarian is high and the epitopic region identified has only minor differences which indicates that a similar reactivity towards this epitope could be expected . An analysis of variance showed that the reactivity of samples from Turkish survivors and from the vaccinated group relative to the control group was highly significant ( F ( 2 , 74 ) = 68 , P<0 . 0001 ) . Lastly , since glycosylation can be important for viral envelope proteins and inducing immune responses , microarray experiments were conducted to determine whether additional epitopes could be identified using glycosylated peptides . Peptides immobilised on slides were treated with ppGalNAc transferase 2 ( T2 ) and ppGalNAc transferase 3 ( T3 ) , as described previously [15] . Glycosylation of peptides that represent parts of the mucin-domain were glycosylated as predicted with Net-O-Glyc ( v3 . 1 ) algorithm . ( S1 Fig ) . We did not observe any additional reactivity of the CCHFV sera after on-chip glycosylation of the peptides . However , one should have in mind that these data are based on peptide glycosylation experiments which may not mimic the natural glycosylation pattern on the envelop proteins . To further evaluate the identified peptide epitopes , a cohort of sera from survivors of CCHFV in South Africa were screened for reactivity against the 168 20mer peptide library . A total of 41 CCHF IgG positive sera from 14 laboratory confirmed patients in South Africa were included in the study . All samples were confirmed to be CCHF IgG antibody positive using a commercial immunofluorescent antibody assay ( EuroImmune ) . In addition , 11 CCHF IgG negative sera from 11 healthy volunteers were included as control samples . Pooled serum samples from confirmed patients reacted against peptides p55 , p56 , p105 , p115 , p119 , p127 and p168 ( Fig 4 ) . Reactivity against peptides 119 , 127 and 168 was unique to these sera . Single serum sample experiments were performed using a 108 peptide sublibrary ( see S1 Table , 1–108 ) from the larger 168 peptide library . As seen with the Turkish cohort of sera , the majority of the South African serum samples ( 97 , 6% ) reacted against the p55 ( ETAEIHDDNYGGPGDKITIC ) ( Fig 5 ) . The specificity was even more pronounced for the South African sera . An analysis of variance showed that the reactivity of samples from Turkish survivors and SA survivors relative to the control group was highly significant ( F ( 2 , 81 ) = 11 , P<0 . 0001 ) . Nine peptides were identified which included a region that reacted against either Turkish or South African samples or reacted against both samples ( Table 2 ) . Finally the predicted amino acid sequences for each peptide region were retrieved from UniProt and aligned to identify similarities between the geographically distinct isolates of the virus ( Table 3 ) . This high coverage of same peptide reactivity can be explained by high amino acid conservation along the selected epitopes ( Table 3 ) . In contrast the alignment confirms up to four mismatches in the predicted amino acid sequence between the Turkish peptide sequence and the South African sequence for p96 , thus possibly explaining their difference in reactivity of geographically diverse samples against this peptide . The combination of p55/p56 ( ETAEIHDDNYGGPGDKITIC/GGPGDKITICNGSTIVDQRL ) with p96 ( NVMLAVCKRMCFRATIEASR ) represents an immunodominant epitopic region that would result in a multiepitope covering the majority of the Turkish and South African strains , due to minimal differences in the amino acid sequence along the defined epitopes , defined ( Fig 6 ) . An analysis of variance showed that the reactivity of samples from Turkish survivors and SA survivors and relative to the control group was highly significant ( F ( 3 , 107 ) = 11 , P<0 . 0001 ) .
CCHFV is considered an emerging pathogen particularly in southern and eastern European countries . The emergence of this virus has significant public health implications . Currently there are a limited number of laboratories that can prepare reagents for diagnosis and serological surveillance as culturing the virus requires maximum containment facilities . Development of safe reagents will play a role in building capacity for diagnosis and surveillance . Development of reagents must take into consideration the global diversity of CCHFV . In this study peptides were used to identify potential epitopic regions on the glycoprotein precursor of an isolate from Turkey . The initial screen was peformed using a total of 168 peptides representing the entire GP of a Turkish strain of CCHFV . Five peptides were selected for further investigation using epitope walk analysis in an attempt to identify specific peptide sequence mimicking immunodominant linear epitopes . Despite some variability in the reactivity of the samples from survivors in Turkey , common regions were identified within p96 and p55+56 . Variability in the responses was particularly noted against the peptides of p24 . The differentiated response to the EW peptides of p24 is most likely caused by the fact that p24 is located in the mucin-like region of the envelope protein which is highly glycosylated . The glycosylation of the mucin-like region can differ and it is therefore expected that the serum response to the naked peptides will be diverse [16] . The role of the mucin-like domain in stimulation of B cells during CCHF infections has not been well defined . By analogy with Ebola virus the mucin-like domain may block access to GP and actually inhibit immune responses [21] . However as the mucin-like domain is not incorporated into viral particles its role may be very different to that proposed for Ebola [11] . It would be useful to determine if the mucin-like domain is involved in immune evasion and if deletion of this region promotes an immune response to more conserved regions on the GP as has been shown for Ebola virus . This would be significant for vaccine development but less important for developing tools for detection of antibody responses . In all the experiments p55 was identified as the top candidate due to the strong response shown when incubated with serum from CCHFV infected patients . The experiments with pooled sera showed a very dominating RFU signal , thereby undermining some of the other binding when reading the scan . The reaction towards p55 was scattered with around 2/3 of the samples binding to the peptide significantly where minor to no binding was seen in other serum samples . In the second group of sera ( orange pattern ) the highest reaction was shifted by 10 aa residues towards p56 in comparison to the blue pattern . A small common epitope was identified in p78 ( EW 57–61 ) with all serum samples showing similar reactivity . This epitope could be of potential interest due to the homogenous binding . The response to this epitope walk was lower than as seen with the other epitope walks except for CCHFV 5 and 6 which reacted to EW peptide 64 . The epitope with the strongest homogenous response was found in p96 epitope walk representing sequences being close to the transmembrane region ( aa 973–997 ) , and the tertiary structure can be expected to be more stable and should be in reach of B-cells/antibodies/immune cells . All serum samples tested showed binding towards the peptides EW peptides 80–86 . In summary , single amino acid shifts generated differentiation in reactivity patterns with no major reactive single peptide epitope for p24 , p55 , p56 and p78 , whereas library members derived from p96 ( epitope walk peptides p81-86 , 951NVMLAVCKRMCFRATIEASRRALLIR975 ) show reactivity with all six CCHFV sera . No reactivity towards these peptides was observed with the two control sera . Identification of peptides that are cross reactive serologically against geographically distinct strains is important for development of standardised assays for detection with application on different continents . Despite some variablity in the reactivity of samples from South African patients , a commonality was identified in p55 . The result of the epitope walk indicated that a strong differentiated binder could be obtained by combining the epitope 541ETAEIHDDNYGGPGDKITIC560 due to the strong signal of p55 , with p96 ( 951NVMLAVCKRMCFRATIEASR970 ) for specific overall coverage . A combinational multi-epitope including these peptides could eventually secure a strong selectivity and sensitivity . The study identified several specific peptide sequences which may have application in development of serological assays and possibly vaccine development . However for vaccine development it must be taken into consideration that the immune correlates of protection for CCHFV are currently not well defined and the role of T cells and discontinuous B cell epitopes would need to be considered . Evidence exists for a role for both antibody and T cell responses . A long lived cytotoxic T cell response was recently described in survivors of infection suggesting a role for T cells in protection [22] . However vaccine studies have suggested a role for both humoral responses and T cell responses . Results from a candidate vaccine employing a modified vaccinia virus Ankara poxvirus vector containing the GP in which passive and adoptive transfer of serum samples and T-lymphocytes were used in an attempt to define the role of each arm of the adaptive immune response , concluded that protective immunity likely requires both humoral and cellular involvement [23] . Serum samples collected from volunteers vaccinated using the inactivated Bulgarian vaccine reacted similarly to survivors of virus infection from Turkey with regard to peptides recognised although there were differences in intensity of reactivity . Sera from vaccinated individuals reacted with lower intensity than sera from naturally infected survivors . Previous investigations of immune responses in vaccinated individuals suggested that even after several boosters the neutralising antibody response was low . Although it is not known if the epitopic regions identified in this study represent neutralising epitopes it is possible that the lower reactivity is a reflection of a less robust immune response in vaccinees compared with survivors . The use of glycoproteins has not been extensively investigated for serological assays likely due to the inherent challenges associated with expression of recombinant glycoprotein antigens . NP has been investigated as a target for diagnostic proteins due to its immunogenicty , abundance and homology between isolates . However although there is less sequence homology in the M gene compared to the S gene encoding for NP , a significant amount of the diversity is located within the mucin-like region with less than 10% amino acid diversity determined for the remainder of the GP [24] . It is probable that epitopes inducing immunodominant and/or neutralising responses are more likely to be conserved between isolates and in the absence of detailed data regarding immunodominant epitopes in the GP it is appropriate to further define if there are conserved regions with possible roles in development of diagnostic tools and incorporation in vaccine design . Peptides mimicking epitopic regions could have a potential as diagnostic tools [12] . Goedhals et al . identified two possible epitopic regions in the GC of CCHFV and although in this study reactivity was identified in these epitopic regions using samples from South African survivors there was no significant reactivity from Turkish sera [13] . This highlights the need to consider diversity when selecting peptides to mimic epitopic regions . Differences in serological reactivity between Turkish and South African samples suggested possible differences in protein sequences which were confirmed by alignment of predicted amino acid sequences . Hence the use of multiple peptides in downstream assay development will likely increase the usefulness of the assays . Lack of reactivity against serum samples collected from patients with other infectious diseases that could be considered in a differential diagnosis suggest that these peptides react more specifically against CCHFV antibodies . However the specificity and sensitivity will need to be validated in any future assay development . The cross-reactivity of these peptides with samples from a geographically distinct region where genetically diverse strains of the virus circulate enabled the identification of unique peptide epitopes from the CCHFV glycoprotein that could have application in develoment of diagnostic tools such as lateral flow or ELISA for antibody detection .
|
Crimean-Congo hemorrhagic fever ( CCHF ) is a widespread disease caused by a tick-borne virus belonging to the genus Orthonairovirus of the Nairoviridae family . The virus is responsible for outbreaks of severe viral hemorrhagic fever with a case fatality rate of approximately 30% . The CCHF virus is transmitted to people either by tick bites or through contact with infected animal blood or tissues . A mouse brain-derived vaccine against CCHF has been developed ( included in this study ) and used on a small scale in eastern Europe . There is no safe and effective vaccine widely available for human use . Currently , there are a limited number of serological assays commercially available for testing for CCHFV specific IgG and IgM . There are enzyme linked immunosorbent assays ( ELISA ) and imunnofluorescent assays ( IFA ) designed for screening human samples for diagnostic purposes however they are not cost effective for surveillance studies . The limiting factor for the replication of these protocols in other laboratories is the availability of antigens and ( where relevant ) specified monoclonal antibodies . To contribute to the improvement of the diagnostic methods for CCHFV , we aimed to identify and characterize new synthetic antigens that were more sensitive and specific and could be applied in epidemiologic surveys .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"methods",
"Results",
"Discussion"
] |
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"crimean-congo",
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"glycobiology"
] |
2018
|
Epitope-mapping of the glycoprotein from Crimean-Congo hemorrhagic fever virus using a microarray approach
|
Sequence-specific transcription factors ( TFs ) are critical for specifying patterns and levels of gene expression , but target DNA elements are not sufficient to specify TF binding in vivo . In eukaryotes , the binding of a TF is in competition with a constellation of other proteins , including histones , which package DNA into nucleosomes . We used the ChIP-seq assay to examine the genome-wide distribution of Drosophila Heat Shock Factor ( HSF ) , a TF whose binding activity is mediated by heat shock-induced trimerization . HSF binds to 464 sites after heat shock , the vast majority of which contain HSF Sequence-binding Elements ( HSEs ) . HSF-bound sequence motifs represent only a small fraction of the total HSEs present in the genome . ModENCODE ChIP-chip datasets , generated during non-heat shock conditions , were used to show that inducibly bound HSE motifs are associated with histone acetylation , H3K4 trimethylation , RNA Polymerase II , and coactivators , compared to HSE motifs that remain HSF-free . Furthermore , directly changing the chromatin landscape , from an inactive to an active state , permits inducible HSF binding . There is a strong correlation of bound HSEs to active chromatin marks present prior to induced HSF binding , indicating that an HSE's residence in “active” chromatin is a primary determinant of whether HSF can bind following heat shock .
Signal-dependent activation of transcription is a highly regulated process that is dictated by transcriptional activators that selectively identify and function at sequence-specific DNA motifs . The most basic function of sequence specific activators is to discriminate between binding sites in the context of the entire genome [1]–[4] , but the mechanism by which this occurs is poorly understood . Two main mechanisms have been proposed that explain the observed in vivo binding specificity ( reviewed in [5] ) : TFs are occluded from cognate site by chromatin structure or TF binding is facilitated by cooperative interactions with cofactors . In vivo , TFs are in competition with chromatin factors , which may limit TF access to cognate binding sites [6] , [7] . Early sequence-specific ChIP experiments of homeoproteins revealed that binding sites are preferentially accessible if target motifs are located within active genes [1] . More recently , advances in genome-wide characterization of histone modifications and chromatin structure have begun to identify additional requirements for the binding of TFs . In human cells , it has been shown that the H3K4me1 and H3K4me3 modifications are present at inducible STAT1 binding sites prior to interferon-gamma stimulation [8] . In Drosophila , H3K36me3 has been revealed as an important histone mark for male-specific lethal ( MSL ) complex binding [9] , [10] . However , the Hox proteins primarily discriminate between equivalent predicted binding sites by cooperative interactions with DNA-bound cofactors ( reviewed in [11] ) . These findings indicate that the binding of TFs depend upon the chromatin landscape as well as specific sequence elements , and we set out to determine the extent to which chromatin affects TF binding genome-wide . Characterizing the mechanistic parameters by which TFs locate and bind to target DNA sequences will provide insight into a critical early step in a cell's ability to orchestrate patterns of gene expression in response to developmental , nutritional , and environmental signals . Heat Shock Factor ( HSF ) has a conserved function as the master regulator of the heat shock ( HS ) response from organisms as distantly related as yeast and humans [12] . The HS genes of Drosophila melanogaster are an attractive model system to study the general functions of HSF and its induced transcriptional regulation [13] . HSF is present as a nuclear-localized monomer during non-stress conditions [14]; upon stress , HSF homotrimerization [15] mediates binding to HSF Sequence-binding Element ( HSE ) motifs within seconds [16] , [17] , which strongly activates a set of HS genes . While transcription factor binding to DNA is necessary for cis regulation of target genes , not all TF binding is necessarily functional [18] . For instance , HSF has been mapped to over 164 cytological sites on the polytene choromosomes of Drosophila salivary gland cells after HS [19] , but only 9 cytological loci exhibit HS-induced transcription elongation factor recruitment and activation [20]–[23] . It remains unclear how HSF discriminates between sites and selectively stimulates functional gene activation . In this study , we set out to determine the comprehensive set of HSF binding sites in the Drosophila genome and the molecular basis for the binding . We used ChIP ( chromatin immunoprecipitation ) followed by sequencing [24] , adapted for high throughput detection ( ChIP-seq ) [25]–[27] , to map the sites of HSF binding in an unbiased manner with high sensitivity and resolution . We made use of the ChIP-chip datasets from the model organism ENCyclopedia Of DNA Elements ( modENCODE ) consortium [28] , [29] , which profiles histone modifications , histone variants [30] , insulators [31] , [32] , and Pol II . These datasets describe critical features of the chromatin landscape in unstressed cells . Using this data , we contrasted the chromatin landscape before HS induction at induced HSF-bound HSE motifs and HSE motifs that remain HSF-free . The roles of many of the modENCODE chromatin features are well established [30]–[33] , thus the absence or presence of one or many of these features provides insight into the mechanism of HSF binding .
To determine the comprehensive set of HSF binding sites , we performed two highly correlated , independent ChIP-seq experiments in Drosophila S2 cells [34] for both non-heat shock ( NHS ) and 20′ HS conditions ( Figure S1 ) . We used well-characterized ChIP-grade HSF antiserum [23] , [35] which specifically recognizes one HSF-RNAi sensitive Western blot band from whole S2 cell extract ( Figure 1A ) [35] and generates the expected global HSF-binding pattern observed by indirect immunofluorescence ( IF ) polytene staining [19] , [36] , [37] . Despite the specificity observed in these assays , we set out to directly assess specificity in genome-wide ChIP by identifying any HSF-non-specific DNA pull-down . We performed two independent HSF antiserum ChIP-seq control experiments , for each condition ( NHS and 20′ HS ) , in cells that were depleted of HSF by RNAi . This approach approximates a control immunoprecipitation ( IP ) from cells that lack the factor of interest [38] , [39] . HSF-knock down ( KD ) depleted endogenous levels of HSF to less than 2 . 5% of control cells as measured by quantitative Western blot ( Figure 1A ) . Importantly , the level of HSF in RNAi depleted cells was reduced at the promoters of well-characterized HS genes , including the highest affinity Hsp83 promoter ( Figure S2 ) . Due to the unique presence of tandem HSEs and cooperative HSF binding , the in vitro dissociation constant for the HSF/Hsp83 promoter interaction is on the order of single-digit femtomolar [40] , and the Hsp83 promoter harbors the only strongly bound sites during NHS [19] ( Figure S1 ) . Since our KD of HSF was successful at reducing HSF levels at the highest affinity binding site , the signal intensity of all HSF-specific peaks should be susceptible to HSF-RNAi depletion as well . Therefore , we discarded peaks that were resistant to HSF depletion , as these are very likely false positives ( Figure 1B , Figure S3 , Figure S4 and Materials and Methods ) . Our analysis of the ChIP-seq data aimed to increase the sensitivity of HSF detection without compromising confidence . To this end , we relied upon two peak calling programs [41] , [42] to determine HSF binding sites ( see Materials and Methods and Figure S3 ) . Lower confidence peaks were initially considered and later filtered out if found resistant to HSF-RNAi . We detected 464 HSF-specific peaks after 20′ of HS ( Dataset S1 ) . We recovered 118 RNAi-sensitive peaks that would have otherwise been discarded because of high false discovery rates ( FDR ) ( Figure S3 ) . In addition , we filtered out 310 non-specific peaks that had FDRs below 0 . 1 ( Figure S3 ) , because they were completely insensitive ( and actually increased in intensity ) to HSF-KD and exhibit comparable NHS intensity ( Figure 1B ) . Therefore , performing ChIP-seq in cells that were depleted of HSF by RNAi increased the sensitivity and specificity of peak calling . We derived a position-specific weight matrix ( PSWM ) [43] and generated an in vivo composite HSF binding site using all 464 HSF peaks occupied after 20′ HS ( Figure 2A bottom ) . Greater than 95% ( 442/464 ) of the peaks contained at least one HSE ( Figure 2A bottom ) with a p-value below 0 . 001 ( Figure S4 and Materials and Methods ) , indicating that we are primarily detecting HSF directly bound to DNA . In contrast , the distribution of HSE motifs surrounding the HSF-RNAi resistant peaks approximates random expectation ( Figure S4 ) . This analysis indicates that the majority of RNAi resistant peaks are false positives that likely result from antiserum cross-reaction with another DNA binding protein , as these peaks are not present in the pre-immune IP . Consistent with the high affinity motif derived by in vitro band shift assays [16] ( Figure 2A top ) , the in vivo HSE is a tandem array of three oppositely oriented five base pair units: AGAAN . In vitro HSF can bind to elements containing three five base pair units , regardless of their orientation relative to one another—although the opposite orientation of three 5 base pair units bound more tightly than direct repeats [16] . Our ChIP-seq study reveals that the opposite orientation of the tandem 5 base pair units is absolutely critical for detectable binding in vivo . At those peaks that contain HSE motifs , we inferred the HSF binding sites at base pair resolution using the consensus-binding motif derived from this study ( Figure 2A bottom ) . If multiple HSEs were within the 442 HSE containing peaks , the motif closest to the peak center was scored as the HSF binding site ( Dataset S2 ) . Our analysis recovered all previously well-characterized HSF binding sites within the promoters of HS responsive genes ( Figure 2B , Figure S2 , and Dataset S3 ) , including the multi-copy Hsp70 gene ( Figure S5 ) . We found that only 20 of the high-confidence HS peaks are detected during NHS conditions , and with a much lower density of tag counts ( Figure 2B ) . Despite the fact that a corresponding NHS peak could not be detected at 422 of the 442 HS peaks , sequence tags are associated with these regions and signal may be above background , but below our threshold for detecting peaks . We considered that true signal should still be susceptible to HSF-KD ( Figure S6 ) and concluded that the majority of these 422 sites are either completely devoid of HSF or contain extremely low , thus undetectable , levels of HSF under NHS conditions . Taken together , our analysis reveals that HSF behaves as we expected from previous molecular analyses of particular genes [16] , [17] and from comprehensive , but low resolution , cytological analyses [19]: HSF binds strictly to HSEs and these sites are absent or show drastically reduced occupancy during NHS conditions . Previous independent reports indicate that ChIP signal intensity positively correlates with motif conformity [3] , [4] , [44] . We find , however , that HSF binding sites conforming more stringently to the PSWM contain a comparable density of sequence tags as degenerate HSF binding sites ( Figure S7A and S7B ) , suggesting that sequence alone is not driving HSF binding affinity . Although bona fide HSF binding sites contain highly specific HSE motifs , only a small fraction of potential HSE motifs are occupied by HSF . To search for HSF-free binding sites , we employed a conservative cut-off for conformity to the consensus HSE by using a p-value of 5×10−6 or less [43] , while ensuring that the flanking region is mappable [45] . There are 708 HSF-free motifs ( Dataset S4 and Figure S8A ) that meet these criteria . Less than 15% ( 107/815 ) of the mappable HSE motifs with a p-value of 5×10−6 or less are detectably bound by HSF after HS . Upon closer inspection ( Figure S6 ) , we find that HSF-free motifs are absolutely HSF-free during NHS , and these same motifs are either unoccupied or infrequently occupied after HS . In contrast , HSF-bound motifs are either very weakly occupied or unoccupied prior to HS , and show strong inducible binding after HS induction . Therefore , these two categories of motifs , HSF-free and HSF-bound , are distinct from one another and are compared below . We determined the distribution of HSF binding sites relative to annotated genes and promoter regions . Annotated genes account for 60 . 6% of the Drosophila reference genome ( Figure S8B ) , however , 72% of the HSF-bound motifs are found within gene boundaries ( Figure 2C ) . HSF-bound motifs within promoters ( 500 bp upstream of a transcription start site ( TSS ) ) were also enriched , accounting for 22% of the total bound motifs ( Figure 2C ) , while such promoter regions only account for 3 . 4% of the total reference genome ( Figure S8B ) . In contrast , the classification of the 708 HSF-free motifs is much closer to a background distribution; 63% HSF-free motifs are within genes and 5 . 5% are within promoters ( Figure S8A ) . These results indicate that HSE motifs are not simply enriched within gene and promoter boundaries , but that HSF preferentially interacts with HSEs that are present within genes and promoters . We hypothesized that HSF discriminates between equivalent HSE sequences in vivo based on the chromatin landscape in which motifs reside . Previous work shows that HSF preferentially binds acetylated nucleosomes in vitro and more recently that the androgen receptor preferentially binds nucleosomes modified with methylated H3K4 in vivo [46] , [47] . To determine the extent to which HSF binding is influenced by chromatin in vivo , we compared the NHS chromatin state between the motifs that become HSF-bound or remain HSF-free following HS , excluding the 20 HSF-bound motifs in which HSF was detected during NHS ( Dataset S5 ) . Using modENCODE S2 ChIP-chip data [28]–[32] , we examined the composite intensity of microarray signal in the region surrounding each HSE . We found that HSF-bound motifs were generally associated with marks of active chromatin , even though these modENCODE signals were generated under NHS conditions ( Figure S9 ) . The HSF-free motifs , as a class , were neither enriched nor depleted for any particular factor , histone modification , or histone variant . Nucleosome occupancy of potential TF binding sites generally restricts TF binding [6] , [7] , so we examined the distribution of histones and histone variants around HSF motifs . We expected the HSF-bound motifs to be depleted of nucleosomal H3 . The composite profiles show that nucleosomal H3 is clearly not depleted ( Figure 3 ) ; in fact , we observe a slight increase in H3 levels at bound HSEs compared to free HSEs . This observation is in contrast to the general inhibitory nature of nucleosomes and the previous view of HSF binding , as the small set of well-characterized HSF binding sites are devoid of canonical nucleosomes prior to HS [48] , [49] . The histone variant H3 . 3 , which associates with active genes [30] , displays a peak centered on the HSE motif ( Figure 3 ) . These results indicate that HSF binding specificity is not simply dictated by nucleosome-free DNA sequence . In recent years , considerable attention has focused on the plethora of covalent histone modifications that occur on the N-terminal tails of histones , the enzymes responsible for catalyzing histone modifications , and the functional consequence of each modification . Acetylation of histone residues H3K9 , H3K18 , H3K27 , H4K5 , H4K8 , and H4K16 were found to associate with HSF-bound motifs ( Figure 3 and Figure S10 ) . Each one of these acetylation marks has previously been shown to mark active chromatin [33] , [50] . We find that the methylation marks H3K4me3 and H3K79me2 , which associate with active genes [33] , [51] , are also enriched around the HSF-bound HSEs ( Figure 3 and Figure S10 ) . Mono-ubiquitylation ( Ub ) of H2B , a modification that is necessary for methylation of H3K4 [52] , correlates with HSF-bound motifs as well ( Figure S10 ) . Conversely , marks of repressive chromatin , H3K27me3 and H3K9me3 , were found to be depleted or at background levels ( Figure 3 and Figure S10 ) . We considered that HSEs and histone marks cooperate to specify HSF binding . Transcription factors can bind acetylation and methylation marks through specific domains such as bromodomains , chromodomains and PHD domains ( reviewed in [53] ) . For example , the MSL complex harbors a chromodomain , accounting for preferential recognition and binding of the H3K36me3 mark in Drosophila [9] . Interestingly , the HSF protein is devoid of all of these domains , and thus cannot be binding to DNA and histone methyl or acetyl marks cooperatively by any of these well-characterized interactions . Comparison of HSF-bound motifs with TF binding data reveals that HSF co-localizes with factors that are associated with active transcription . The presence of Pol II is the foremost indicator of an active gene or a gene that is primed to be activated . The composite Pol II profile at HSF-bound HSEs exhibits a striking peak , even in instances where bound HSEs are within intergenic regions ( Figure 3 ) . Likewise , we observe a strong BEAF ( boundary element-associated factor ) signal centered on HSF-bound motifs ( Figure 3 ) . BEAF is an insulator that localizes to transcriptionally active and paused polymerase-harboring genes [32] . The multifaceted TF , GAGA Associated Factor ( GAF ) , is associated with both paused polymerases and HSF-bound motifs [54] ( Figure 3 ) . Taken together , these profiles indicate that HSF binds to sites that contain hallmarks of open and active chromatin . These composite profiles provide an average view of HSF-binding , which could potentially be influenced by a small population of binding sites . We used the available “Regions of Significant Enrichment” tracks from modENCODE to determine which motifs ( HSF-bound or HSF-free ) were present within the significantly enriched regions of a given factor or modification . We employed the Fisher exact test to determine whether HSF-bound motifs were associated with each factor compared to HSF-free motifs and vice versa ( Table S1 ) . Depicted in Figure 4 and Figure S11 are the fractions of HSEs that are present within a given region of enrichment ( enriched is colored yellow , unenriched is blue ) . Strikingly , only 30 ( 7% ) inducible HSF-bound sites do not contain any tested activation marks prior to HS . This analysis reveals a statistically significant association ( p-value<0 . 05 ) of HSF-bound motifs with 17 different histone modifications or chromatin-bound factors that have previously been shown to be associated with active chromatin ( Table S1 , Figure 4 , and Figure S11 ) , regardless of whether the motifs are classified as intergenic , promoter proximal or within genes ( Table S2 , Table S3 , and Table S4 ) . Unlike previous genome-wide TF binding data that show the co-occupancy of many TFs and histone marks , we are able to show that these chromatin features are present before any detectable HSF binding ( Figure S12 ) . We have shown that the presence of activation marks strongly influences the pattern of HSF binding , so we next determined whether quantitative differences in individual marks play a role in the degree of HSF binding . For each HSE that is enriched for a mark or factor in Figure 4 , we compared the ChIP-chip intensity of each mark or factor during NHS to the intensity of induced HSF binding following HS . We found a modest , but significant ( p-value<0 . 05 ) , correlation between the intensity of BEAF , tetra-acetylated H4 , and H3K18ac with HSF binding intensity ( Figure S13 ) . Considering that the intensity of any one mark only modestly affects HSF binding , we set out to determine whether distinct patterns of TF profiles and histone modifications affect HSF binding intensity . Sets of histone modifications and TFs occur together in distinct combinations on the genome-wide scale in eukaryotic cells [18] , [33] , [55] , [56] , and this chromatin landscape can be used to predict and characterize functional regions of the genome [57] , [58] . We used cluster analysis [59] to determine whether TF factors and histone modifications showed clear binding patterns at both classes of HSE motifs ( Figure 5 ) . This clustering shows that , generally , any single HSF-bound motif is enriched for many activation marks . HSF-free motifs are primarily found in regions with background levels or depleted levels of activation marks . Consistent with our composite profiles , nucleosomal H3 and H2A were not depleted at the bound HSEs prior to HSF binding and H3 . 3 is generally enriched . Our findings indicate that HSF-accessible chromatin is not synonymous with nucleosome vacancy , but rather , with marks of loose or active chromatin . Clusters are not absolutely delineated by the presence or absence of a given factor or set of factors; however , we note general properties of individual clusters . For instance , ubiquitous acetylation of histone residues and high levels of H3K4me1 characterize HSF-bound cluster three , while cluster four contains modest levels of every factor and modification tested ( Figure 5 ) . Considering that motif conformity does not significantly affect HSF-signal ( Figure S7A and S7B ) , we tested whether clustering HSEs cleanly separated strong and weak binding sites . We observe that cluster four generally exhibits less intense HSF binding , while cluster one , which is driven by intense Pol II and GAF signal , contains stronger HSF binding sites ( Figure S7C ) . These patterns , however , are not sufficient to account for differences in HSF binding intensity , as the HSF intensity in any p-value quartile or cluster overlaps with all other classes . Ultimately , it is likely that the rules that govern TF binding and intensity of binding are a complex nonlinear system , which results in motif accessibility . The strong correlation between open chromatin and HS-induced HSF suggests that open chromatin dictates HSF accessibility . To test this hypothesis , we directed a change in the chromatin landscape , from the restrictive to the permissive state , at an unbound HSE and then examined HSF binding following HS . HSF has been shown to selectively occupy the ecdysone inducible 75B cytological locus , only when the locus is transcriptionally “puffed” , in salivary gland cells [19] . We found an HSF-free motif that resides within the body of an ecdysone inducible gene isoform , Eip75B , which can be inducibly expressed in S2 cells ( Figure 6A ) [60] . We confirmed that this motif is minimally bound by HSF after HS , and is below the threshold for peak detection by ChIP-seq ( Figure 6B ) . Ecdysone treatment alone results in RNA Pol II recruitment to the body of the Eip75B gene , but does not affect HSF occupancy of the HSE motif ( Figure 6B ) . H3K9ac and tetra-acetylated H4 increase above the background threshold ( top dashed line ) , while H3 levels are unaffected after a 30′ ecdysone treatment ( Figure 6B ) . Recall that prior to HS , between 70% and 80% of the HSF-bound HSEs are significantly enriched for each RNA Pol II , tetra-acetylated H4 and H3K9ac ( Figure 4 ) . A 30-minute ecdysone pre-treatment changes the chromatin landscape and allows HSF to strongly occupy the motif following HS ( Figure 6B ) . Pre-treatment with ecdysone , followed by HS , not only allows HSF binding at this HSE , but also causes a concomitant increase in local H4 and H3K9 acetylation and decrease in RNA Pol II intensity ( Figure 6B and Figure S12 ) . Increased acetylation of histones is consistent with HSF's ability to recruit the acetyltransferase CREB Binding Protein ( CBP ) to HSF bound sites [61] , [62] . At first glance , it is unintuitive that RNA Pol II intensity is compromised following heat shock ( Figure 6B and Figure S12 ) . However , this molecular analysis confirms a long-standing observation that following HS , HSF has the ability to repress ecdysone inducible puffs and general protein synthesis [19] , [63] . While the mechanism of HSF-mediated repression is unknown in Drosophila , it is tempting to speculate that HSF can act as a roadblock to RNA Pol II within the bodies of active genes ( Figure S14 ) . It has long been known that HSF inducibly binds to many sites and only a subset of sites are transcriptionally activated by HS [19] , [64] . These studies , however , did not have the resolution to determine if HSF binding sites did not lead to mRNA production simply because HSF was not promoter-bound . In all well-characterized cases of Drosophila HSF-induced transcription , HSF binds to the promoter . To determine whether promoter-bound HSF is sufficient to upregulate the local gene , we measured mRNA abundance at candidate genes during NHS and after a 20′ HS ( Figure 7 ) . Note that HSF is inducibly bound at each gene after 2′ minutes of HS ( Figure S15 ) , allowing sufficient time for mRNA accumulation ( reviewed in [65] ) . We observe a continuum of induced mRNA accumulation , from the highly induced Hsp26 gene , to genes that are unaffected by HSF binding ( Figure 7 ) . Previous genome-wide ChIP experiments report that TF binding intensity generally correlates with functional binding [18] , [66] . The ChIP-seq signals that we observe are directly comparable to qPCR quantified ChIP material , indicating that the quantitative properties of ChIP were retained in our sample preparation ( Figure S16 ) . Because HSF acts as a potent acidic activator [67] , we hypothesized that all genes that exhibit inducible and strong promoter binding of HSF would be activated . HSF can activate when bound moderately to the promoters of genes , as is the case for the CG3884 and CG6770 genes ( Figure 7 and Figure S15 ) . Surprisingly , HSF binds inducibly and intensely to the CG3016 and CG13025 promoters ( Figure S15 ) , but these mRNA levels remain unchanged ( Figure 7 ) . Selective activation is not unique to HSF , as both ER and p53 bind the promoters of genes in a signal-dependent manner , but transcription of some local genes remains unaffected [2] , [68] . To investigate how HSF may be selectively activating local genes , we used the ChIP-chip data to look for patterns of histone modification and TF binding that separates functional promoter-bound HSF sites , which can activate gene expression , from promoter-bound HSF that does not result in gene activation . We noticed that GAF was present at many up-regulated genes , and in contrast , BEAF was present at unregulated genes ( Figure S17 ) . Previous work has shown that GAF is important for the activation of HS genes [69] , but our results indicate that GAF is not necessary for HSF activation ( Figure S17 ) . BEAF has been shown to function as an insulator [31] , [32]; therefore , we speculate that BEAF is blocking the activation function of HSF at unregulated genes . Previous work has implicated paused polymerase as an important criterion for activation from an Hsp70 promoter [70] . Using promoter-proximal enriched Pol II and pausing factor ( NELF ) data [54] , however , we did not see a significant correlation between these pausing hallmarks and activation potential using these 16 genes . In the same way that chromatin signatures affect the binding of HSF to a motif in vivo , we expect that chromatin landscape and individual gene properties act together to dictate the activation potential of activator-bound genes .
We present an experimental approach that increases the sensitivity and power of determining TF-bound sites by ChIP-seq , and we use this approach to characterize the binding profile for HSF under both NHS and HS conditions . Our analysis revealed that HSF binding is dependent upon an underlying HSE motif , although the primary HSE sequence is not sufficient to confer HSF binding . HSF-bound HSEs were found to be associated with a chromatin landscape that harbors active marks prior to HSF binding . Lastly , we demonstrated that promoter-bound HSF is not sufficient to activate local genes . The ChIP-seq method is used routinely to determine genome-wide factor binding profiles; however , important controls and variations in the ChIP protocol more fully exploit this approach . Our implementation of the control RNAi knockdown of HSF allowed us to eliminate the genome-wide set of false positive signals that were resistant to this knockdown , and prevented the elimination of many true positive binding sites . Another rigorous and complementary control for specificity includes performing independent ChIP experiments with multiple antiserum preparations , each of which is affinity purified with nonoverlapping antigens [18] , [38] . The details of ChIP-seq chromatin preparation can also enhance peak detection [71]–[73] . Additional crosslinking agents [74] and crosslinkers that target particular types of protein/DNA interactions , such as exclusively probing direct protein/DNA interactions with UV light [1] , [75] , can also augment the type and quality of information obtained by the basic ChIP-seq strategy . The non-sequence dependent specificity observed by TFs can be explained by non-mutually exclusive mechanisms: DNA binding is specifically inhibited by repressive chromatin , aided by active chromatin , or mediated by cooperative interactions with chromatin factors . Here , we report that repressive marks contribute minimally to restrict HSF binding , as only a small fraction of HSF-free motifs are associated with repressive chromatin ( Figure S11 ) . Additionally , we observe that chromatin containing background levels of active and repressive marks is unfavorable to inducible HSF binding—the default state of an in vivo HSE can be considered inaccessible . In contrast , HSF inducibly binds to sites that contain TFs and marks of active chromatin prior to HS induction . We have shown that the chromatin landscape can be modified to the permissive state and result in recognition and binding of a previously unbound HSE . This result suggests that HSF does not primarily function to bind DNA cooperatively with other factors , but simply co-occupies the same regions as other TFs , due to the accessible nature of the DNA . These results provide a framework for understanding the binding selectivity of HSF , and we look forward to mechanistic studies that solidify the rules of in vivo binding specificity . Activators are generally thought to bind to promoters and recruit either Pol II or coactivators to produce productively elongating Pol II . HSF recruits the acetyltransferase CREB Binding Protein ( CBP ) and a methyltransferase , Trithorax , directly to HS genes [61] , [62] . Paradoxically , this study shows that the chromatin landscape at HSF binding sites contains considerable histone acetylation and methylation prior to detectable HSF binding . HSF recruits these enzymes after HS to broaden the domain or increase the level of histone modifications ( Figure 6 and Figure S12 ) . Another , non-mutually exclusive , possibility is that cofactors other than histones are the functional targets of recruited transferases . Although we describe the landscape at HSF binding sites prior to HS , it still remains unclear which factors are responsible for setting up or maintaining the accessibility of these motifs . Furthermore , many HSF-binding sites are probably passively occupied because they happen to be accessible and HSF binding is non-deleterious [76] , but these sites likely have no function in the HS response . The global chromatin landscape is dynamic throughout development and environmental changes; therefore , we expect that the HSF binding profile at non-functional sites is dynamic as well . Nonetheless , the HS response is a ubiquitous cellular response , so functional sites are likely to be evolutionarily constrained at the sequence level [77] , [78] , and actively maintained in the accessible state at the level of chromatin organization . The maintenance of functional HSF binding sites may be occurring as a result of a specific class of activators . Non-traditional activators , such as GAF , are known to recruit cofactors that establish an accessible chromatin state , as opposed to directly activating transcription of the local gene ( reviewed in [79] ) . This general mechanism has been characterized at the phaseolin gene in Arabidopsis [80] and at the PHO5 gene in yeast ( reviewed in [81] ) . Taken together , this suggests a step-wise process whereby a repressed site can be potentiated for activator binding and subsequently activated . Additionally , it has been shown that active marks are not simply a product of transcription , as the active marks that are associated with intergenic DNaseI hypersensitive sites and putative enhancers are not correlated with respective gene expression [33] . Our results suggest that the landscape may be marked with active histone modifications to allow binding of activators that can stimulate transcription; therefore , the presence of a modification would not be expected to correlate with gene expression if the activator has yet to bind . Further investigation of activator binding sites during non-induced conditions will determine the generality of this observation . Our candidate gene analysis shows that HSF is not sufficient to activate local genes . Although inducibly activated genes are occupied by their cognate transcriptional activator near the TSS [4] , [82]–[84] , it remains unclear how the majority of activators discriminate between locally bound genes to selectively activate . Strikingly , Caudal exhibits promoter element-specific activation , specifically activating genes that contain the Downstream Promoter Element ( DPE ) [85] . Previously , we presented evidence that the presence of a paused polymerase facilitates activation from an Hsp70 promoter [70] , but it is unclear whether or not this is true for the majority HSF-inducible genes . Combinations of promoter features and gene properties are likely necessary for activation . One certainty , however , is that the recent emergence of genome-wide expression and binding data makes the characterization of complex regulatory mechanisms more exciting and promising than ever .
The ChIP protocol has been previously described [86] . In short , S2-DRSC ( lot 181A1 ) cells were grown in Schneider's media with 10% FBS ( lot ASD29137 ) , consistent with modENCODE experiments . Heat shocked cells were instantaneously shifted to 36 . 5°C by the addition of an equal volume of 48°C media to the 25°C culture . Heat shocked cells were instantaneously cooled to room-temperature and crosslinked with a final concentration of 2% paraformaldhyde for one minute; this shorter duration of crosslinking with higher concentration of paraformaldehyde was found to increase the signal-to-noise ratio . Instant cooling to room temperature and immediate crosslinking allows the heat shock and NHS samples to be crosslinked at the same efficiency and directly compared . We cannot strictly rule out the possibility that instantaneous cooling cells to room-temperature for one minute contributes to the recovery and dissociation of HSF at lower affinity sites , including the 708 HSF-free sites . However , paraformaldehyde penetrates cells quickly to effectively block further cellular changes , and HSF's DNA binding activity is only modestly affected even after a 30 minute recovery from HS [87] . Crosslinking was quenched by the addition of glycine to a final concentration of 250 mM and the extract was sonicated as previously described [86] , but for three-times the duration to increase enrichment [88] . The Protein-A beads were blocked with BSA ( 1 mg/ml ) and Polyvinylpyrrolidone ( 1 mg/ml ) prior to the IP and freshly thawed antiserum was used for each IP , which also increased signal compared to noise . The sample preparation was previously described [89] , with some modifications . Only one size selection , after adapter ligation , was performed . Thirteen cycles of PCR were performed . Quant-iT Pico Green ( Invitrogen ) staining was used to quantify the DNA sample . Samples were submitted to the Cornell DNA Sequencing and Genotyping Lab and run on the Illumina Genome Analyzer II . RNAi-mediated HSF knockdown was performed as previously described [69] . Primer sequences are available within Dataset S6 . Sequence tags were aligned to the Drosophila melanogaster April 2006 release of the reference genome using MAQ [90] . We considered those tags that aligned uniquely with less than 4 mismatches . A summary of the sequencing tag counts and unique alignment counts for each condition are supplied in Table S5 . The text files containing raw sequence tags and uniquely aligned tags were deposited into NCBI's Gene Expression Omnibus ( GEO ) [91] , accession number GSE19025 . Two programs [41] , [42] ( referred to as MACS and SPP , respectively ) were independently used to call peaks with the MAQ mapped sequences for each experimental condition . The parameters we used for each program are indicated in Figure S3 . The Subpeaks package was further used to dissect the few areas of broad MACS enrichment . Using SPP , we determined that we achieved saturation at this depth of sequencing . Either of two criteria was used to consider a peak RNAi-sensitive: 1 ) a peak coordinate was called in both the experimental and RNAi dataset and the peak is depleted in the RNAi data more than the Hsp83 promoter depletion; 2 ) a peak was only called in the experimental dataset and the corresponding region of the RNAi dataset was depleted by at least 3-fold . The intensity used to calculate depletion was defined by the normalized tag count of mapped 5′ ends in the 240 base window centered on the experimental peak center coordinate . SPP and MACS were considered to have called the same peak if the SPP peak center was within the Subpeak enrichment boundary or the broader MACS enrichment boundary . The window that corresponds to the 60 bases flanking each peak center was used as input for MEME [43] . MAST and Tallymer were used in conjunction to determine the 100% mappable ( for 40mer tags in the 400 bp window centered on the motif ) HSF-free motifs [43] , [45] . The individual labs that generated the chromatin landscape data also validated their results . Table S6 provides the respective modENCODE ID or GEO accession number for each dataset used in this study . Drosophila S2 cells were treated with 1000×20-hydroxyecdysone ( 20E ) in 2% ethanol , at a final concentration of 1 µM for 30 minutes . ChIP was performed immediately after 30 minutes of 20E , for the NHS treated cells , or after a 20 minute HS . Two independent experimental replicates were performed for “20E/NHS” and “20E/HS” . Control cells were treated with 2% ethanol as the vehicle . Two independent control samples were performed , and the values were compared to a no treatment control . Vehicle treatment was comparable to no treatment , so we combined the measurements for a total of three independent biological replicates for both NHS and HS conditions . The error bars indicate the standard error of the mean . Importantly , we calculated two important background measurements . First , we performed ChIP with Rabbit IgG for each condition , to control for non-specific pull-down by IgG or beads . Secondly , we performed ChIP-qPCR at eight regions where each factor or modification is not enriched in untreated conditions [29] , which controls for non-specific background pull-down by each antibody . Generally , the background IP by histone modification antibodies is high as measured by raw percent input , presumably do to cross reaction with unmodified histones , so this measurement is necessary in order to assign a threshold for enrichment in ChIP-qPCR assays ( the top dashed line ) . RNA levels were measured as previously described [92] . Dataset S6 contains the primer sets that were used for measuring mRNA abundance . Table S7 contains the primer sequences that were used for ChIP-qPCR .
|
Many Transcription Factors ( TFs ) have been shown to bind DNA in a sequence-specific manner . However , only a sub-set of possible binding sites are occupied in vivo , and it remains unclear how TFs discriminate between sequences of equal predicted binding affinity . We set out to determine how a specific TF , Heat Shock Factor ( HSF ) , distinguishes between utilized and unused potential binding sites . HSF is uniquely qualified to study this problem , because HSF is inactive and lowly bound to DNA in unstressed cells and upon stress HSF becomes active and strongly binds to DNA . We compared the properties of the unstressed chromatin between the sites that become HSF-bound or remain HSF-free following stress activation . We find that sites that are destined to be bound strongly by HSF after stress are associated with distinct chromatin marks compared to sites that are unoccupied by HSF after heat shock . Furthermore , chromatin landscape can be changed from a restrictive to a permissive state , allowing inducible HSF binding . These finding suggest that TF binding sites can be predicted based on the chromatin signatures present prior to induced TF recruitment .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"molecular",
"biology/histone",
"modification",
"molecular",
"biology/chromatin",
"structure",
"molecular",
"biology/transcription",
"initiation",
"and",
"activation"
] |
2010
|
Chromatin Landscape Dictates HSF Binding to Target DNA Elements
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Bacteria contain several nucleoid-associated proteins that organize their genomic DNA into the nucleoid by bending , wrapping or bridging DNA . The Histone-like Nucleoid Structuring protein H-NS found in many Gram-negative bacteria is a DNA bridging protein and can structure DNA by binding to two separate DNA duplexes or to adjacent sites on the same duplex , depending on external conditions . Several nucleotide sequences have been identified to which H-NS binds with high affinity , indicating H-NS prefers AT-rich DNA . To date , highly detailed structural information of the H-NS DNA complex remains elusive . Molecular simulation can complement experiments by modelling structures and their time evolution in atomistic detail . In this paper we report an exploration of the different binding modes of H-NS to a high affinity nucleotide sequence and an estimate of the associated rate constant . By means of molecular dynamics simulations , we identified three types of binding for H-NS to AT-rich DNA . To further sample the transitions between these binding modes , we performed Replica Exchange Transition Interface Sampling , providing predictions of the mechanism and rate constant of H-NS binding to DNA . H-NS interacts with the DNA through a conserved QGR motif , aided by a conserved arginine at position 93 . The QGR motif interacts first with phosphate groups , followed by the formation of hydrogen bonds between acceptors in the DNA minor groove and the sidechains of either Q112 or R114 . After R114 inserts into the minor groove , the rest of the QGR motif follows . Full insertion of the QGR motif in the minor groove is stable over several tens of nanoseconds , and involves hydrogen bonds between the bases and both backbone and sidechains of the QGR motif . The rate constant for the process of H-NS binding to AT-rich DNA resulting in full insertion of the QGR motif is in the order of 106 M−1s−1 , which is rate limiting compared to the non-specific association of H-NS to the DNA backbone at a rate of 108 M−1s−1 .
Bacterial chromosomal DNA is organized within the nucleoid , which is distinctly different from the cytoplasm . The organization of the nucleoid in bacteria involves a group of DNA binding proteins known as architectural proteins . The Histone-like Nucleoid Structuring protein ( H-NS ) is a architectural protein occurring in Gram-negative enterobacteria and plays a key role in the genome organization . H-NS can structure DNA by binding to two separate DNA duplexes or to adjacent sites on the same duplex , depending on external conditions [1–6] . In addition , H-NS is a global regulator of transcription as it binds to promoter regions [7–9] . As H-NS has a preference to bind to foreign genetic material , it functions as a xenogeneic silencer . Activation of foreign H-NS-silenced genes occurring in response to lethal environmental conditions links H-NS to bacterial stress resistance and virulence [10–13] . H-NS is a relatively small protein composed of 137 amino acid residues that comprises two domains: the oligomerization domain and the DNA binding domain . The first 83 residues represent the oligomerization domain and is composed of four helices [14] . This domain contains two sites for homodimerization and can multimerize into higher order structures [15] . At low concentrations , H-NS primarily exists as a dimer [1] . Residues 89-137 make up the DNA-binding domain , which consists of an antiparallel β-sheet , an α helix and a 310 helix [15 , 16] . NMR experiments on the full-length H-NS protein indicate that the oligomerization domain and DNA binding domain function independently , suggesting that a flexible linker connects the two [17] . This region , residues 65-93 , contains the most divergent amino acid sequence of H-NS-related proteins and is composed of amino acids that typically occur in linkers [17 , 18] . However , removing positively charged residues from the linker abrogates DNA binding by H-NS [19] . The loop ( residues 112-114 ) between one β strand and the α helix of the DNA-binding domain contains a conserved three amino acid sequence: QGR [4 , 17] . NMR experiments indicate that this motif interacts with the minor groove of DNA [16] in a manner similar to other H-NS related proteins , such as Ler and Lsr2 [16 , 20 , 21] . Gel electrophoresis experiments revealed that H-NS binds to curved DNA [22 , 23] . Several experiments showed that H-NS prefers to bind to conserved nucleotide sequences that are rich in AT and tend to be curved [24–28] . Recent studies using either protein binding microarrays or chromatin immunoprecipitation reveal that H-NS has a high affinity for AT-rich sequences with short A-tracts interrupted by TpA steps [3 , 10 , 24 , 28] . This is in agreement with previously reported high-affinity H-NS binding sites [25] . Varying the relative location of high affinity H-NS binding sites on plasmids with respect to each other results in different topologies of the resulting plasmid and H-NS complexes [29] . To date , highly detailed structural information on the H-NS-DNA complex is still lacking . Molecular simulation can complement experiments by providing information in atomistic detail . A coarse grained approach highlighted the relevance of protein flexibility in forming DNA bridges , yet lacks full atomistic insights [30 , 31] , that are necessary in order to consider eventual structural modifications of complexes [32 , 33] . All-atom molecular dynamics ( MD ) simulations follow a molecular system in time , and can provide the required resolution in both space and time to characterize the binding of H-NS to DNA . Recently , an MD exploration of the conformational space of an H-NS dimer showed flexible regions in the connectors between the dimerization domains and the DNA-binding domain [6] . The region linking the DNA-binding domain to the N-terminal region of H-NS is shown to be involved in DNA binding [19] . However , the relatively large system size in combination with the slow interaction dynamics in the order of microseconds to seconds , only allows for qualitative predictions , as most of the simulation time is spent with the system being in a stable state . Quantitative predictions , such as the rate constant associated with the binding of H-NS to DNA , require many transitions from one stable state to another . Assuming a binding rate constant in the order of 106 M−1 s−1 , observing a single transition would require at least 1 μs of molecular dynamics simulations . A reasonably accurate estimation of the rate constant , therefore , would require simulation times in the order of seconds , which is currently impossible . Focusing on the transitions regions by avoiding long waiting times in stable states enables a directed end efficient sampling of the transitions [34] . Path sampling techniques achieve this focusing on the transitions by performing an importance sampling procedure in trajectory space [34 , 35] . New relevant paths are generated by running relatively short MD trajectories within the transition region , providing a speed up of several orders of magnitude [35 , 36] . To compute the reaction rate , we use replica exchange transition interface sampling ( RETIS ) [35] . The method requires the definition of multiple interfaces λi along the estimated reaction coordinate . For each interface , reactive ( from state A to state B ) and non-reactive trajectories ( from state A back to state A ) are collected , from which the probability to reach the next interface can be computed . The product of these probabilities gives the reaction rate [37 , 38] . In the first part of this paper , we use all-atom molecular dynamics to characterize the different ways in which the DNA-binding domain of H-NS can bind to a high affinity nucleotide sequence AATATATT based on known H-NS binding sites [3 , 10 , 24 , 28] , containing two AT steps . In the second part of the paper , we provide mechanistic insights and a prediction of the rate of the binding of H-NS to a high affinity DNA sequence by means of RETIS simulations .
We performed Molecular Dynamics ( MD ) simulations of the following systems in explicit water: H-NS; dsDNA with nucleotide sequence GCAATATATTGC; and H-NS with GCAATATATTGC . The solution NMR structure of the DNA-binding domain of Salmonella typhimurium H-NS-like protein Bv3F ( residues 91-139 , PDB code 2L93 ) [16] served as a starting structure for H-NS . As the N-terminal end of this domain is connected to a linker in the full length protein , we placed an acetyl cap on the N-terminus to neutralize its charge . An initial structure for the dsDNA was obtained from the make-na sever developed by Stroud , J . ( 2006 ) , which models any nucleotide sequence as an ideal B-DNA structure . We chose as a high affinity sequence AATATATT based on known H-NS binding sites [3 , 10 , 24 , 28] , Containing two AT steps . This sequence is capped with GC base pairs at both ends to lower the probability of base opening at the DNA ends . The coordinates of the dsDNA and the C-terminal domain of H-NS were combined resulting in an initial minimum separation distance between H-NS and DNA of 2 . 0 nm , and are therefore not directly in contact with each other at the start of the MD simulations . Fig 1A shows a snapshot of the initial configuration . Preparation of the system consisted of placing the structures in a periodic dodecahedron box , with the box boundaries at least 1 nm from the system , followed by the addition of water molecules . To mimic experimental conditions [16] and neutralize the system , we added 50 mM NaCl by replacing water molecules with ions . Interactions between atoms are described by the AMBER03 force field [39] in combination with the TIP3P water model [40] . We selected this particular force field as it contains topologies for both amino acids and nucelotides and performs reasonably well , as long as no major conformational changes are involved [41] . For non-bonded interactions , both van der Waals and electrostatic , we used a cut-off at 0 . 8 nm . Long range electrostatic interactions were handled by the Particle Mesh Ewald method [42 , 43] with a grid spacing of 0 . 12 nm . To remove unfavorable interactions we performed energy minimization using steepest descents . By applying position restraints on the heavy atoms of the protein and DNA with a force constant in each direction of 1000 kJ/mol nm2 and performing 0 . 1ps of MD at a temperature of 300K and a pressure of 1 bar , we relaxed the water and ions around the initial structures . After preparation , we performed twenty 50 ns MD runs for the H-NS—DNA system , varying initial conditions by assigning new random starting velocities drawn from the Maxwell-Boltzmann distribution at 300K . All simulations were performed with the GROMACS software suite , versions 4 . 5 . 3 and 4 . 5 . 4 [44 , 45] at the Dutch National Supercomputer , Scinet [46] , and a locally maintained cluster , with the leap-frog integration scheme and a time step of 2 fs , using LINCS [47] to constrain bonds in the protein and SETTLE [48] to constrain water bonds . All simulations were performed in the isothermal-isobaric ensemble at a pressure of 1 bar , using the v-rescale thermostat [49] and the isotropic Parrinello-Rahman barostat [50 , 51] . During the MD simulations , the frames were stored every 20 ps . Analysis consisted of visual inspection using VMD [52] and the calculation of various geometric parameters , including the probability of finding either the protein or the DNA within 0 . 6 nm of a residue of the DNA or protein respectively , and the probability of finding either Na+ or Cl− ions within 0 . 6 nm of a residue in the protein or DNA . This is achieved by first computing the minimum distance between a given residue and the protein/DNA or ion , and then computing the probability histogram of that distance for all simulations . For the ion probability conformations in either the backbone bound or the fully inserted states were included . In addition we calculated the root mean square deviation ( RMSD ) of both H-NS and the DNA , with respect to equilibrated starting structures , including all atoms in the calculation . Also , we computed distances and number of hydrogen bonds between hydrogen bond donors in the protein and hydrogen bond acceptors in the DNA . A hydrogen bond is counted when donor and acceptor are within a distance of 0 . 35 nm and the angle between acceptor , donor and hydrogen is less than 30° . Snapshots are visualized using pymol , developed by Schrödinger , L . L . C . ( 2010 ) [53] . A promising quantitative descriptor to follow the interaction between DNA and H-NS has been found in mapping the counting the number of contacts cij between hydrogen bond acceptors in the minor groove of DNA , labeled i , and hydrogen bond donors in the QGR motif of H-NS , labeled j . Fig 1B shows a schematic representation of these hydrogen bond donors and acceptors . For each pair ij contacts are counted with the expression: i f ( r i j - d 0 ) ⩽ 0 c i j = 1 i f ( r i j - d 0 ) ⩾ 0 c i j = 1 - ( ( r i j - d 0 ) r 0 ) n n 1 - ( ( r i j - d 0 ) r 0 ) m m ( 1 ) where rij is the distance between atom i and atom j , located in the DNA and H-NS , respectively . The parameters r0 = 0 . 4 nm , d0 = 0 . 25 nm , nn = 2 , mm = 4 have been chosen such to count contacts at hydrogen bond distance ( < 0 . 35 nm ) as 1 and contacts at 0 . 7 nm as 0 . 5 . This provides a smooth and descriptive function able to discriminate between the different binding modes . Summing the contacts for all pairs results in the contact map parameter cQGR−minor: c Q G R - m i n o r = ∑ j = 1 N H - N S ∑ i = 1 N D N A c i j ( 2 ) where NDNA and NH−NS are the number of interaction sites in the DNA and in H-NS . In this contact map , hydrogen bond donors in Q112 , G113 and R114 have been included . In addition , we calculated the number of contacts between hydrogen bond donors in R93 ( atoms N , NZ , NH1 and NH2 ) and hydrogen bond acceptors in the minor groove of the AT bases cR93−minor . To discriminate between different binding modes , the contact map is also computed by considering each hydrogen bond donor in the QGR motif separately with respect to the acceptors in the minor groove of the DNA: c j = ∑ i = 1 N D N A c i j ( 3 ) with j indicating the atoms Q112-N , Q112-NE2 , G113-N , R114-N , R114-NZ , R114-NH1 , R114-NH2 in the QGR motif . The computation of the transition rate between two states , labeled A and B , requires the definition of these states in terms of a set of order parameters . By performing a series of MD simulations , it is possible to characterize the mechanism of the transition between these states and compute its rate . By defining interfaces λi along an order parameter that sufficiently describes the progression of the transition , the transition rate rAB becomes: r A B = f A B ∏ i = 0 N - 1 P i | i + 1 ( 4 ) where fAB is the flux of MD trajectories passing by interface λ0 and Pi|i+1 are the local probabilities to pass interface λi+1 given the crossing of the interface λi , called the crossing probability . In essence , the order parameter space is divided in subsections ( ensembles ) by arbitrarily positioned interfaces . The first ensemble provides the flux , which is a quantification of the frequency of the system escaping stable state A . The other ensembles provide the local probability to cross the next interface , given that the previous one has been reached . In regions with large free energy differences , the likelihood to reach the next interface becomes smaller , and therefore a higher number of interfaces is preferable . Further details on the approach , including the mathematical description to compute the flux and the local probabilities , can be found in Refs . [34 , 36 , 38 , 54] . From the MD simulations , we obtained numerical definitions for the stable states , based on the number of contacts between the QGR motif and the minor groove side of the AT basepairs cGQR−minor . The BB state is the non-specific interaction between H-NS and the DNA backbone and corresponds to values of 0 < cGQR−minor < 10 . The FI state corresponds the full insertion of the Q112 , G113 and R114 segment of H-NS , corresponds to values of cQGR−minor > 30 . An intermediate meta-stable state , state PI , has been also detected for values of cQGR−minor around 20 , which corresponds to an insertion of either Q112 or R114 into the minor groove . According to the RETIS procedure and following the developers’ guidelines [36 , 38 , 54] , interfaces have been positioned along the order parameter . If during the preparatory procedures steep gradients were observed , more interfaces were added . In other words , if the probability to cross the next interface from a particular interface becomes very low ( less than 20% as a rule of thumb ) , an additional interface should be added . Setting up a RETIS simulation is a trade-off between keeping the number of interfaces as low as possible , to reduce computational costs , and maintaining sufficient crossing . It is worthwhile to point out that an optimal interface number and positioning would improve the efficiency of the sampling , but it would not affect the final results . In cases where the potential energy surfaces is as complex as in the present study , it is computationally exceedingly expensive to achieve the optimal set up . In the binding of H-NS to DNA , a metastable state PI has been identified which imposed a division of the sampling of the overall transition in two regions: BB → PI and PI → FI . In the BB → PI transition , the interfaces has been located at values for cQGR−minor as follows: λ 0 B B - P I = 10 , λ 1 B B - P I = 12 , λ 2 B B - P I = 14 , λ 3 B B - P I = 16 , λ 4 B B - P I = 18 , λ 5 B B - P I = 20 . For the PI → FI transition the interfaces are located at values for cGQR−minor: λ 0 P I - F I = 20 , λ 1 P I - F I = 22 , λ 2 P I - F I = 24 , λ 2 P I - F I = 26 , λ 2 P I - F I = 28 , λ 3 P I - F I = 30 . Several cycles of RETIS simulations allowed for an educated guess of the interface positions . A series of snapshots have been randomly selected from a regular MD trajectory connecting the BB and FI states , covering the range of value of cQGR−minor between BB and PI and PI and FI . These trajectory fragments constituted the initial points to start the RETIS simulations . The RETIS simulations have been performed via the PyRETIS library [37] . RETIS considers three Monte Carlo based moves . Two way shooting , time reversal and swapping . A two-way shooting move consists of starting two MD simulations from a randomly selected snapshot on the previously accepted trajectory . The shooting points , to which random velocities have been assigned respecting the Boltzmann distribution of kinetic energies for the given temperature ( aimless shooting ) , are the seeds from which the new trajectories are propagated along positive and negative , times to generate a full new trajectory . Two consecutive trajectories have , in this scheme , only one point in common . In time reversal moves the trajectory is reversed in time by reversing the sequence of snapshots and by reversing the velocities of each atom in each snapshot . In the swapping moves , two trajectories that both satisfy the properties of both relative ensemble are swapped . A description of the moves and the criteria to select their relative frequency is provided in Refs . [38 , 54] . Initially , to decorrelate the paths with the initials snapshots , a total of about 400 two-way aimless shootings moves have been performed in 4 independent parallel simulations . Thereafter , the production runs consisted in in about 500 RETIS cycles for the rate estimation of the transitions BB → PI and PI → FI . A cycle corresponds to a RETIS move for each ensemble . For 12 ensembles , a total of 6000 trajectories have been considered of which 1500 generated by shooting moves , 1500 by time-reversal moves and the remaining by swapping moves [36 , 38] . The resulting acceptance path ratio has been equal to 0 . 31 , while the average path length for BBtoPI and PItoFI has been around 30000 time steps corresponding to 60 ps . The aggregate simulation time is around 200 ns . For each simulation , the order parameter and the other descriptors have been stored every 0 . 1 ps . To visualize the underling mechanisms , two-dimensional histograms of the path ensembles of the transitions BB → PI and PI → FI have been produced , projected onto pairs of selected descriptors . From all the generated paths , the relative frequency of visiting a 2D region has been computed in a grid of 500 by 500 bins which subdivided the interval between the minimum and maximum value of each respective descriptor .
Several studies have indicated that H-NS has a preference for AT-rich DNA , [25 , 26 , 28 , 29] . Additional research has shown that H-NS binds DNA with a highly conserved motif consisting of three amino acids , QGR , in the DNA-binding domain [4 , 17] . To confirm these observations with molecular dynamics , we performed twenty 50 ns MD simulations of the DNA binding domain of H-NS and an AT-rich nucleotide sequence . For the latter , we selected AATATATT , capped with GC , resulting in sequence 5’-GCAATATATTGC-3’ , based on known H-NS binding sites [3 , 10 , 24 , 28] For the part of H-NS , we used the NMR structure for residues 91-139 ( PDB code 2L93 [16] ) . The protein and the DNA were placed at a separation distance of 2 nm , see Fig 1A . Note that no hydrogen bonds are formed between the protein and the DNA at the start of the MD simulations . We have conducted MD simulations with a larger distance of separation between the protein and the DNA . These simulations resulted in many complexes with the protein attached to one of the DNA ends . Such conformations are not relevant from a physiological point of view . Visual inspection of the MD trajectories reveals that H-NS binds to the DNA in all trajectories . The protein has a strong preference for binding to the AT region in the DNA sequence , as evidenced by the high probabilities for finding protein within 0 . 6 nm of a nucleotide , see Fig 2A . The probabilities at the CG ends of the DNA arise from the protein binding to the DNA end . Fig 2B shows the probability of finding DNA within 0 . 6 nm of a protein residue . The highest probability occurs for R114 , followed by G113 , Q112 and R93 . These residues are all conserved and when altered , abolish DNA binding [16 , 19] . For the hydrogen bond donors in the residues identified as interacting with DNA ( R93 , Q112 , G113 and R114 ) we have plotted time traces of the minimum distance between the hydrogen bond donors in these residues and hydrogen bond acceptors in the minor groove of the AT basepairs d D N A - N m i n . These time traces are shown in the Supporting Information , S1 Fig . Three main H-NS binding categories can be identified , based on interactions of the QGR motif with the minor groove of the DNA: bound to the DNA backbone ( BB ) , with one side chain of the QGR motif inside the minor groove , referred to as partially inserted ( PI ) and with the entire QGR motif inside the minor groove , referred to as fully inserted ( FI ) . Fig 3 shows snapshots of these three modes . A movie of one of the trajectories showing H-NS binding to DNA and visiting the three modes is given in the Supporting Material , S1 and S2 Movies . For each run in S1 Fig we indicated in which state the MD simulation ended . The time traces S1A Fig , columns Q112NE2 , R114NH1 and R114NH2 , show that partial insertion can occur with the sidechain of either Q112 or R114 ( indicated as PI-Q and PI-R respectively ) . The distance between Q112 and th DNA fluctuates more than the distance , between R114 and the DNA , suggesting that the former may be less stable . As these MD simulations have not sampled all states exhaustively , we refrain from making more quantitative statements based on this data . Fig 2 suggested a role for R93 in the binding of DNA . The time traces of the individual interactions of all hydrogen bond donors in this residue with the minor groove indicate that this residue indeed interacts with DNA , but to a lesser extent than the GQR motif . The three interaction modes differ in number of contacts between the QGR motif and the DNA . To quantify the number of contacts and establish that the states identified by visual inspection are indeed stable states , we computed the number of contacts cQGR−minor between the hydrogen bond donors in the QGR motif and hydrogen bond acceptors in the minor groove for the AT base pairs , see Fig 1B for a schematic drawing of the number of contacts . The time traces shown in Fig 4A represent two trajectories in which all binding modes are visited . Data from all 20 simulations is collected in a probability histogram of cQGR−minor in Fig 4A , indicating the three states . The backbone bound state occurs at cQGR−minor around 10 , followed by the partial inserted binding mode at cQGR−minor around 20 . The fully inserted binding mode occurs at cQGR−minor larger than 30 . We chose the descriptor cQGR−minor to distinguish the different binding modes over the number of hydrogen bonds , as cQGR−minor allows for a clearer separation of the different binding modes . This aspect is shown in Fig 4B as a probability density plot of the number of hydrogen bonds between the QGR motif and the minor groove of the DNA hbGQR−minor and cQGR−minor . The number of hydrogen bonds increases with cQGR−minor , but does not distinguish clearly between the different binding modes . Expanding the probability histogram into a second dimension describing the RMSD of the protein with respect to the starting configuration , see Fig 4C , shows that the RMSD does not exceed 0 . 4 nm , indicating that the conformational fluctuations in the protein involve side chain re-orientation and that the overall structure of the protein changes little upon binding to DNA . Similarly , as the RMSD of the DNA remains below 0 . 4 nm , see Fig 4D , no large conformational changes occur upon binding H-NS . When H-NS is in the BB state , the QGR motif interacts with either the phosphate and/or the deoxyribose groups , see Fig 3 . R114 can form multiple salt bridges with the phosphate groups , whereas Q112 and G113 can form hydrogen bonds with the DNA backbone . In the BB binding mode , the QGR motif can adopt many different orientations with respect to the DNA backbone , some of which also involve R93 . In the PI binding mode , one residue of the QGR motif interacts with one or more bases in the minor groove of the DNA via hydrogen bonds . Partial insertion can occur with either R114 or with Q112 , see Fig 3 . When R114 is inside the minor groove , Q112 interacts with either the phosphate backbone , R93 or solvent . R114 interacts with phosphate groups when Q112 is inside the minor groove . The protein typically covers one to four base pairs when partially inserted , depending on the orientation of residues in the QGR motif not involved in minor groove interactions . Finally , full insertion of the QGR motif occurs when both Q112 and R114 form hydrogen bonds with bases in the minor groove , see Fig 3 . When fully inserted , the QGR motif is aligned with the phosphate backbone and covers three to four base pairs . G113 can also form hydrogen bonds to the bases , causing Q112 and R114 to extend such that the QGR motif covers five base pairs in the minor groove . Finally , we investigated whether the location of ions on the DNA and protein change upon binding . To this end we calculated the probability of finding a Na+ or Cl− ion close to the protein or DNA , pion , see Fig 5 . For the DNA we observe that less Na+ ions are located close to the TATT bases in the FI binding mode compared to the BB state . For the protein , changes in pion occur for both Na+ and Cl− in going form BB to FI . The drop in finding ions close to R114 clearly relates to its insertion into the minor groove . Increases in pion for sodium may indicate that in the FI state those residues have lost the transient interactions with R93 or R114 , which are now replaced by Na+ . The decrease of sodium interactions in the FI state for residues 130-139 may be a consequence of the DNA being closer to those residues . Upon binding to AT-rich DNA , the QGR motif visits various stable states with a residence time of more than 10 ns . We performed RETIS simulations to characterize the mechanisms of the transitions between these states . Starting from an initial trajectory that samples a transition , RETIS collects trajectories along the order parameter space by monitoring MD simulations [37] . Once a simulation enters a stable state , it is stopped , limiting an excessive sampling of the stable state . RETIS calculations thus permits to compute the rate constant for H-NS binding to DNA . The MD simulations show that the number of contacts between the QGR motif and the minor groove side of the AT basepairs in the DNA cQGR−minor sufficiently indicates the progression of H-NS binding to DNA . Fig 6 reports the results of the weighted histogram analysis method applied to the local crossing probabilities as a function of the main order parameter cQGR−minor for the BB to PI to FI transitions . The profile of the combined crossing probabilities is smooth , indicating that cGQR−minor is a good choice as a reaction coordinate to describe the binding of H-NS to DNA and that no additional processes play a role . Table 1 reports the values of the flux , the crossing probabilities and the resulting rate constants , and relative errors . No paths that directly connect states BB and FI have been sampled . This suggests that full insertion of the QGR motif occurs via an intermediate state in which the motif is partially inserted . Since BB and PI transitions are adjacent in the considered order parameter space , an overall rate rBBtoPI can be computed by multiplying the crossing probabilities PBBtoPI and PPItoFI by the flux out of the BB state ϕBB . The rates for the three transitions BB to PI , PI to FI and BB to FI are given in Table 1 . The errors on the estimation of the rate for transition events in complex energy lanscapes is commonly accepted to be within an order of magnitude [55] . We therefore consider the 100% error achieved to be relatively modest and within the accuracy of the force field . Further computations would , therefore , add only a very limited value without providing new insight into the process . After non-specific association to the backbone , the insertion of H-NS into the minor groove occurs in the order of 106 M−1 s−1 . When considering the binding of H-NS to DNA as limited by diffusion , the forward rate of the process is in the order of 108 M−1s−1 [56] . Including electrostatic consideration would make the non-specific association even faster [56] . We therefore conclude that the transition from BB to the fully inserted conformation is the rate-limiting step in the binding process of H-NS to DNA . By projecting the trajectories collected during the RETIS simulations onto relevant geometrical parameters allows for insights into the mechanism in the form of a probability density plot . Fig 7 shows the RETIS trajectories as a probability density projected onto cQGR−minor and several number of contact counts between the minor groove of the AT basepairs and the individual hydrogen bond donors in the QGR motif . In all trajectories the number of contacts between the hydrogen bond donors in the Arg114 sidechain ( atom NH1 ) and the DNA start to increase from 2 to 5 contacts before forming the PI state at cQGR−minor = 20 ( Fig 7E ) , while the other hydrogen bond donors do not form increasingly more contacts This observation indicates that the sidechain of R114 is the first to enter the minor groove to form the PI state . When comparing the profiles focused on the protein backbone contacts , Fig 7 ( A ) –7 ( C ) , the backbones of R114 and G113 insert before Q112 . The sidechain of Q112 is the last to enter the minor groove , which happens after cQGR−minor = 23 . We also computed the number of contacts between the hydrogen bond donors in R93 and the minor groove cR93−minor and plotted this as a probability density in Fig 7 ( F ) . The number of contacts in the overall transition from BB to FI averaged to around 3 , indicating interactions between R93 and the minor groove . In the transition from BB to PI two density spots appear at cR93−minor = [2 , 3] and cR93−minor = 4 , suggesting that R93 has multiple ways of interacting with the DNA . The calculation of cR93−minor involves counting the contacts between four atoms in R93 and the minor groove and can be qualitatively compared to cQGR−minor , which is dominated by the number of contacts between R114 and the minor groove . Compared as such , the values for cR93−minor are low , indicating that R93 does not insert in the minor groove , but rather interacts with the DNA backbone . Summarizing , binding of the H-NS DNA binding domain to the minor groove of AT basepairs occurs via the QGR motif , and also involves R93 . Our observations from both the MD and RETIS simulations suggest a sequential mechanism for the insertion of the QGR motif into the minor groove , initiated by the side chain of R114 , as summarized in Fig 8 . The time traces obtained from the MD simulations in S1 Fig all show that the FI state is formed via insertion of the sidechain of R114 followed by insertion of the sidechain of Q112 . These observations are confirmed in the RETIS simulations . The MD simulations occasionally show the formation of a PI state with Q112 inserted . This interaction fluctuates more than the interaction between R114 and the minor groove . Furthermore , the time traces show that insertion of Q112 can be followed by a return to the BB state , which is also confirmed by the RETIS simulations .
Molecular dynamics simulations revealed that the H-NS DNA-binding domain binds to DNA with its conserved QGR motif , aided by Arg93 . The association of the protein domain to DNA does not result in deformation of either the protein or the DNA . We identified three binding modes , which can be distinguished via the number of contacts between the QGR motif and the DNA . The binding modes , in order of increasing number of contacts between the protein and the bases , are BB ( backbone bound ) PI ( partially inserted ) and FI ( fully inserted ) . Replica Exchange Ttransition Interface Simulations enabled the calculation of the rate of the transition from the backbone bound state to the fully inserted state . These calculations indicate that the fully inserted state is always reached via the partially inserted intermediate , and that R114 initiates the binding . The rate of going from BB to FI is predicted to be in the order of 106 M−1 s−1 .
|
The Histone-like Nucleoid Structuring protein ( H-NS ) occurs in enterobacteria , such as Salmonella typhimurium and Escherichia coli , and structures DNA by forming filaments along DNA duplexes . Several nucleotide sequences have been identified to which H-NS binds with high affinity . Yet , obtaining highly detailed structural information of the H-NS DNA complex has proven to be a major challenge , which has not been yet resolved . By employing molecular dynamics simulations we were able to provide high resolution insights into the mechanism of DNA binding by H-NS . We identified various ways in which H-NS can bind to DNA . In all binding events , a conserved region in the protein initiates the association of H-NS to DNA . Our results show that H-NS binds in the minor groove of AT-rich DNA via a series of intermediate steps . Using advanced molecular simulation methods we predicted that the process of H-NS binding to the DNA backbone to full insertion into the minor groove occurs in the order of a million times per second , which is slower than the non-specific association of H-NS to the DNA backbone .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"sequencing",
"techniques",
"chemical",
"bonding",
"molecular",
"dynamics",
"dna-binding",
"proteins",
"sequence",
"motif",
"analysis",
"dna",
"dna",
"structure",
"molecular",
"biology",
"techniques",
"hydrogen",
"bonding",
"research",
"and",
"analysis",
"methods",
"physical",
"chemistry",
"sequence",
"analysis",
"bioinformatics",
"proteins",
"chemistry",
"molecular",
"biology",
"nucleotide",
"sequencing",
"biochemistry",
"biochemical",
"simulations",
"nucleic",
"acids",
"database",
"and",
"informatics",
"methods",
"genetics",
"protein",
"domains",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"computational",
"chemistry",
"computational",
"biology",
"macromolecular",
"structure",
"analysis"
] |
2019
|
Predicting the mechanism and rate of H-NS binding to AT-rich DNA
|
Non-malaria febrile illnesses such as bacterial bloodstream infections ( BSI ) are a leading cause of disease and mortality in the tropics . However , there are no reliable , simple diagnostic tests for identifying BSI or other severe non-malaria febrile illnesses . We hypothesized that different infectious agents responsible for severe febrile illness would impact on the host metabololome in different ways , and investigated the potential of plasma metabolites for diagnosis of non-malaria febrile illness . We conducted a comprehensive mass-spectrometry based metabolomics analysis of the plasma of 61 children with severe febrile illness from a malaria-endemic rural African setting . Metabolite features characteristic for non-malaria febrile illness , BSI , severe anemia and poor clinical outcome were identified by receiver operating curve analysis . The plasma metabolome profile of malaria and non-malaria patients revealed fundamental differences in host response , including a differential activation of the hypothalamic-pituitary-adrenal axis . A simple corticosteroid signature was a good classifier of severe malaria and non-malaria febrile patients ( AUC 0 . 82 , 95% CI: 0 . 70–0 . 93 ) . Patients with BSI were characterized by upregulated plasma bile metabolites; a signature of two bile metabolites was estimated to have a sensitivity of 98 . 1% ( 95% CI: 80 . 2–100 ) and a specificity of 82 . 9% ( 95% CI: 54 . 7–99 . 9 ) to detect BSI in children younger than 5 years . This BSI signature demonstrates that host metabolites can have a superior diagnostic sensitivity compared to pathogen-detecting tests to identify infections characterized by low pathogen load such as BSI . This study demonstrates the potential use of plasma metabolites to identify causality in children with severe febrile illness in malaria-endemic settings .
The introduction of malaria rapid diagnostic tests ( RDT ) has revealed that febrile illnesses in the tropics and subtropics are more commonly caused by non-malaria pathogens than by malaria [1–5] . Bacterial bloodstream infections ( BSI ) are considered the most severe non-malaria febrile illness , with mortality rates of 10–25% [6–8] . The high BSI mortality rates highlight the importance of accurate diagnosis and immediate correct case management , particularly in malaria-endemic regions where clinical presentation of BSI and severe malaria are similar [9] . However , in most malaria-endemic regions , children with non-malaria severe febrile illness are not easily diagnosed , which reflects a number of deficiencies in current WHO guidelines that still rely largely on malaria diagnostic tests . First of all , health care workers do not have access to tests to diagnose non-malaria febrile illness , which would inform the most effective treatment . For instance , BSI diagnosis still relies on traditional microbiology , which requires laboratory infrastructure and highly trained staff . Even when available , it takes two to three days for a result , which is too slow to timely inform case management . This can lead to ineffective first-line treatment choices that result in poorer survival outcomes [10] , or alternatively leads to over-treatment with broad-spectrum antibiotic therapy that wastes limited resources and fuels emerging antibiotic resistance [11] . Secondly , the current guidelines also fail to alert for primary non-malaria febrile illness in patients with asymptomatic or recent malaria that can have a positive malaria RDT [12] . Finally , the current guidelines fail to recognize concomitant non-malaria febrile illness in patients with severe malaria . BSI/malaria co-infections affect 5–7% of all children with malaria in Africa , and have a 2–3 fold higher mortality rate compared to patients with malaria alone [6] . An ideal test to assess severe febrile illness in malaria-endemic settings should detect more than a single pathogen . In particular , it should provide information on the type of infecting pathogen ( malaria and non-malaria ) in order to immediately inform on the best treatment and referral options . Such information is unlikely to be captured by a single biological measurement or molecule ( biomarker ) , but is expected to require a combination of molecules ( signature ) . With the development of ‘omics’ profiling approaches it is now possible to measure hundreds of biological analytes simultaneously and thus enable assessment of the diagnostic performance of a molecular signature . This study utilized metabolomics , which in contrast to studies of DNA , RNA and proteins , enables characterization of the metabolome that is the final product of all cell regulatory processes . Hence , a metabolome profile provides a read-out of the ( patho ) physiological status of a patient at the time of sampling that cannot be obtained directly from the genome , transcriptome , or proteome . In addition , metabolomics analyses only require small sample volumes ( 20–50 μL ) to determine a comprehensive metabolome profile , which is a particular advantage when studying pediatric populations . We hypothesized that the pathophysiological processes triggered by malaria and non-malaria febrile illness induce distinctive changes in the > 4 , 000 blood metabolites and explored for the first time whether such characteristic metabolites can be used for differential diagnosis of severe febrile illness .
The study was conducted according to the principles expressed in the Declaration of Helsinki and was approved by the national ethics committee of Burkina Faso , the institutional review board of the Institute of Tropical Medicine Antwerp , the ethics committee of the University Hospital of Antwerp and the human research ethics office of the University of Western Australia . Written informed consent was given by all parents or guardians of enrolled children . Patients were recruited at the district hospital Centre Médical avec Antenne Chirurgicale Saint Camille in malaria-endemic Nanoro , Burkina Faso . Admitted children < 15 years of age presenting with axillary temperature ≥ 38°C , or clinical signs of severe illness were enrolled . Medical history , physical examination and outcome of febrile episode were registered on a standardized form . At time of hospital admission , venous whole blood for blood culture , malaria diagnosis , full blood count , glucose measurements , plasma metabolome analysis , and 16S rRNA deep sequencing were collected from all participants by trained study nurses . Details of sample processing and diagnostic procedures are provided in S1 Methods . Malaria was defined as the presence of asexual Plasmodium falciparum parasites in blood smear confirmed by microscopy . All patients with a negative blood smear were classified as non-malaria . Recent malaria was defined as positive malaria HRP-2 RDT ( which can remain positive up to 6 weeks after successful treatment ) , but a negative blood smear [13 , 14] . Confirmed BSI was defined as the growth of clinical significant organisms from blood culture and/or reproducible detection of clinical significant organisms in two 16S deep sequencing experiments . Patients for which 16S deep sequencing could not be performed ( n = 5 ) were classified as ‘BSI diagnosis incomplete’; and patients with unusual but possible clinically relevant bacteria ( n = 4 ) were classified as ‘possible BSI’ . Severe anemia was defined as patients with hemoglobin levels < 5 g/dL . Patients who died in hospital following admission for severe febrile illness were classified as non-survival . Mass spectrometry-based metabolomics data were collected for all patients with 3 complementary analytical platforms: gas chromatography mass spectrometry ( GC-MS ) , C8 column liquid chromatography mass spectrometry ( C8-LC-MS ) in positive ionization mode , and C18 column ultra-high pressure liquid chromatography mass spectrometry ( C18-UHPLC-MS ) in positive and negative ionization mode . These three analytical techniques each capture a different part of the plasma metabolome due to the different chromatography column chemistries used , and thus provide a very broad coverage of the metabolome . Detailed procedures for sample preparation and data acquisition are provided in S1 Methods . Raw data were processed with dedicated data processing pipelines , which are described in S1 Methods . All metabolite data has been deposited in the metabolomics data repository , MetaboLights ( study identifier MTBLS315 ) . All data processing and statistical analyses were performed using the R software environment ( version 3 . 1 ) . This language comprises a selection of packages suitable for the implemented statistical methods: multivariate analysis ( partial-least-squares ( PLS ) regression ) , receiver-operating curve ( ROC ) analysis , correlation analysis , hierarchical cluster analysis , biomarker validation and Bayesian latent class modeling . The specific statistical methods and R packages used are explained in S1 Methods , the R scripts can be obtained upon request from the authors .
A total of 2 , 635 reproducibly detected plasma metabolite features showed a minimal degree of variation ( relative standard deviation < 15% ) in the study sample ( Table 3 ) , these features were further analyzed to fathom the metabolic nature of severe febrile illness . We first investigated with partial-least-squares regression analyses which characteristics of our study participants have a considerable influence on the plasma metabolome composition ( Fig 1 ) . Of all the tested patient characteristics , the correlated factors age/weight/height had the biggest impact on the measured metabolome ( Q2 = 61 . 5/53 . 1/69 . 3% ) , closely followed by blood glucose level ( Q2 = 48 . 1% ) and disease outcome ( survival Q2 = 46 . 5% ) . In comparison , the type of infection ( BSI , malaria ) had little impact on the measured metabolome ( Q2 = 18 . 4% and 23 . 1% respectively ) , which does however not preclude the existence of individual metabolite features that are characteristic for the type of infection . We identified the metabolite features characterizing the following five patient groups with ROC analysis: ( i ) patients with non-malaria febrile illness , ( ii ) malaria patients , ( iii ) BSI patients , ( iv ) non-survival patients and ( v ) severe anemia patients ( Table 4 ) . The details of the metabolite features are provided in S1 Data . This section focuses on the metabolite characteristics of non-malaria illness , malaria and BSI; the results for non-survival and severe anemia are described in S1 Results . We identified a group of 10 correlating metabolite features that characteristically appeared in non-malaria patients ( sensitivity range: 0 . 90–0 . 67; specificity range: 0 . 89–0 . 63; Fig 2 ) . These features include four corticosteroids , of which three were putatively identified as glucocorticoids . The correlation map shows that these corticosteroids were also markers of non-survival patients ( S1 Results ) . In addition , the non-malaria features included three highly correlating features of which one was putatively identified as an eicosanoid ( leukotriene F4 ) . Malaria patients on the other hand were characterized by a higher concentration of 16 lipids , predominantly triglycerides and ether phospholipids ( sensitivity range: 0 . 83–0 . 60; specificity: 0 . 96–0 . 63 ) . The latter group included four lipids that showed a fairly positive quantitative correlation with Plasmodium parasite density ( Pearson’s correlation: 0 . 38–0 . 45 ) . The heatmap of the 26 metabolite features characteristic for non-malaria/malaria further clarifies the distinct metabolic character of the two patient groups ( Fig 3 ) . Non-malaria patients formed two subclusters . Subcluster A had moderate increases of corticosteroids and predominantly consisted of patients who survived the febrile illness episode , while subcluster B had the highest concentration of corticosteroids seen in our study , and was associated with non-survival . The malaria patients also grouped in two major subclusters . Subcluster C was characterized by more moderate increases of the typical malaria lipids compared to subcluster D which may reflect a differential response that varies with age and/or Hb-levels ( higher median age and Hb levels in subcluster C compared to subcluster D ) . Finally , we expected patients with concomitant malaria and non-malaria febrile illness to have an increase in both the typical malaria lipids/triglycerides and the non-malaria metabolites . Such a cluster of 8 presumptive co-infection patients was indeed observed ( far right within subcluster D ) , of whom two were confirmed as a BSI/malaria co-infection and one as a recent malaria case with a BSI infection . BSI patients were found to have eight upregulated lipids , including several bile acids and alcohols , compared to patients without BSI ( sensitivity range: 0 . 83–0 . 67; specificity range: 0 . 91–0 . 73; Fig 4 ) . We could not find a plausible identification in the checked databases for half of the upregulated features , however given their correlating signal intensity profiles and chromatographic retention-time they were likely biologically and structurally related to the identified bile acids and alcohols . The plasma concentration of these upregulated bile features did not seem to be related to the particular infecting bacterial species . The metabolome features that had lower concentrations in BSI patients compared to non-BSI patients were consistent with a lower rate of lipolysis ( e . g . lower concentration of lysophospholipids ) and a disruption of fatty acid ß-oxidation ( e . g . lower concentration of plasma acylcarnitines ) . We selected a signature of two metabolites for non-malaria patients , and also for BSI patients . We assessed the diagnostic performance of the sum of the signal intensity of the 2 individual metabolites for non-malaria febrile illness and BSI respectively ( Fig 5 and Fig 6 ) . The metabolite signature for non-malaria illness consisted of a C21-corticosteroid ( m/z M-H = 377 . 196 , putative ID: C21H30O6 18-hydroxycortisol , ID_2094_ON in S1 Data ) and a steroid glucuronide ( m/z M-H = 481 . 243 , putative ID: C25H38O9 11-beta-hydroxyandrosterone-3-glucuronide , ID_3388_ON in S1 Data ) . The first steroid had an individual area under the curve or AUC = 0 . 81 ( 95% CI = 0 . 68–0 . 91 ) , adjusted p-value = 0 . 01 , fold change = 4 . 90; while the second steroid had an AUC = 0 . 79 ( 95% CI = 0 . 67–0 . 90 ) , adjusted p-value = 0 . 01 and fold change = 24 . 6 . The ROC curve analysis for the 2 steroids combined including 57 patients ( excluding confirmed malaria/BSI co-infections ) had a more favorable AUC value of 0 . 82 ( Fig 5A ) . As shown in the boxplot of Fig 5A , when considering all 61 patients , there were 8 malaria patients falsely classified as non-malaria by this signature , which included six patients that were presumptive malaria/non-malaria co-infections as indicated in subcluster D of Fig 3 . The non-malaria signature failed to identify six non-malaria patients , including two BSI patients with grown blood culture . As explained above , when present at high concentrations the same corticosteroid metabolites were predictive of non-survival . This is confirmed by the ROC-curve analysis on 60 patients ( excluding the single patient who left hospital against medical advice ) which had a very good AUC of 0 . 86 , and a cut-off value that was indeed almost two-fold higher than the cut-off value to predict non-malaria febrile illness ( Fig 5B ) . A test based on the two combined metabolites missed only 2/15 non-survival patients and thus had a good sensitivity ( 87% ) to predict non-survival . However the specificity ( 73% ) was rather poor , in our patient sample 12/45 survival patients tested false positive ( Fig 5B ) . We selected a bile acid ( m/z M+FA-H = 493 . 316 , putative ID: C27H44O5 C27 bile acid , ID_6663_ON in S1 data ) and bile alcohol ( m/z M+FA-H = 483 . 331 , putative ID: C26H46O5 27-Nor-5b-cholestane-3a , 7a , 12a , 24 , 25-pentol bile alcohol , ID_4741_ON in S1 data ) for the BSI signature . The individual diagnostic performance of the bile alcohol was AUC = 0 . 82 ( 95% CI = 0 . 69–0 . 93 ) , adjusted p-value = 0 . 07 , fold change = 2 . 54; and for the bile acid AUC = 0 . 79 ( 95% CI = 0 . 63–0 . 92 ) , adjusted p-value = 0 . 1 , fold change = 2 . 8 . The ROC-curve analysis for the 2 bile metabolites combined including 51 patients ( excluding confirmed malaria/BSI co-infections , patients with incomplete or possible BSI diagnosis ) had a good AUC of 0 . 85 ( Fig 6 ) . The signature falsely classified 4/18 confirmed BSI cases as non-BSI ( Fig 6 ) . Notably these false positives represented 4/5 BSI patients older than 5 years , suggesting that the signature was most suitable to diagnose BSI in under-fives . Eight patients ( all malaria patients ) in whom we could not detect BSI were classified by the metabolite signature as BSI ( Fig 6 ) . It is difficult to conclude whether they were falsely classified by the metabolite signature as BSI-positive , or whether they were misdiagnosed by blood culture/sequencing . We therefore estimated sensitivities and specificities of the three BSI diagnostic tests with Bayesian latent class models ( Table 5 ) . Although the confidence intervals were fairly wide given the limited sample size , the results were in line with the expectations . Regardless which age group we considered , the sensitivity of blood culture ( 50% ) was inferior to 16S sequencing ( 70% ) . The BSI metabolite signature had the best sensitivity and negative predictive value of the three tests , and reached an impressive 98 . 1% and 98 . 3% respectively when considering under-fives only . However , the specificity of the BSI signature ( 85 . 8% in all patients , 82 . 9% in under-fives only ) was inferior to that of blood culture and 16S sequencing ( > 95% for both tests ) .
We report here for the first time that malaria and non-malaria severe febrile illnesses each trigger a distinct metabolic host response affecting plasma lipid profiles and this opens new options for differential diagnosis of severe febrile illness . For BSI , one of the most severe non-malaria febrile illnesses , we identified a simple bile metabolite signature with a superior sensitivity and negative predictive value than the current tests ( blood culture and 16S sequencing ) . If our findings can be validated in large-scale studies , then such a simple metabolite signature could be the basis of a new rapid diagnostic test that could potentially reduce BSI mortality by facilitating early diagnosis and timely hospital referral of BSI patients , and reduce empirical antibiotic usage . Severe malaria was shown to affect the plasma lipid profile , with triglycerides and phospholipids being most significantly changed ( Fig 3 ) . Hypertriglyceridemia was also found in all patients with severe anemia ( Table 4 , S1 Results ) . A meta-study on the impact of malaria on plasma lipids supports our findings and demonstrated that malaria is characterized by ( i ) low serum total cholesterol , HDL and LDL which seems to be unique for malaria , and ( ii ) high triglycerides which is common to other febrile conditions [15] . The underlying biological mechanism of lipid profile changes during malaria is not fully understood yet [15] , but may be related to metabolic changes induced by both the parasite and host immune responses . Our data further suggests that some malaria phospholipid markers are moderately quantitatively correlated with parasite density , and that the overall concentration of the typical malaria lipids increases with disease severity ( young age and low Hb-levels , Fig 3 ) . Similar findings have been reported by malaria metabolomics and clinical observational studies [16 , 17] , and suggest that host metabolites characteristic for malaria may be more suitable to assess malaria disease progression than to diagnose malaria patients amongst febrile patients . In contrast to most malaria patients , non-malaria patients were characterized by the presence of immunoregulatory metabolites including corticosteroids , of which the majority were glucocorticoids , and presumably several metabolites related to the eicosanoids . Glucocorticoids are produced by the adrenal glands in response to activation of the hypothalamic-pituitary-adrenal ( HPA ) axis by the immune system when sensing challenges like infectious agents [18] . During infection these steroid hormones have immuno-suppressive effects to prevent overshooting of inflammatory and immune responses that would be detrimental [19] . Numerous studies have documented HPA activation and subsequent glucocorticoid release upon bacterial and viral infection [18] , but little is known about the HPA axis response during malaria [20–24] . One study also reported an inappropriately low glucocorticoid release in severe malaria [24] . The reason for this apparent HPA dysfunction in severe malaria is unclear , but could reflect low levels of the corticosteroid precursor cholesterol seen in malaria ( see above ) , or a deregulation of the pituitary-adrenal function caused by P . falciparum parasites . Further research is needed to understand this phenomenon , but our results already suggest that whatever the cause of the HPA dysfunction in malaria , it may be overcome by concomitant infections . Indeed , patients with the presumptive co-infections in our study were characterized by both the typical malaria lipids and the immunoregulatory metabolites found in non-malaria illness ( Fig 3 ) . The immunoregulatory metabolites also appeared to be predictive of poor clinical outcome ( non-survival ) when present in high concentrations ( Fig 3 and Fig 5B ) . There is indeed increasing evidence that an excessive proinflammatory response and an abnormal activation of the HPA axis are key determinants in progression to organ failure and death in patients with critical illness [25 , 26] . The high circulating levels of glucocorticoids are a symptom of either impaired glucocorticoid clearance or growing glucocorticoid tissue resistance which opens the door to uncontrolled systemic inflammation associated with high mortality [26] . BSI patients were marked by increased plasma levels of bile acids and alcohols , which appeared to be independent of the infecting bacterial species . Elevated plasma levels of bile acids points to endotoxemia-related cholestasis in the liver [27 , 28] , and has also been reported as a marker of sepsis patients with community-acquired pneumonia [25] . Cholestasis and the associated elevated plasma levels of bile metabolites occurs in many conditions affecting liver function but in the target population of severe febrile illness it appeared to be fairly specific for BSI patients . With an estimated sensitivity and specificity > 80% , the BSI bile signature had ( i ) a superior sensitivity than the current diagnostic tests based on pathogen detection ( sensitivity < 70% ) , ( ii ) a superior diagnostic performance than clinical assessment ( estimated at sensitivity 83% , specificity 62% ) [29] , and ( iii ) a performance that very well approaches the reported minimal requirements that should be met by a BSI rapid diagnostic test to be cost-effective in low-resource settings ( sensitivity of 83% , specificity of 94% ) [29] . The excellent sensitivity ( 98 . 1% ) and negative predictive value ( 98 . 3% ) in under-fives is particularly promising as those characteristics would allow the BSI test to be used as a screening test for patients with severe febrile illness attending primary health care service . The results of the test would allow first line health care workers to confidently withhold antibiotics from under-fives that are negative for the bile signature test , while those with a positive test result could be immediately referred to hospital for further diagnosis and care . Such a metabolite screening test needs to meet the ‘ASSURED’ criteria ( affordable , sensitive , specific , user- friendly , rapid and robust , equipment free and delivered ) to ensure that it can be used in resource-limited and remote settings [30] . Immunoassays generally meet ASSURED and are now available in two formats . The simple lateral-flow “dipstick” test is currently the most widely used format . One test-strip generally carries one or two antibodies for the detection of one or two biomarkers , and different tests can be combined in one device . A new emerging format is the microfluidic chip that allows measurement of multiple biomarkers . The chip is attached to mobile phones to read and display the test results to the healthcare worker . Prototypes of this digital test format are already being tested in Central Africa [31] . Metabolite detection with immunoassays has already been developed by the food & agriculture industry ( e . g . dipstick tests to detect bacterial toxins , pesticides , veterinary drugs ) whereby metabolite-binding antibodies are designed with computer-assisted molecular modelling [32] . The design of this study has several limitations . We acknowledge that the presented results are hypothesis-generating and large-scale validation studies are essential to validate the performance of the candidate diagnostic signatures for BSI and non-malaria illness . In this discovery study , we did not include a diagnostic test panel for viral and non-malaria parasitic infections , which could have helped to better characterize the non-malaria patient group . We did not check the identified candidate diagnostic signatures in an asymptomatic control population and in patients with non-severe febrile illness . These 2 groups should be included in future validation studies . However a recent study conducted in Tanzania reported that over 70% of pediatric febrile patients without severe clinical signs had a viral disease [33] , thus minimizing the urgency for diagnostic tests for this target population . Hence , we deem it a priority to validate the performance of the non-malaria and BSI signatures in a larger sample of patients with severe febrile illness . In conclusion , this study demonstrates that malaria and non-malaria patients with severe febrile illness have some fundamental differences in host response that could be exploited for differential diagnosis of severe febrile illness . In combination with the current malaria RDT , a rapid test assessing the plasma levels of 3–4 metabolites could inform on the likelihood of malaria , non-malaria illness , BSI , and survival and could thus empower primary health care centers in making informed treatment and referral decisions .
|
In the tropics , malaria is commonly attributed to be the cause of most childhood fevers , while in fact this condition is more commonly caused by other pathogens that are clinically indistinguishable from malaria . These so-called non-malaria febrile illnesses include bacterial bloodstream infections , which are associated with a higher mortality than malaria . Most health care facilities in the tropics have malaria diagnostic tests available , but tests for non-malarial febrile illnesses are extremely limited . There is the critical need for new tests that can address the question ‘if a febrile patient is not suffering from malaria , then what is it and what treatment will be effective ? ’ Using metabolomics , we have comprehensively screened the biochemical profile of patients with severe febrile illness for biological markers of non-malaria febrile illness . The results show that severe malaria and non-malaria febrile illness trigger a distinct metabolic response in the host . We demonstrate that this pathophysiological difference can be exploited for differential diagnosis of severe febrile illness and identification of patients with bacterial bloodstream infections .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"body",
"fluids",
"tropical",
"diseases",
"bile",
"microbiology",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"metabolomics",
"protozoans",
"metabolites",
"malarial",
"parasites",
"hematology",
"biochemistry",
"diagnostic",
"medicine",
"blood",
"anatomy",
"virology",
"physiology",
"co-infections",
"biology",
"and",
"life",
"sciences",
"malaria",
"metabolism",
"organisms"
] |
2016
|
Towards Improving Point-of-Care Diagnosis of Non-malaria Febrile Illness: A Metabolomics Approach
|
Self-organization in the cell relies on the rapid and specific binding of molecules to their cognate targets . Correct bindings must be stable enough to promote the desired function even in the crowded and fluctuating cellular environment . In systems with many nearly matched targets , rapid and stringent formation of stable products is challenging . Mechanisms that overcome this challenge have been previously proposed , including separating the process into multiple stages; however , how particular in vivo systems overcome the challenge remains unclear . Here we consider a kinetic system , inspired by homology dependent pairing between double stranded DNA in bacteria . By considering a simplified tractable model , we identify different homology testing stages that naturally occur in the system . In particular , we first model dsDNA molecules as short rigid rods containing periodically spaced binding sites . The interaction begins when the centers of two rods collide at a random angle . For most collision angles , the interaction energy is weak because only a few binding sites near the collision point contribute significantly to the binding energy . We show that most incorrect pairings are rapidly rejected at this stage . In rare cases , the two rods enter a second stage by rotating into parallel alignment . While rotation increases the stability of matched and nearly matched pairings , subsequent rotational fluctuations reduce kinetic trapping . Finally , in vivo chromosome are much longer than the persistence length of dsDNA , so we extended the model to include multiple parallel collisions between long dsDNA molecules , and find that those additional interactions can greatly accelerate the searching .
Homologous pairing of DNA molecules is involved in many fundamental biological processes , including homologous recombination in meiosis , interaction between alleles on homologous chromosomes ( transvection ) [1] , and homologous repair of double strand breaks [2] . Recent experiments have shown that dsDNA fragments in solution are capable of pairing in a homology-dependent manner even in the absence of proteins [2–8] . This protein-free mode of pairing , which can also occur in the presence of nucleosomes [7] , is robust to salt concentration , PH , and shear force , suggesting that it may serve as the ‘default’ mode of chromosome pairing in vivo [2] . Various models have been proposed to explain the homology-dependent attraction between dsDNA molecules [9–11] , many of which attribute this interaction to hydrophobic forces or electrostatics . Molecular dynamics simulations [11] show that adsorbed positive ions lie in the grooves of DNA molecules , which may suggest an attractive dipole-dipole attraction . While possible origins of attractive interactions have been considered , the kinetics of this pairing process have not been studied . The homologous pairing of dsDNA molecules is one example of a biological system in which two molecules spontaneously attach to each other as a result of attractive interactions between multiple matching binding sites . Other in vivo examples include sequence-dependent DNA or RNA binding by proteins , target binding by small regulatory RNAs , and gene editing by CRISPR/Cas9 . Such pairing processes face at least three key demands . First , they must form a product within a biologically reasonable timescale ( speed ) . Second , they must form a product that is durable enough to perform a subsequent function ( stability ) . Third , the error rate must be acceptably low for the given system ( stringency ) [12] . However , in systems where recognition involves many binding sites that contribute collectively to the interaction , the tradeoff between parameters can be incompatible with system requirements . This incompatibility has been referred to as the speed-stability-stringency ( SSS ) paradox [12–17] . Previously it has been shown that the tradeoff between speed and stability can be mitigated if the testing process is divided into multiple steps , characterized by different binding energies [14 , 15] . Additionally , mechanisms of kinetic proofreading can allow such systems to achieve arbitrarily good stringencies [18 , 19] , at the expense of searching speed . In general , the tradeoff between speed , stability , and stringency depends strongly on the environment in which the search is performed , which includes the prevalence of near matches in the sample being searched , and the average time between collisions . The effectiveness of search strategies is also influenced by specific details of the interaction , such as the binding energies of matching sites and the characteristic decay length of the binding interaction . Here , we investigate how a feature that is intrinsic to any collision between DNA duplexes–their freedom to rotate following a collision–affects the dynamics of homologous pairing . The rotational degree of freedom provides a continuum of testing stages characterized by different effective binding energies and interaction times . By analyzing an effective tractable model , we show that the rotational degree of freedom mitigates tradeoffs between speed , stability , and stringency as compared to a system in which the duplexes are rotationally constrained so that they remain aligned . This new result provides insight into dynamics of dsDNA-dsDNA pairing in vivo , and its general features may extend to other biological systems that depend on the pairing of matched binding sites .
We make a few key assumptions inspired by dsDNA-dsDNA pairing in vivo . First , we note that the crowded environment of the cell ensures that different regions of the genome collide frequently . We model each collision as an opportunity for homology testing . We assume that binding of homologous dsDNA regions promotes some function that is performed by a molecular machinery , provided that pairing of the two dsDNA regions is sufficiently persistent . We ask how long it takes for two homologous regions in a bacterial chromosome to find each other and stay bound long enough for that machinery to act . We model this action as an irreversible transition that makes the pairing permanent . RecA mediated homologous repair in bacteria may be an example of such a process . [20] . Theoretical work that describes homology dependent pairing of dsDNA highlight the importance of the helical nature of these molecules [9 , 10 , 21 , 22] . The helical structure limits the interaction sites to roughly a few bases per helical turn , creating an interacting system in which discrete periodically spaced binding sites are separated by non-interacting zones . We therefore model a collision between two regions of bacterial chromosomes as a local interaction between two rigid ‘rods’ carrying a linear array of equally spaced binding sites . The binding sites on the rods are separated by 3 . 4 nm , on the assumption that two dsDNA molecules interact roughly once per helical turn . The total length of the rods is 17 binding sites or 57 . 8 nm , which is comparable to measured values for the persistence length of dsDNA . The energy associated with interaction between the two rods is given by the sum of interactions among their binding sites , which is taken to be exponentially decaying with distance . Thus , in this model the interaction energy between the two molecules depends only on two degrees of freedom: the planar angle between the two rods , and the distance between their centers . Together , these assumptions simplify the analysis of this model considerably , providing clear results that are easy to interpret . Our simple model is illustrated in Fig 1 . We define θ as the angle between the rods . We assume that a homology test begins with a collision between two molecules at an angle θ0 . Because the spacing between binding sites is much larger than the short range of the interactions between binding sites , even a small θ limits the interaction to only a few bases near the interaction point ( Fig 1B ) . We analyze this model within the framework of a discrete state Markov model . Thermal fluctuations allow θ to grow and shrink , but can also result in a complete and irreversible unbinding . Conversely , if the two molecules remain bound long enough , an external energy-driven process stabilizes the binding or executes downstream processes . For simplicity , we assume that this happens a fixed time TD post collision , although other choices which provide a time delay can be considered . Our model does not allow for sliding of the two molecules with respect to each other . Assuming that sliding occurs on time scales that are significantly slower than rotation , such an event is captured by our model as a composition of unbinding and rebinding events . A detailed description of the model is provided in the SI . In what follows , we consider the search process from a standpoint of an individual genomic locus , or a single rod searching for a homologous partner . At the end of the paper we extend our model to include the multiple parallel interactions that are characteristic of collisions between chromosomes , which are much longer than the dsDNA persistence length .
Limiting initial interactions to a few binding sites helps systems mitigate the SSS paradox by allowing mismatched pairings to rapidly unbind from a weakly bound state; however , that mitigation is most effective if the weak initial interaction can be followed by a homology dependent transition to a more deeply bound state that stabilizes the binding of correct pairings [12–14] . In what follows , we show that the rotational degree of freedom provides both of these features . In particular , a finite collision angle θ0 limits the number of binding sites that can interact strongly , resulting in a weak initial binding; however , attractive interactions between matched pairings exert a torque that induces rotation toward a parallel alignment . This rotation brings binding sites closer together , thus increasing the binding energy and enhancing the stability of correct pairings . The challenge of finding a correct pairing depends strongly on the level of similarity among molecules in an ensemble . For concreteness , we define the ensemble by assuming that sequences are generated randomly from an alphabet of 1/q letters . Thus , two sites in two different sequences ‘accidentally’ match each other with probability q . With respect to a specific searching sequence , we group all other sequences into disjoint classes defined by the number N of consecutive matching sites around the midpoint , assumed to be the collision point between the molecules . The kinetics of the interaction between two rods depend on both N and q . In Fig 2A we show pN , the probability that a collision at initial angle θ0 leads at some point to perfectly parallel molecules ( θ = 0 ) for different values of N and for q = 1/4 . For each N we considered 2000 different randomly chosen sequences and averaged over the results for all of them . With a smaller q the number of ‘accidentally’ matching sites beyond the given N is expected to be smaller , and therefore rejecting these sequences is expected to be easier ( Fig 2A and S1 Fig ) . Our results show that without requiring any matches near the collision site ( N = 0 ) , the probability of rotating into a parallel alignment decays very rapidly with θ0 . This decay becomes slower already with a single match between the molecules ( N = 1 ) , a behavior that persists for N = 2 , 3 and saturates at N = 4 . This suggests that mismatched pairings are likely to be rejected quickly , while matched sites immediately around the collision points contribute significantly to stabilization . Importantly , the probability of proceeding from the initial binding to a parallel configuration is acutely sensitive to mismatches around the collision point , even when those mismatches are embedded in a rod that otherwise perfectly matches its target , as shown in Fig 2B . For θ0 > π/8 , even a sequence with one mismatched binding site ( M = 1 ) has significantly lower probability of reaching parallel alignment than a perfectly matched sequence ( M = 0 ) . Increasing the number of mismatched bases further reduces the probability that parallel alignment will be obtained . To place the effect of the initial collision angle in the context of the entire search process , we calculate the probability that any collision would lead to a parallel configuration as the weighted average P = ΣN fN pN of the interactions characteristic of dsDNA collisions in the sample . Here pN is again the probability that a pair with N matches near the collision point reaches a parallel alignment ( Fig 2A ) , and fN is the normalized frequency of pairs with N such matches in the ensemble of targets ( S2 Fig ) . In the context of a target search on the chromosome , this ensemble contains all possible segments of the prescribed length found in the genome of that organism . For a random set of targets , this ensemble is characterized by a single parameter q defined above , which is simply the inverse of the number of possible types of binding sites . In Fig 2C we plot the probability P for several values of q , as well as for the ensemble defined by the E . coli genome . Comparing with the same probability for a perfect pairing ( dashed line ) , it is clear that the advantage of the initial step is more significant when accidental matches are more rare . This effect persists for all collision angles . In Fig 2D , we show the probabilities of reaching θ = 0 assuming that collision angles are uniformly distributed over a half-unit sphere , p ( θ0 ) ∼ sin ( θ0 ) . We obtain these values by averaging the traces in Fig 2C from θ = 0 to θ = π/2 . This figure summarizes the main result of this section , that the transition from initial collision to parallel configuration rapidly rejects many mismatches . In the next section , we consider how speed and stringency are influenced by proofreading steps after a parallel alignment is reached . Although the period from initial binding to arrival at a parallel alignment screens against many mismatched pairings , some of these pairs will reach this deeply-bound configuration . Kinetic trapping , whereby almost-matched sequences remain bound for a significant time before they unbind and resume the search , can lead to an appreciable slowdown in the search process , especially in an ensemble that contains many similar sequences . However , as we show next , rotational fluctuations can provide a transient decrease in the unbinding barrier that reduces kinetic trapping . The reduction in kinetic trapping occurs because rotational fluctuations transiently decrease the binding energy , by increasing the separation between corresponding binding sites . To demonstrate this , we set θ0 = 0 and calculate the mean unbinding time as a function of N when rotational fluctuations are present , and compare the results with the case where θ = 0 at all times . Fig 3A shows the mean unbinding times as a function of N for various values of q . In all cases , rotational fluctuations accelerate unbinding considerably . In particular , even when the two molecules are very similar ( N near 17 ) the unbinding occurs around 100 times faster when the molecules are allowed to rotate around the collision point ( S3 Fig ) . For q = 1/4 and q = 1/2 , rotational diffusion offers a significant speed advantage even at low N values , as we discuss below . We note that the remaining q dependence of the results at high N comes mostly from interactions from sites in the two molecules that are not directly facing each other . The impact of rotational fluctuations is related with the range of angles that are visited by the two rods before unbinding . If the energy barrier for unbinding is high enough ( as it is for large N ) or if the time scale associated with these fluctuations are fast , the system achieves a quasi-equilibrium state where the distribution of angles is given approximately by the Boltzmann distribution ( S4 Fig ) . In this case , the two rods spend significant time in relatively wide angles , where unbinding is more likely . Conversely , if the barrier for unbinding is not high ( such as for small N ) , unbinding is likely to occur before arrival at significant angles , and the difference between the rotating and frozen systems is diminished . Given these results , which suggest that rotational fluctuations can accelerate unbinding of unwanted pairings , we turn to the effect of these fluctuations on the stringency of the search process , quantified by the error rate in the irreversibly paired products . In what follows , we show that the rotational degree of freedom allows high stringencies to be achieved orders of magnitude faster than they can be reached in the rotationally constrained system where θ = 0 at all times . Consistent with well-known properties of kinetic proofreading systems , increasing the time delay , TD , between the initial binding and the irreversible stabilizing process increases the stringency; however , the introduction of such a time delay slows down the search process because increasing TD also reduces the probability PT that a pairing with the true target become irreversibly bound . If a correct pairing unbinds , the search process must start again . We define the achieved specificity σ ( TD ) as the probability that a searching sequence ultimately binds to it homologous sequence . This probability is given by σ ( T D ) = f T P T ( T D ) f T P T ( T D ) + ∑ N = 1 17 f N P N ( T D ) , ( 1 ) where fT is the frequency of target sequences ( assumed to be 1 in the ensemble ) and fN is the frequency of target sequences containing N accidental matches ( S2 Fig ) . Here again PT ( TD ) is the probability that pairing with the true target remains bound at time TD , and PN ( TD ) is the same probability for an off-target with N continuous matched sites . Alternatively , the outcome of the search could be quantified in terms of the error rate η ( TD ) , defined as the probability that a searcher is bound to an off-target sequence at time TD , which is related with the specificity by η ( TD ) = 1 − σ ( TD ) . Fig 3B shows the specificity as function of TD in both the rotating system ( averaged over the collision angle as in Fig 2D ) and the rotationally-constrained model . Results are shown for three values of q , as well as for the empirical distribution of accidental matches in the E . coli genome . The characteristic time to reach a parallel configuration for q = 1/4 is plotted for reference . Smaller values of q allow higher specificity to be attained faster as mismatched pairings with less accidental matches tend to unbind more quickly . This figure demonstrates that a much shorter time delay is required to achieve a certain level of specificity if rotational fluctuations are allowed . For example , to obtain σ > 99% , the rotationally-constrained system requires a TD that is approximately 100 times larger than rotating system . Note that the q = 1/2 case cannot achieve σ = 1 because on average a bacterial genome contains ∼ 40 sequences that match the 17 consecutive binding sites in the searching sequence . In our model , these sequences cannot be distinguished from the true homologous partner . The fact that a smaller time delay TD suffices to guarantee a required specificity has a strong impact on the overall search time of the target . In general , the search period can be broken into periods punctuated by encounters with the true targets . Since the probability that such an encounter ends in irreversible binding is PT , the mean number of such periods is 1/PT . In each period , the searcher spends time τoff bound to off-targets , τdiff in free diffusion , and τtarget interacting with the true target . Together ⟨ T search ( T D ) ⟩ = 1 P T ( T D ) τ off + τ diff + τ target . ( 2 ) Since the number of off-targets is very large we generally expect τoff ≫ τtarget , and therefore neglect the latter . To compute τoff , we assume that the searcher interacts with a set of targets that obeys the statistics of the entire ensemble ( this might not be the case in vivo , as crowding in the cell may limit the searcher to a local environment in the genome , that could have its own statistical properties ) . Under this assumption , we have τ off = ∫ 0 θ max d θ 0 p ( θ 0 ) ∑ i = 1 17 f N τ N ( θ 0 ) , ( 3 ) where τN ( θ0 ) is the mean unbinding time for a pairing with N consecutive matches starting from a collision at angle θ0 . Here we allow the possibility that the collision angle between two fragments of the genome is bounded by some θmax < π/2 due to molecular crowding in the cell , as discussed below . In what follows , we let fN take the frequencies found in the E . coli genome . To compute PT ( t ) , we choose TD as the minimum time required to achieve a specificity equal to 0 . 99 of the maximum attainable value . We start our discussion by neglecting the time spent in free diffusion ( i . e . set τdiff = 0 ) , and come back to it at the end of the section . The search time is highly dependent on the amplitude ϵ of the pairwise site interaction energy ( Fig 4A , S5 and S6 Figs ) . Small ϵ lessens the binding energy difference between the true target and its close matches , thus reducing stringency; it also increases the likelihood of unbinding before forming an irreversible product . Small ϵ therefore increases the search time by increasing the delay time TD required to achieve a given stringency , and by increasing the average number binding attempts 1/PT required before an irreversible product is formed . The increase in 1/PT results from two effects: decreasing ϵ lowers the probability that the rods will rotate to θ = 0 , and increasing the likelihood of unbinding in cases where parallel alignment is achieved . Overall , when ϵ is small the rotational degree of freedom increases the search time , while when ϵ is large , and kinetic trapping impedes the search , rotations can substantially reduce the search time . Importantly , the minimum search time as a function of ϵ is smaller in the rotating system than in the rotationally constrained system if high stringency is required ( Fig 4B ) . In contrast , if low stringency is acceptable , the rotational degree of freedom may not reduce searching times ( Fig 4C ) . In the cell , crowding may weight the collision angle probability toward small angles . Such change in the distribution of the collision angle increases the probability of rotation into parallel alignment , which increase the probability that homologous pairings form irreversible products , but also reduces the fraction of mismatched pairings that are rejected immediately after collision . The balance between the two effects is demonstrated in Fig 4D , where we plot the mean search time for a different values of θmax , demonstrating that at larger ϵ the positive effect on kinetic trapping supersedes the negative effect of failing to catch the true target . Finally , we note that the results of Fig 4 may underestimate the importance of the number of rounds of binding attempts by neglecting the diffusion time τdiff . In S7 Fig we show the search time as a function of ϵ for τdiff = 1 and 100 . As expected , increasing the diffusion time reduces the overall benefit due to rotation , because it magnifies the importance of increasing PT over that of decreasing trapping . Thus , rotation is particularly beneficial if the system spends a limited amount of time diffusing , and the benefit is greatest if the diffusion time is much smaller than the time spent interacting with off-target sequences . Above , we considered an interaction between the dsDNA to be limited to a region with a length of the order of the persistence length , so both dsDNA could be modeled as rigid rods that make contact at only one single point; However , bacterial genomes are much longer than the persistence length of dsDNA , so two copies of the same genome could make simultaneous contact at multiple positions . Thus , the interaction between the two regions of the genome may consist of multiple simultaneous binding attempts . If these N searches were uncorrelated , we would expect the search time to be reduced by a factor of 1/N . Further reductions in search time can be achieved if kinetic trapping can persist for times comparable with the entire search time , since a parallel search can continue even if one searcher is kinetically trapped [16] . However , the parallel searches that occur at multiple collision points between two segments of the genome are not uncorrelated , since the regions are physically connected . For some choices of search parameters , establishment of one correct pairing rapidly leads to the establishment of several correct pairings . As long as one of those correct pairings lead to an irreversible product before all of the pairings unbinding , the search time penalty due to the unbinding of correct pairing is greatly reduced without significantly reducing the efficiency with which incorrect pairings are rejected . This advantage mainly affects the freely rotating system , where rejections of true targets are more likely . To get an insight for the contributions of multiple local collisions , consider the simple case where k searchers collide with their true target . The probability that at least one of them becomes irreversible bound , P T ′ , is given in terms of the probability PT for successful collision such that P T ′ ( k ) = 1 - ( 1 - P T ) k . ( 4 ) For example , choosing the delay time TD such that σ > 0 . 99 , we have PT ≃ 0 . 2 but P T ′ > 90 % if the number of collision points is k > 20 . This suggests a 4 . 5-fold reduction in the number of expected rounds of pairing attempts . Since this number is a main contributor to the search time at weak pairwise interactions ( small ϵ ) , we find a significant decrease in the search time in this range ( S8 Fig ) . Thus , parallel local searches extends the advantage of rotational fluctuations towards lower values of ϵ .
In our model the rotation of two molecules about their collision point represents a particular example of a degree of freedom that allows the effective interaction strengths to vary as binding progresses . Other examples may include the degree to which DNA is wrapped around histones , the relative orientation of paired histones and DNA , as well as the stem-loop RNA structures during small RNA-mRNA interactions . In this paper we assume that the pairwise interaction between binding site decays exponentially with distance; our conclusion however are not sensitive to the form of these interactions , as long as they decay rapidly ( S9 Fig ) . Although the binding energy varies continuously with θ , we have discussed the binding progression in terms of stages with different characteristic times and binding energies . As shown in Fig 3 , the reversible interactions occur on timescales that typically vary by several orders of magnitude . In order of increasing duration , the major characteristic timescales are: ( i ) the characteristic interaction time for a collision involving completely mismatched partners ( ∼ 1 ) ; ( ii ) the characteristic time required for a matched interaction to rotate the colliding partners into parallel alignment ( τrotation ∼ 100 ) ; and ( iii ) the characteristic unbinding time for near matches that have rotated into parallel alignment ( >106 × τrotation ) , which dictates the choice of the waiting time TD to irreversible transition . A direct consequence of this time scale cascade can be tested experimentally . Consider a mixture of two types of short dsDNAs that contain 3 mismatched sites . If these sites are well-separated , all in registration collisions between the molecules will lead to quick rotation into parallel configuration . However , if the 3 mismatched sites are grouped together , collisions at these sites will be quickly rejected . The expected difference in the statistics of unbinding times , which can be measured e . g . using FRET , is a signature of the rotational degree of freedom . Our system maps closely to many features of the protein/DNA recognition model proposed by Slutsky and Mirny [14] , but our simplified model allows exact calculations of the binding energy over a continuum of binding states . We propose that the weak initial interaction in the protein system corresponds to the weak initial interaction that occurs when two rods collide at a significant angle . In addition , a conformational change of the protein leading to stronger DNA binding is analogous to homology-dependent rotation of the rods into parallel alignment . Furthermore , thermal fluctuations allow proteins that incorrectly undergo conformational change to change back and continue searching , just as thermally driven angular fluctuations destabilize rod pairings that incorrectly reach parallel alignment . Our model , however , distinguishes between the role of rapid initial screen , that occurs during the transition from an initial collision angle , and the extended interrogation of the target , that occurs once a parallel alignment is achieved . Finally , if high kinetic barriers block folding to deeply bound states , then the long times required to overcome the barriers may be analogous the long delay time TD that precedes irreversible binding in the rotating system . Our representation of dsDNA as a rigid rod with one binding site per helical turn may miss important features of dsDNA-dsDNA pairing in vivo , including protein binding , histone wrapping , the helical geometry of the genome , molecular crowding , and mechanical and entropic penalties due to pairing . Nevertheless , many of the key features of our model may still contribute to understanding how multiple separated protein-free regions of nucleosomal dsDNA pair rapidly and specifically , as we discuss in the following . In general , interactions between matching genomic sections should be transient , since it is undesirable that genomic segments bind together permanently . This is consistent with our model of the first two stages in the pairing process . In vivo , the initial stages may almost always lead to dissolution of pairing rather than formation of an irreversible product; however , such transient interactions can be important if they preposition DNA for a subsequent interactions , including irreversible biochemical process such as RecA mediated repair of double strand breaks [20] . The relationship between our model and the function of RecA family proteins may extend beyond the protein’s providing a final irreversible step in the binding process . RecA protein family mediated homologous recombination may itself represent an example of an in vivo system that exploits features elucidated by the model presented in this paper . In the RecA system the final product depends on the binding between bases in one strand of a dsDNA molecule and bases in an ssDNA strand that is embedded at the center of a nucleoprotein filament formed when RecA binds to the ssDNA . In the context of recognition in the RecA system , we define a binding site as an ssDNA base . A product is formed when an ssDNA base in the filament pairs with an ssDNA base in one of the strands of the dsDNA . The ssDNA in the filament is extended by 1 . 5 x the B-form length , so homologous pairing requires that the dsDNA strand that pairs with the ssDNA also extend to 1 . 5× the B-form length . Seminal theoretical work has considered how the registration mismatch between B-form dsDNA and RecA bound ssDNA may influence homology recognition by limiting homologous contacts [23] . More recent theoretical work has considered how the extension of dsDNA that results when the dsDNA binds to RecA filaments may enhance homology recognition . That work showed that the free energy penalty due to the stretching of the bound dsDNA optimizes recognition in a system where the energy penalty was assumed to depend linearly on the number of base pairs bound to RecA [24] . Later theoretical work extended deGennes treatment of the shearing of dsDNA [25] to dsDNA bound to RecA and showed that the mechanical binding energy may include a term which is a non-linear function of the number bound dsDNA [26] . Recent molecular modeling supports the existence of such a non-linear term and indicates that the term may play a vital role in limiting the initial homology testing to 8 bp [27] . This initial 8 bp test can reject ∼ 95% of attempted pairings to unbind without further testing [28–30] . The dsDNA-dsDNA pairing considered in this work and the homology recognition mediated by RecA family proteins may share some important common features: ( 1 ) An initial interaction that limits contact between binding sites unless the initial interaction passes a homology test , in the dsDNA-dsDNA pairing case the limitation is due to the initial collision angle that creates a separation between binding sites and in the RecA case it is due to the structure of the bound dsDNA that creates a separation between binding sites . ( 2 ) Iterative homology dependent progression toward more deeply bound states . ( 3 ) A mechanism for catastrophic unbinding from fairly deeply bound states , which in the dsDNA-dsDNA system results from thermal fluctuations in the angle between the rods , but in the RecA system involves much more complicated and difficult to model interactions between the proteins and the DNA . ( 4 ) A low probability that even a correct pairing will progress to an irreversible state that is combined with simultaneous parallel testing of separated regions , which allows correct pairings to have a high probability of becoming irreversible even though each individual region has a low probability . The dsDNA-dsDNA system and RecA protein family mediated homologous recombination also have some differences . In the RecA system , the DNA and the protein constantly restructure as recognition progresses and unfavorable mechanical energy terms that depend non-linearly on the number of extended dsDNA bases play important roles , making energy calculations very challenging . The simplified dsDNA-dsDNA model considered here does not allow any deformation of the rigid rods and there are no unfavorable mechanical energy terms . We note that recent theoretical work [31] includes the possibility that the real dsDNA-dsDNA system may include a non-linear free energy term due to the torque on the dsDNA that results because the helical period of dsDNA is not an integer multiple of the number of base pairs , so torsional deformation of the helix is required in order for successive helical turns to be aligned in registration . We have shown that this term is not required to achieve recognition , but future work may consider how the presence of a non-linear term alters the results that we observed under the assumption that the rods are not deformable . In the case of the RecA system , during the initial interaction the geometry of the nucleoprotein filament limits the initial interaction between the incoming and complimentary strands to ≈ 8 bp whose binding is easily reversed , allowing ∼ 95% of attempted pairings to unbind without further testing [28–30] . The rare pairings that pass the initial test progress to more deeply bound states as more base pairs are allowed to interact . After more stable state bounds are reached , thermal fluctuations may promote catastrophic unbinding that reduces kinetic trapping [12] . Furthermore , in the RecA system correlated parallel testing may allow pairings that are homologous over >1000 bp to have a high probability of forming an irreversible product , even though the probability of irreversible pairing between ∼ 100 bp sequences is rather low [32] . Finally , parts of the model may provide insight into some of the function of other systems . For example , BRCA2 polymers with bound Rad51 may exploit multiple weak correlated parallel binding to differentiate between dsDNA and ssDNA targets [33] , and synthetic polyvalent inhibitors may also exploit the same mechanism to improve specificity at low global concentrations [34 , 35] .
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Protein folding and the binding of sequence dependent proteins to DNA are examples of self-assembling systems in which the binding energy varies continuously throughout the interaction . Previous theoretical work has highlighted the importance of dividing the interaction into separate stages characterized by interaction times and binding energies that vary by orders of magnitude . Insight into how such a division might naturally arise and promote accurate and efficient self-assembly is provided by our study of a simple tractable model inspired by the homology dependent pairing of double stranded DNA molecules in vivo . In the model , the binding energy is controlled by one single continuously tunable variable whose natural evolution creates stages that efficiently and accurately form stable products .
|
[
"Abstract",
"Introduction",
"Models",
"Results",
"Discussion"
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2017
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Mechanisms of fast and stringent search in homologous pairing of double-stranded DNA
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Alternative splicing is known to remodel protein-protein interaction networks ( “interactomes” ) , yet large-scale determination of isoform-specific interactions remains challenging . We present a domain-based method to predict the isoform interactome from the reference interactome . First , we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins . Then , we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction . Our prediction framework is of high-quality when assessed by experimental data . The predicted human isoform interactome reveals extensive network remodeling by alternative splicing . Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function , tissue expression , and disease phenotype than protein pairs interacting with the same isoforms . Our prediction method complements experimental efforts , and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing .
Protein-protein interaction ( PPI ) networks ( also known as interactome networks ) have been extensively studied in systems biology to understand genotype-phenotype relationships and several have been constructed for different model organisms such as human , yeast and bacteria [1–10] . The increase in the number of interactions reported by independent studies has led to the construction of large databases of experimentally determined PPIs , such as IntAct [11] and BioGRID [12] . In the case of human interactome mapping , some studies have used systematic yeast two-hybrid ( Y2H ) screening to generate large-scale , high-quality maps of the human binary interactome network [13] , whereas other studies have used mass spectrometry to generate catalogues of protein complexes in human cells [14 , 15] . The utility of these PPI networks can be further enhanced by annotating nodes and edges with structural domain information [16–21] . Despite their success , current network biology studies typically make the assumption that one gene encodes one protein isoform , and ignore the effect of alterative splicing ( AS ) . It is estimated that more than 100 , 000 AS events occur in pre-mRNA transcripts of human multi-exon genes [22 , 23] and that over two-thirds of human genes contain one or more alternatively spliced exons [22 , 24 , 25] . During evolution , the expansion of the proteome by AS correlates positively with the increase in species complexity [26 , 27] . In human , splicing events occur frequently in a tissue specific manner [22 , 28] and in regions located on the surfaces of proteins , which are candidates for mediating molecular interactions [29 , 30] . Moreover , AS occurs more often in transcripts which encode proteins that are involved in a high number of interactions and , through alternative inclusion or exclusion of exons , creates or eliminates protein-protein interactions [28 , 31] . Given the known impact of AS on protein function [32] , and its strong association to disease [33] , efforts to systematically relate this functional impact to its role in remodeling the human interactome recently culminated in the large-scale mapping of a human isoform interactome [34] . Subsequent analysis of this experimentally mapped isoform interactome showed that different isoforms of the same gene having different interaction profiles with other proteins tend to behave as products of different genes in terms of function , disease phenotype and tissue expression , proving that AS contributes to the functional complexity of different human cell types by creating or eliminating isoform interactions . However , due to the challenging nature of these experiments , the current human isoform interactome is far from complete where protein isoforms encoded by less than 5% of the human genome were successfully tested for PPIs . Hence , there is a great need to complement experimental efforts with the development of computational methods for accurate prediction of isoform interactions . Such computational predictions also enable us to assess the general applicability of insights gained from the size-limited experimental datasets on the human isoform interactome . In this paper we present a computational method , named DIIP: Domain-based Isoform Interactome Prediction , that predicts the isoform interactome from an experimentally determined reference interactome , with application to human ( Fig 1 ) . Starting with experimentally determined interactions between reference proteins , we map known structural domains onto proteins and known domain-domain interactions ( DDIs ) onto PPIs , and construct a domain-resolved reference interactome where PPIs are annotated with DDIs . Next , we construct an isoform interactome by expanding this domain-resolved reference interactome to include interactions predicted for alternative isoforms of reference proteins . Specifically , for each interaction involving a reference protein , an alternative isoform is predicted to maintain the interaction if the DDI mediating the interaction is retained , and predicted to lose the interaction if the DDI mediating the interaction is lost . We apply our computational method to predict isoform interactomes from two human reference interactomes , the high-quality HI-II-14 reference binary interactome [13] and the larger interactome from IntAct [11] . We find extensive network remodeling by alternative splicing: ~22% of genes with two or more isoforms in the predicted isoform interactome have at least one isoform losing an interaction , and ~18% of isoform pairs encoded by the same gene in the isoform interactome have different interaction profiles . In addition , we find that compared to protein pairs interacting with the same subset of isoforms of the same gene , protein pairs interacting with different subsets of isoforms of the same gene tend to be more divergent in terms of function , disease phenotype and tissue expression . Our predicted isoform interactome explores a different part of the isoform space than the experimentally mapped isoform interactome of Yang et al . ( 2016 ) [34] . Despite this minimal overlap , our results are consistent with the results of Yang et al . ( 2016 ) , indicating that these results are broadly applicable to the human isoform interactome . Finally , using the experimentally mapped interactome of Yang et al . ( 2016 ) as a benchmark dataset , we show that our computational framework for predicting isoform interactions is of high-quality , performing better than random expectation . All together , our results show that our computational method complements experimental efforts , and that integrating structural domain information with PPI networks provides insights into the functional impact of AS on different human cell types through remodeling of the human interactome network .
We used the 11 , 557 DDIs combined from the 3did database of three-dimensional interacting domains [35] and the DOMINE Database of Protein Domain Interactions [36] which were inferred from Protein Data Bank ( PDB ) entries to construct two domain-resolved reference interactomes for human . The first domain-resolved reference interactome was constructed by annotating with DDIs the high-quality HI-II-14 human reference binary interactome [13] which consists of 13 , 427 interactions between 4 , 303 proteins . The resulting domain-resolved interactome consists of 917 proteins and 901 annotated interactions ( S1 Table ) . The second domain-resolved reference interactome was constructed by annotating with DDIs the larger human interactome from IntAct [11] ( retrieved May 2016 ) which consists of 9 , 023 proteins and 29 , 775 interactions reported by two or more experiments . The resulting domain-resolved reference interactome consists of 2 , 944 proteins and 4 , 363 annotated interactions ( S2 Table ) . From each domain-resolved reference interactome , we predicted an isoform interactome that includes experimentally determined interactions between reference proteins , as well as predicted interactions for the alternative isoforms of these proteins , where the isoform data are obtained from UniProt [37] . We predicted isoform interactions using the following rule: Given an experimentally determined interaction between two reference proteins annotated with one or more DDIs , we predict that an alternative isoform of one protein loses its interaction with the other protein if the isoform interaction loses all of the above-mentioned DDI annotations , otherwise the interaction is predicted to be retained . The predicted HI-II-14 isoform interactome consists of the 901 experimentally determined reference interactions , 2 , 185 predicted retained interactions and 303 predicted lost interactions involving the 917 reference proteins and their 1 , 227 alternative isoforms ( S3 Table ) , whereas the predicted IntAct isoform interactome consists of the 4 , 363 experimentally determined reference interactions , 12 , 651 predicted retained interactions and 1 , 709 predicted lost interactions involving the 2 , 944 reference proteins and their 4 , 471 alternative isoforms ( S4 Table ) . The lost interactions are spread among a large number of genes: 22 . 4% ( 130 of 580 ) of genes with two or more isoforms in the predicted HI-II-14 isoform interactome have at least one isoform losing one or more interactions , whereas 21% ( 402 of 1 , 911 ) of genes with two or more isoforms in the predicted IntAct isoform interactome have at least one isoform losing one or more interactions . Widespread remodeling of the human interactome by AS can also be seen at the level of isoform pairs . 18 . 8% of isoform pairs encoded by the same gene in the predicted HI-II-14 isoform interactome have different interaction profiles , whereas 16 . 5% of isoform pairs encoded by the same gene in the predicted IntAct isoform interactome have different interaction profiles . Altogether , these observations highlight the extensive role of AS in preserving or eliminating isoform interactions , hence creating different interaction profiles for different isoforms of the same gene . Next , we focused on protein pairs interacting with the same target protein in the reference interactome , and classified these protein pairs into those interacting with the same subset of isoforms of the same target gene , and those interacting with different subsets of isoforms of the same target gene ( Fig 2A ) . The vast majority of protein pairs considered here belong to only one category; protein pairs belonging to more than one category for different target genes are extremely rare , and are excluded from further analysis . In the predicted HI-II-14 isoform interactome , we identified 1 , 437 protein pairs interacting with the same subset of isoforms of the same gene , and 63 protein pairs interacting with different subsets of isoforms of the same gene . In the predicted IntAct isoform interactome , we identified 20 , 685 protein pairs interacting with the same subset of isoforms of the same gene , and 2 , 669 protein pairs interacting with different subsets of isoforms of the same gene . Therefore , AS is capable of creating a wide variety of isoform interaction profiles for proteins interacting with the same target protein . We retrieved Gene Ontology ( GO ) associations from the UniProt-GOA database [38] and constructed a GO association profile for each protein in the reference interactome . We then used the Jaccard similarity index to calculate GO similarity for each protein pair in which at least one protein is GO annotated . Finally , we systematically calculated and compared GO similarity for different types of protein pairs: those interacting with the same subset of isoforms of the same gene , those interacting with different subsets of isoforms of the same gene ( as defined in the previous section ) , as well as those interacting with protein products of different genes only . In the HI-II-14 isoform interactome , compared to protein pairs interacting with the same subset of isoforms of the same gene , protein pairs interacting with different subsets of isoforms of the same gene are less similar in molecular function , biological process , as well as all three GO categories combined ( p < 10−5 each ) , but the similarity is not significantly different in cellular component ( p = 0 . 25 ) ( two-sided bootstrap test with 100 , 000 resamplings; Fig 2B ) . On the other hand , compared to protein pairs interacting with protein products of different genes , protein pairs interacting with different subsets of isoforms of the same gene are more similar in cellular component ( p = 2 x 10−3 ) , but less similar in molecular function ( p < 10−3 ) . However , the difference is not statistically significant in biological process ( p = 0 . 054 ) and all three GO categories combined ( p = 0 . 71 ) ( two-sided bootstrap test with 1 , 000 resamplings; Fig 2B ) . In the IntAct isoform interactome , compared to protein pairs interacting with the same subset of isoforms of the same gene , protein pairs interacting with different subsets of isoforms of the same gene are less similar in molecular function , biological process , cellular component , as well as all three GO categories combined ( p < 10−5 each , two-sided bootstrap test with 100 , 000 resamplings; Fig 2C ) . On the other hand , compared to protein pairs interacting with protein products of different genes , protein pairs interacting with different subsets of isoforms of the same gene are more similar in biological process , cellular component , as well as all three GO categories combined , but less similar in molecular function ( p < 10−3 each , two-sided bootstrap test with 1 , 000 resamplings; Fig 2C ) . Altogether , our results show that protein pairs interacting with different subsets of isoforms of the same gene tend to be more divergent in biological function than protein pairs interacting with the same subset of isoforms of the same gene . In addition , they tend to be less divergent in biological function than protein pairs interacting with protein products of different genes , albeit to a lesser degree . Notably , for the GO molecular function aspect , protein pairs interacting with different subsets of isoforms of the same gene can be as divergent as protein pairs interacting with protein products of different genes . Our results therefore demonstrate that AS increases the functional complexity of human cells by remodeling the human interactome network . Disruptions in AS events in human are associated with a wide range of known diseases [33] . To investigate the extent to which protein pairs interacting with different subsets of isoforms of the same gene are associated with different disease phenotypes , we retrieved gene-disease associations from the DisGeNET database [39 , 40] and constructed a disease annotation profile for each human reference protein . Because the fraction of human proteins with disease annotations is small , we further constructed a disease subnetwork profile for each human reference protein where a protein belongs to a specific disease subnetwork if that protein or any of its interaction partners in the unbiased , high-quality HI-II-14 reference binary interactome is annotated with the disease . We then used the Jaccard similarity index to calculate the fraction of disease subnetworks shared by pairs of reference proteins each annotated with at least one disease subnetwork , where two proteins share a specific disease subnetwork if each of the two proteins or any of its interaction partners in the HI-II-14 reference binary interactome is annotated with that disease . Finally , we systematically calculated and compared disease subnetwork sharing for different types of protein pairs: those interacting with the same subset of isoforms of the same gene , those interacting with different subsets of isoforms of the same gene ( as defined previously ) , and those interacting with protein products of different genes only . In the HI-II-14 isoform interactome , protein pairs interacting with different subsets of isoforms of the same gene ( n = 47 ) tend to share a smaller fraction of disease subnetworks than protein pairs interacting with the same subset of isoforms of the same gene ( n = 1 , 241 ) ( p < 10−5 , two-sided bootstrap test with 100 , 000 resamplings; Fig 3A ) . In addition , protein pairs interacting with different subsets of isoforms of the same gene tend to share a larger fraction of disease subnetworks than protein pairs interacting with protein products of different genes ( p = 0 . 024 , two-sided bootstrap test with 1 , 000 resamplings; Fig 3A ) . In the IntAct isoform interactome , protein pairs interacting with different subsets of isoforms of the same gene ( n = 196 ) also tend to share a smaller fraction of disease subnetworks than protein pairs interacting with the same subset of isoforms of the same gene ( n = 3 , 226 ) ( p < 10−5 , two-sided bootstrap test with 100 , 000 resamplings; Fig 3B ) , as well as a larger fraction of disease subnetworks than protein pairs interacting with protein products of different genes ( p < 10−3 , two-sided bootstrap test with 1 , 000 resamplings; Fig 3B ) . All together , our results show that compared to protein pairs with identical isoform interaction profiles , protein pairs with different isoform interaction profiles tend to be more divergent in disease phenotype , consistent with the experimental results of Yang et al . ( 2016 ) [34] . Our results therefore demonstrate that by remodeling the human interactome network , AS creates divergence in disease phenotype among protein pairs interacting with different isoforms of the same gene . Since tissue expression strongly correlates with biological function and disease phenotype [41 , 42] , we investigated the extent to which protein pairs interacting with different subsets of isoforms of the same gene diverge in tissue expression , in addition to divergence in biological function and disease phenotype . We used the Illumina Body Map 2 . 0 RNA-Seq dataset [43] to quantify gene expression in 16 different body tissues , and calculated tissue co-expression using Pearson’s correlation coefficient for pairs of reference proteins with both expression levels simultaneously quantified in at least 8 tissues . In the HI-II-14 isoform interactome , protein pairs interacting with different subsets of isoforms of the same gene ( n = 62 ) tend to be less co-expressed than protein pairs interacting with the same subset of isoforms of the same gene ( n = 1 , 182 ) ( p = 5 . 5 x 10−3 , two-sided bootstrap test with 100 , 000 resamplings; Fig 4A ) . In the IntAct isoform interactome , protein pairs interacting with different subsets of isoforms of the same gene ( n = 2 , 609 ) also tend to be less co-expressed than protein pairs interacting with the same subset of isoforms of the same gene ( n = 19 , 678 ) ( p < 10−5 , two-sided bootstrap test with 100 , 000 resamplings; Fig 4B ) , and more co-expressed than protein pairs interacting with protein products of different genes ( p < 10−3 , two-sided bootstrap test with 1 , 000 resamplings; Fig 4B ) . Our results show that protein pairs interacting with different subsets of isoforms of the same gene tend to be more divergent in tissue expression than protein pairs interacting with the same subset of isoforms of the same gene , consistent with the experimental results of Yang et al . ( 2016 ) [34] . Here , we present case studies of two types of protein pairs predicted by our method to interact with different subsets of isoforms of the same gene . We first looked at the epidermal growth factor EGF and the growth factor receptor-bound protein GRB2 , which were predicted by our method to interact with different subsets of isoforms of the epidermal growth factor receptor EGFR ( UniProt ID: P00533 ) ( Fig 5A ) . Our method predicted that EGF interacts with EGFR ( P00533 ) and its three shorter alternative isoforms ( P00533-2 , P00533-3 , P00533-4 ) , whereas GRB2 was predicted to interact with EGFR ( P00533 ) and lose interaction with all its alternative isoforms . All of these predictions are consistent with experiments . By interacting with EGFR , EGF and GRB2 carry out different functions in protein signaling . EGF activates P00533 by binding to its extracellular ligand binding ( LB ) domain , hence inducing autophosphorylation of its protein kinase ( PK ) domain [44] . GRB2 binds to the tyrosine-phosphorylated PK domain of P00533 through its SH3 domain , hence connecting growth factor stimulation to other intracellular signalling pathways [45] . P00533 consists of three main parts: the extracellular domain ( exons 1–16 ) containing the LB domain , the transmembrane ( TM ) domain ( exons 16–18 ) , and the intracellular domain ( exons 18–24 ) containing the PK domain . The second isoform ( P00533-2 ) contains a large part of the extracellular domain which retains LB function [46] , whereas the third and fourth isoforms ( P00533-3 and P00533-4 ) contain the whole extracellular domain [47] . All three alternative isoforms of P00533 , however , lack the TM domain and the intracellular domain . Therefore , EGF interacts with all four isoforms of EGFR on their LB domains , whereas GRB2 interacts with P00533 on its PK domain and loses interaction with the other three truncated isoforms . These three truncated isoforms have different tissue expression patterns [48] , are expressed in different cancers [49–51] , and may play a role in supressing cell growth by inhibiting EGFR [50 , 51] . We also looked at the protein pair XIAP and APAF1 that were predicted by our method to interact with different subsets of isoforms of CASP9 ( UniProt ID: P55211 ) ( Fig 5B ) . Our method predicted that APAF1 interacts with CASP9 ( P55211 ) and its alternative isoforms P55211-2 and P55211-3 , and loses interaction with P55211-4 . All of these predictions are consistent with experiments . Our method also predicted that XIAP interacts with CASP9 ( P55211 ) and its alternative isoforms P55211-2 , P55211-3 and P55211-4 . All of these predictions are supported by experiments with the exception of XIAP’s zinc finger-mediated interaction with P55211-3 , which retains the CARD domain known to interact with zinc fingers [52] . XIAP and APAF1 are known to carry out antagonistic functions in activating and inhibiting apoptosis . This is also true for their CASP9 isoform partners . The CASP9 gene encodes four isoforms: P55211 ( CASP9-alpha ) which has the longest sequence ( 416 residues ) , P55211-2 ( CASP9-beta ) which lacks a central large sequence segment ( residues 140–289 ) , P55211-3 ( CASP9-gamma ) which lacks the catalytic domain ( residues 139–416 ) and only has the caspase recruitment domain ( CARD ) ( residues 1–92 ) , and P55211-4 that lacks the CARD domain but has the catalytic domain . XIAP inhibits apoptosis by binding to the catalytic domain of P55211 through its BIR3 domain thus inhibiting its catalytic activity [53] . On the other hand , APAF1 activates apoptosis by forming an apoptosome complex with P55211 through CARD-CARD interaction [54] . The isoforms of CASP9 also play different roles in apoptosis due to their different interaction profiles . P55211-3 which lacks the catalytic domain containing the active site for catalysis interacts with APAF1 through its CARD domain , interfering with the formation of the apoptosome and therefore functioning as an endogenous inhibitor of apoptosis [55] . Similarly , P55211-2 functions as an endogenous inhibitor of apoptosis by interacting with APAF1 through its CARD domain while at the same time losing a large part of its catalytic domain , even though it retains some residual interaction with XIAP [56] . On the other hand , P55211-4 , which lacks the CARD domain , interacts with XIAP but does not interact with APAF1 , suggesting that it may not inhibit apoptosis . Moreover , the interaction of P55211-4 with the apoptosis inhibitor XIAP suggests that it may promote apoptosis , contrary to P55211-2 and P55211-3 . Overall , these two case studies highlight specific mechanisms for how AS-mediated remodeling of interactions of the isoforms of the same gene leads to divergence in their biological function and disease phenotype , and illustrate that our computational method is capable of identifying biologically relevant isoform-specific interactions . Here , we empirically assessed the quality of our isoform-interaction prediction method by validating it against the experimental dataset of Yang et al . ( 2016 ) [34] , which is the only genome-wide isoform interactome dataset available so far . However , the experimental dataset of Yang et al . ( 2016 ) is small in size for our purpose of validation: there are only 310 reference interactions between reference proteins , among which we were able to annotate only 34 reference interactions with known DDIs . We then predicted isoform-specific interactions from these 34 DDI-annotated reference interactions using our method , and compared our predictions with experiments . In terms of predicting interaction loss events , we obtained a true positive rate ( TPR ) of 0 . 33 ( 9 out of 27 experimental interaction loss events are correctly predicted ) , and a false positive rate ( FPR ) of 0 . 2 ( 3 out of 15 experimental interaction retention events are incorrectly predicted ) . A random predictor would give a TPR equal to the FPR whereas a TPR that is larger than the FPR indicates that predictions are better than random . Our observed TPR is larger than the observed FPR , however the difference is not statistically significant due to small sample size ( p = 0 . 48 , two-sided Fisher’s exact test ) . To increase the sample size , we expanded the 34 reference interactions with full DDI annotations in the dataset of Yang et al . ( 2016 ) by including 226 additional reference interactions with partial DDI annotations for a total of 260 domain-annotated reference interactions . A reference interaction has a partial DDI annotation if a reference protein with multiple isoforms contains an interacting domain of a DDI whereas its interaction partner does not contain the other interacting domain of the DDI . An alternative isoform of the reference protein is then predicted to lose the interaction if it loses all of its interacting domains , otherwise the interaction is retained . Using this method , we predicted isoform-specific interactions from these 260 domain-annotated reference interactions , and compared our predictions with experiments . In terms of predicting interaction loss events , we obtained a true positive rate ( TPR ) of 0 . 31 ( 53 out of 171 experimental interaction loss events are correctly predicted ) and a false positive rate ( FPR ) of 0 . 10 ( 12 out of 117 experimental interaction retention events are incorrectly predicted ) . Similar to the rates obtained above , the observed TPR here is three times larger than the observed FPR and the difference is statistically significant due to much larger sample size ( p = 2 . 6 x 10−5 , two-sided Fisher’s exact test ) , indicating that our prediction method is of high-quality , performing significantly better than random predictions . Since the benchmark experimental dataset may contain errors , the actual TPR of our method is expected to be even higher than the observed TPR .
Our computational method for predicting isoform-specific interactions is reliable because it does not aim to predict new PPIs from scratch . Rather , it starts with the annotation of experimentally determined PPIs with known DDIs , and predicts the retention or loss of PPIs based on the retention or loss of the annotated DDIs in different isoforms . All these individual steps are expected to generate high-quality predictions; thus our overall isoform-interaction predictions are expected to be of high quality as well . Indeed , the high quality of our predictions is confirmed by experimental validations . At the same time , our method is limited in that it can only predict isoform interactions from known reference PPIs . If a reference protein does not have any interactions in the reference interactome , it is not possible to predict interactions for any of its alternative isoforms . Therefore , the size of the isoform interactome predicted by our method is ultimately constrained by the size of the reference interactome . Since our method predicts isoform interactions based on experimentally determined interactions between reference proteins , it is important to ensure the high quality of these experimentally determined interactions . Interactions in the HI-II-14 reference binary interactome are of high-quality since they were subjected to multiple screening and several other quality-control measures . On the other hand , interactions in the IntAct reference interactome were curated from different sources with varying quality . Furthermore , IntAct may contain indirect interactions between proteins in the same complex . To ensure the high quality of the IntAct-derived interactions , we only included physical interactions in IntAct reported by at least two independent experimental studies . In addition , our DDI-annotated interactome significantly enriches for direct binary interactions and filters out indirect interactions , as indirect interactions are much less likely to be annotated with DDIs than direct binary interactions . For reference PPIs with multiple DDI annotations , our method imposes a strict requirement that for an interaction to be lost , all DDI annotations of that interaction must be lost , otherwise the interaction is retained . Assuming that the interactivity of different domains is independent of each other within the same protein , this strict requirement maximizes the accuracy of predicted lost interactions , at the cost of possibly reducing the accuracy of predicted retained interactions . It should be noted that this is not a significant problem in our study , as about half of the PPIs in the HI-II-14 and IntAct domain-resolved interactomes have only one DDI annotation ( 51% and 46% , respectively ) . While our predicted isoform interactomes reveal extensive network remodeling by AS , the ratio of remodeled protein pairs ( i . e . , the ratio of the number of protein pairs interacting with different subsets of isoforms of the same gene to the number of protein pairs interacting with the same subset of isoforms of the same gene ) is different in the two predicted isoform interactomes ( 12 . 9% in IntAct , and 4 . 4% in HI-II-14 ) . This difference is due to systematic differences in the types of interactions reported in the two reference interactomes and in the fraction of protein hubs between the two domain-resolved interactomes . Since proteins with significant medical and scientific interests are intensely studied in the literature , protein hubs are much better annotated with domains in the literature-curated IntAct interactome than in the systematic HI-II-14 interactome . Indeed , protein hubs with vertex degree >23 ( ~5% of proteins in both binary interactomes ) are 3 . 4 times more likely to have a domain-annotated interaction in the IntAct interactome than in the HI-II-14 interactome , whereas average proteins are only 1 . 6 times more likely to have a domain-annotated interaction in the IntAct interactome than in the HI-II-14 interactome . As a result , the fraction of protein hubs is significantly larger in the IntAct domain-resolved interactome than in the HI-II-14 domain-resolved interactome ( 11 . 1% in IntAct and 5% in HI-II-14 , for hubs with vertex degree >5 ) . A larger fraction of protein hubs in the IntAct domain-resolved interactome is the major cause for the observed larger ratio of remodeled protein pairs in the IntAct isoform interactome , since protein pairs interacting with different subsets of isoforms of the same gene can only be created by isoforms that lose some but not all interactions ( which is more likely to occur for isoforms of a protein hub with many interactions ) , and cannot be created by isoforms that lose all interactions at once ( which is more likely to occur for isoforms of a protein non-hub with few interactions ) . Indeed , the observed difference ( 12 . 9% in IntAct , and 4 . 4% in HI-II-14 ) in the ratio of remodeled protein pairs between the two isoform interactomes is almost completely eliminated ( 6 . 1% in IntAct , and 5 . 2% in HI-II-14 ) upon the removal of all protein hubs with vertex degree >5 . This observed difference does not significantly bias our isoform-specific interaction predictions for the following reasons: we predict isoform-specific interaction retention/loss one isoform at a time based on sequence information only; no significant differences are observed between the two isoform interactomes ( IntAct and HI-II-14 ) in terms of the fraction of isoforms per gene losing at least one interaction ( 8 . 2% and 8 . 6% ) and the fraction of isoform pairs per gene with different interaction profiles ( 14 . 2% and 15 . 3% ) ; we only draw conclusions from comparing protein pairs in terms of their biological properties , and from observations that are consistent between the two isoform interactomes . Detailed sequence information is available for some reference proteins , but not others . When the precise sequence information is not available , we chose the reference isoform designated by UniProt to represent each reference protein in the reference interactomes . These UniProt-designated reference isoforms are typically either the longest isoform ( ~88% ) or the most prevalent isoform; hence they are most likely the ones used in different experimental studies to map the reference interactions . In the unlikely event that some reference proteins in the reference interactomes are represented by a different isoform than the one designated by UniProt , our method only requires the knowledge that a reference PPI exists , and can still work well without knowing the exact sequence of the reference protein . Given the experimental knowledge that a reference PPI exists , our method predicts that an alternative isoform loses this interaction if and only if the isoform loses all possible interacting domains present in the UniProt-designated reference isoform with typically the longest sequence , which in half of the cases is just one interacting domain . Hence , our predictions of interaction loss and retention remain very reasonable even in the presence of minor discordances in reference isoform annotations . In summary , we developed a domain-based computational method for predicting an isoform interactome from a reference interactome by integrating structural domain information with experimentally determined interactions . Our predictions reveal extensive remodeling of the human interactome network by AS: ~22% of genes with two or more isoforms in the predicted isoform interactome have at least one isoform losing an interaction , and ~18% of isoform pairs encoded by the same gene in the isoform interactome network have different interaction profiles . Our isoform-interaction prediction framework is of high quality as it performs significantly better than random predictions when assessed by experimental data . In addition , our predicted isoform interactome is larger and probes a different part of the isoform space than the experimental isoform interactome of Yang et al . ( 2016 ) [34] . In terms of the space of genes with at least two isoforms tested for interactions , our predicted isoform interactomes cover ~4 times larger gene space than Yang et al . ( 2016 ) . Only ~19% of the gene space covered by our predicted HI-II-14 isoform interactome is covered by Yang et al . ( 2016 ) , and only ~8% of the gene space covered by our predicted IntAct isoform interactome is covered by Yang et al . ( 2016 ) . Despite this minimal overlap , the biological insights provided by our predicted isoform interactome are largely consistent with Yang et al . ( 2016 ) . Compared to protein pairs interacting with the same subset of isoforms of the same gene , protein pairs interacting with different subsets of isoforms of the same gene tend to be more divergent in biological function , disease phenotype , and tissue expression . Thus , our computational study complements large-scale experimental efforts on mapping the human isoform interactome , and highlights the broad applicability of AS-mediated interactome remodeling as a driving force for the functional divergence of different isoforms encoded by the same gene .
Domain-domain interactions ( DDIs ) were retrieved from the 3did Database of Three-Dimensional Interacting Domains [35] ( retrieved May 2017 ) and the DOMINE Database of Protein Domain Interactions [36] ( retrieved Oct 2015 ) . For DOMINE DDIs , we kept the 6 , 634 DDIs inferred from Protein Data Bank ( PDB ) entries , and excluded those DDIs predicted by computational methods . We then combined the 6 , 634 PDB-inferred DDIs from DOMINE with the 10 , 593 PDB-inferred DDIs from 3did . After removing duplicates , we obtained a total of 11 , 557 DDIs . To annotate PPIs with DDIs , we first annotated proteins in the reference interactome with structural domains . Gene Entrez IDs in the HI-II-14 reference interactome were mapped to Swiss-Prot IDs using the Retrieve/ID mapping tool provided by UniProt [37] . After removing self-interactions , 4 , 091 genes with unique SwissProt IDs were then used for further analysis . Swiss-Prot IDs for genes in the IntAct reference interactome were provided by the IntAct database . We then retrieved protein sequences from UniProt and scanned them for Pfam domains using HMMER hmmscan [57] with an E-value cutoff of 10−5 . After annotating each protein in the reference interactome with its structural domains , each PPI in the reference interactome was annotated with a DDI if one of the interacting proteins was annotated with an interacting domain of the DDI and the other interacting protein was annotated with the other interacting domain of the same DDI . Only PPIs annotated with at least one DDI were included in the domain-resolved interactome . Alternative isoforms of each reference protein in the domain-resolved reference interactome were annotated with structural domains by retrieving their sequences from UniProt and scanning them for Pfam domains using HMMER hmmscan [57] with an E-value cutoff of 10−5 . Then , for each interaction between two reference proteins in the domain-resolved reference interactome annotated with one or more DDIs , we predicted that an alternative isoform of one protein loses its interaction with the other protein if the isoform interaction loses all the above-mentioned DDI annotations . If the isoform interaction keeps at least one of the DDI annotations , the interaction was predicted to be retained . We retrieved Gene Ontology ( GO ) associations from the UniProt-GOA database [38] ( retrieved Feb 2016 ) , which provides a set of 16 , 329 controlled hierarchical GO terms split into three categories: 3 , 812 molecular function terms , 11 , 042 biological process terms , and 1 , 475 cellular component terms . GO terms were mapped onto reference proteins using the Swiss-Prot IDs associated with each GO term . We quantified GO similarity between two proteins by calculating the Jaccard similarity index of their GO association profiles , which is defined as the number of GO terms shared by both proteins divided by the number of GO terms associated with at least one protein . Similarly , we calculated molecular function similarity , biological process similarity and cellular component similarity between two proteins by calculating the Jaccard similarity index of their GO association profiles using the corresponding GO entries . We retrieved gene-disease associations from the DisGeNET database [39 , 40] ( retrieved July 2016 ) , which integrated data from UniProt [37] , ClinVar [58] , Orphanet ( http://www . orpha . net ) , CTD [59] , and the GWAS Catalog [60] . Diseases were mapped onto reference proteins by mapping disease-associated gene names to Swiss-Prot IDs using the mapping table provided by DisGeNET . To calculate the fraction of disease subnetworks shared by two proteins , we included in each protein’s disease association profile all diseases associated with that protein and its first-degree neighbors in the HI-II-14 reference binary interactome . We then used the Jaccard similarity index to calculate the fraction of disease subnetworks shared by the two proteins , where two proteins share a specific disease subnetwork if each of the two proteins or any of its interaction partners in the HI-II-14 reference binary interactome is annotated with that disease . We used the RNA-Seq dataset of Illumina Body Map 2 . 0 [43] ( retrieved Jan 2016 ) , normalized using log2 transformation , to quantify gene expression levels in 16 human body tissues: adipose , adrenal , brain , breast , colon , heart , kidney , leukocyte , liver , lung , lymph node , ovary , prostate , skeletal muscle , testis and thyroid . Gene expression profiles were mapped onto reference proteins in the IntAct reference interactome by mapping the protein Swiss-Prot IDs to gene names using the Retrieve/ID mapping tool provided by UniProt [37] . Gene expression profiles were mapped onto reference proteins in the HI-II-14 reference interactome using gene names provided by the original HI-II-14 dataset . Tissue co-expression of each pair of reference proteins was calculated as Pearson’s correlation coefficient of their gene expression profiles . The quality of our computational method was assessed by the experimental isoform interactome dataset of Yang et al . ( 2016 ) [34] , which consists of 985 interactions and 763 non-interactions between reference proteins taken from the human ORFeome V8 . 1 database [61] and newly-cloned isoform sequences . 310 of these interactions involve an ORFeome protein and a newly-cloned reference isoform sequence ( reference-reference ) , and the rest of the interactions involve an ORFeome protein and a newly-cloned alternative isoform sequence ( reference-alternative ) . We annotated all isoforms in this experimental dataset with structural domains by scanning their sequences for Pfam domains using HMMER hmmscan [57] with an E-value cutoff of 10−3 . The 11 , 557 PDB-inferred DDIs from 3did [35] and DOMINE [36] were then used to annotate the reference-reference interactions . A protein-protein interaction was given a full DDI annotation if one protein was annotated with an interacting domain of a DDI , and its interaction partner was annotated with the other interacting domain of the same DDI . A protein-protein interaction was given a partial DDI annotation if one protein with multiple isoforms was annotated with an interacting domain of a DDI , even if the interaction partner was not annotated with the other interacting domain of the same DDI . We have created a web tool called “DIIP: Domain-based Isoform Interactome Prediction” that allows users to query our predicted isoform interactome for isoform-specific interactions of a protein of interest . In addition , our web tool gives users a second advanced option to predict isoform-specific interactions using our isoform interactome prediction method ( DIIP ) from interactions provided by the user . Interactions provided by the user do not need to be part of our predicted isoform interactome . The web tool can be accessed at the following URL: http://bioinfo . lab . mcgill . ca/resources/diip . The code used for predictions and analysis is available at the following URL: http://github . com/MohamedGhadie/isoform_interactome_prediction .
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Protein-protein interaction networks have been extensively used in systems biology to study the role of proteins in cell function and disease . However , current network biology studies typically assume that one gene encodes one protein isoform , ignoring the effect of alternative splicing . Alternative splicing allows a gene to produce multiple protein isoforms , by alternatively selecting distinct regions in the gene to be translated to protein products . Here , we present a computational method to predict and analyze the large-scale effect of alternative splicing on protein-protein interaction networks . Starting with a reference protein-protein interaction network determined by experiments , our method annotates protein-protein interactions with domain-domain interactions , and predicts that a protein isoform loses an interaction if it loses the domain mediating the interaction as a result of alternative splicing . Our predictions reveal the central role of alternative splicing in extensively remodeling the human protein-protein interaction network , and in increasing the functional complexity of the human cell . Our prediction method complements ongoing experimental efforts by predicting isoform-specific interactions for genes not tested yet by experiments and providing insights into the functional impact of alternative splicing .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"cell",
"death",
"protein",
"interactions",
"protein",
"interaction",
"networks",
"cell",
"processes",
"alternative",
"splicing",
"mathematics",
"forecasting",
"statistics",
"(mathematics)",
"network",
"analysis",
"genome",
"analysis",
"research",
"and",
"analysis",
"methods",
"computer",
"and",
"information",
"sciences",
"protein-protein",
"interactions",
"proteins",
"mathematical",
"and",
"statistical",
"techniques",
"gene",
"expression",
"statistical",
"methods",
"proteomics",
"gene",
"ontologies",
"biochemistry",
"rna",
"rna",
"processing",
"cell",
"biology",
"nucleic",
"acids",
"apoptosis",
"protein",
"domains",
"genetics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"genomics",
"computational",
"biology"
] |
2017
|
Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing
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The three major soil-transmitted helminths ( STH ) Ascaris lumbricoides , Trichuris trichiura and Necator americanus/Ancylostoma duodenale are among the most widespread parasites worldwide . Despite the global expansion of preventive anthelmintic treatment , standard operating procedures to monitor anthelmintic drug efficacy are lacking . The objective of this study , therefore , was to define the efficacy of a single 400 milligram dose of albendazole ( ALB ) against these three STH using a standardized protocol . Seven trials were undertaken among school children in Brazil , Cameroon , Cambodia , Ethiopia , India , Tanzania and Vietnam . Efficacy was assessed by the Cure Rate ( CR ) and the Fecal Egg Count Reduction ( FECR ) using the McMaster egg counting technique to determine fecal egg counts ( FEC ) . Overall , the highest CRs were observed for A . lumbricoides ( 98 . 2% ) followed by hookworms ( 87 . 8% ) and T . trichiura ( 46 . 6% ) . There was considerable variation in the CR for the three parasites across trials ( country ) , by age or the pre-intervention FEC ( pre-treatment ) . The latter is probably the most important as it had a considerable effect on the CR of all three STH . Therapeutic efficacies , as reflected by the FECRs , were very high for A . lumbricoides ( 99 . 5% ) and hookworms ( 94 . 8% ) but significantly lower for T . trichiura ( 50 . 8% ) , and were affected to different extents among the 3 species by the pre-intervention FEC counts and trial ( country ) , but not by sex or age . Our findings suggest that a FECR ( based on arithmetic means ) of >95% for A . lumbricoides and >90% for hookworms should be the expected minimum in all future surveys , and that therapeutic efficacy below this level following a single dose of ALB should be viewed with concern in light of potential drug resistance . A standard threshold for efficacy against T . trichiura has yet to be established , as a single-dose of ALB is unlikely to be satisfactory for this parasite . ClinicalTrials . gov NCT01087099
The three major Soil-Transmitted Helminths ( STH ) , Ascaris lumbricoides ( roundworm ) , Trichuris trichiura ( whipworm ) and Necator americanus/Ancylostoma duodenale ( the hookworms ) are amongst the most widespread parasites worldwide . An estimated 4 . 5 billion individuals are at risk of STH infection and more than one billion individuals are thought to be infected , of whom 450 million suffer morbidity from their infection , the majority of who are children . An additional 44 million infected pregnant women suffer significant morbidity and mortality due to hookworm-associated anemia . Approximately 135 , 000 deaths occur per year , mainly due to infections with hookworms or A . lumbricoides [1] . The most widely implemented method of controlling STH infections is through periodic administration of anthelmintics . Rather than aiming to achieve eradication , current control programs are focused on reducing infection intensity and transmission potential , primarily to reduce morbidity and avoid mortality associated with the disease [2] . The benzimidazole ( BZ ) drugs , i . e . albendazole ( ALB ) and mebendazole , are the most widely used drugs for the control of STH . While both show broad-spectrum anthelmintic activity , for hookworms a single dose of ALB is more effective than mebendazole [3] . The scale up of chemotherapy programs that is underway in various parts of Africa , Asia and South America , particularly targeting school children , is likely to exert increasing drug pressure on parasite populations , a circumstance that is likely to favor parasite genotypes that can resist anthelmintic drugs . Given the paucity of suitable alternative anthelmintics it is imperative that monitoring programs are introduced , both to assess progress and to detect any changes in therapeutic efficacy that may arise from the selection of worms carrying genes responsible for drug resistance . The well documented occurrence of resistance to anthelmintics in nematode populations of livestock [4] , highlights the potential for frequent treatments used in chemotherapy programs to select drug resistant worms . Such an eventuality threatens the success of treatment programs in humans , both at individual and community levels [5] . Although some small scale studies [6] , [7] , have suggested emerging drug resistance in human STH , these studies should be interpreted with some caution , since suboptimal efficacy could have been due to factors other than drug resistance . Moreover , although for the BZ drugs there are many published studies reporting the Cure Rate ( CR ) and the Fecal Egg Count Reduction ( FECR ) , the two most widely used indicators for assessing the efficacy of an anthelmintic in human medicine , comparison of such studies is difficult , largely because there is no widely accepted standard operating procedure for undertaking such trials [8] . Published studies are confounded by methodological variations including treatment regimens , poor quality of drugs , differing statistical analyses used to calculate therapeutic efficacy , as well as a range of other problems in study design , such as small sample size , diagnostic methods , variation in pre-intervention infection intensities and confounding factors related to geographical locations . Such variation among studies greatly complicates direct comparison [3] . A World Health Organization-World Bank ( WHO-WB ) meeting on “Monitoring of Drug Efficacy in Large Scale Treatment Programs for Human Helminthiasis” , held in Washington DC at the end of 2007 , highlighted the need to closely monitor anthelmintic drug efficacy and to develop standard operating procedures for this purpose . In a systematic meta-analysis of published single-dose studies , Keiser and Utzinger [8] , confirmed that there was a paucity of high quality trials , and that the majority of trials were carried out more than 20 years ago . They recommended that well-designed , adequately powered , and rigorously implemented trials should be undertaken to provide current data regarding the efficacy of anthelmintics against the main species of STH . These should be designed to establish benchmarks ( including standard operating procedures ) for subsequent monitoring of drug resistance . The objective of the present work was to validate a standard protocol that has been developed for monitoring efficacy of anthelmintics against STH . To give the study wide relevance , we conducted the trial in seven populations in different geographic locations in Brazil , Cameroon , Cambodia , Ethiopia , India , Tanzania and Vietnam . In each of the study sites , different epidemiologic patterns of infection prevail , including different combinations of STH . We assessed the efficacy of a single dose ( 400 mg ) of ALB in terms of the CR and the FECR in school children between 14 and 30 days following treatment . The McMaster egg counting technique was used in a standardized fashion , with rigorous quality control . Levecke et al . [9] reported that the McMaster holds promise as a standardized method on account of its applicability for quantitative screening of large numbers of subjects . This method is the recommended method for measuring fecal egg counts ( FEC ) when performing FECR for the detection of anthelmintic resistance in veterinary medicine [10] , [11] .
This study was carried out in seven different countries covering Africa ( Cameroon , Ethiopia and Tanzania ) , Asia ( Cambodia , India and Vietnam ) and South-America ( Brazil ) . However , it is important to note , that while we refer to individual countries to identify results from particular trials , we do not make any conclusions about any country as such . Here , names of countries are used only to distinguish between 7 separate trials that were conducted in 7 geographically disparate regions of the world . In total ten study sites with varying STH and treatment history were included . These seven STH endemic countries were selected because of the presence of investigator groups with previous extensive experience in the diagnosis and control of STH . Table 1 provides their specific locations ( district/province/state ) and treatment history . Both species of hookworms ( N . americanus and A . duodenale ) were present in all study sites in varying degree with the exception of Brazil where only N . americanus was present . During the pre-intervention survey , school children aged 4 to 18 years at the different study sites were asked to provide a stool sample . For the initial sampling the aim was to enroll at least 250 infected children with a minimum of 150 eggs per gram of feces ( EPG ) for at least one of the STH . This sample size was selected based on statistical analysis of study power , using random simulations of correlated over-dispersed FEC data reflecting the variance-covariance structure in a selection of real FEC data sets . This analysis suggested that a sample size of up to 200 individuals ( α = 0 . 05 , power = 80% ) was required to detect a 10 percentage point drop from a null efficacy of ∼ 80% ( mean percentage FEC Δ per individual ) over a wide range of infection scenarios . Standard power analyses for proportions also indicated that the detection of a ∼10 percentage point drop from a null cure rate required sample sizes up to 200 ( the largest samples being required to detect departures from null efficacies of around 50% ) . Given an anticipated non-compliance rate of 25% , a sample of 250 individuals with >150 EPG pre-treatment was therefore considered necessary at each study site . Fecal samples were processed using the McMaster technique ( analytic sensitivity of 50 EPG ) for the detection and the enumeration of infections with A . lumbricoides , T . trichiura and hookworms [9] . None of the samples were preserved . Samples which could not be processed within 24 hours were kept at 4°C . A single dose of 400 mg ALB ( Zentel ) from the same manufacturer ( GlaxoSmithKline Pharmaceuticals Limited , India ) and same lot ( batch number: B . N°: L298 ) was used at all trial sites . No placebo control subjects were included in the trial for ethical and operational reasons . Between 14 to 30 days after the pre-intervention survey , stool samples were collected from the treated subjects and processed by the McMaster technique . All of the trials were carried out in a single calendar year ( 2009 ) . Subjects who were unable to provide a stool sample at follow-up , or who were experiencing a severe concurrent medical condition or had diarrhea at time of the first sampling , were excluded from the study . The participation , the occurrence of STH and sample submission compliance for pre- and post-intervention surveys are summarized in Figure 1 . The McMaster counting technique ( McMaster ) was based on the modified McMaster described by the Ministry of Agriculture , Fisheries and Food ( UK; 1986 ) [12] . Two grams of fresh stool samples were suspended in 30 ml of saturated salt solution ( density = 1 . 2 ) . The suspension was poured three times through a wire mesh to remove large debris . Then 0 . 15 ml aliquots were added to each of the 2 chambers of a McMaster slide . Both chambers were examined under a light microscope using a 100x magnification and the FEC for each helminth species was obtained by multiplying the total number of eggs by 50 . The efficacy of the treatment for each of the three STH was evaluated qualitatively based on the reduction in infected children ( CR ) and quantitatively based on the reduction in fecal egg counts ( FECR ) . The outcome of the FECR was calculated using three formulae . The first two formulae were based on the mean ( arithmetic/geometric ) of the pre- and post-intervention fecal egg count ( FEC ) ignoring the individual variability , whereas the third formula represented the mean of the reduction in the FEC per subject . The latter is the only quantitative indicator of efficacy for which the importance of confounding factors can be assessed by statistical analysis . The CR and the FECR ( 1-3 ) outputs were calculated for the different trials , both sexes , age classes ( A: 4–8 years; B: 9–13 years and C: 14–18 years ) and for the level of egg excretion intensity at the pre-intervention survey . These levels corresponded to the low , moderate and high intensities of infection as described Montresor et al . [13] For A . lumbricoides these were 1–4 , 999 EPG , 5 , 000–49 , 999 EPG and >49 , 999 EPG; for T . trichiura these levels were 1–999 EPG , 1000–9 , 999 EPG and >9 , 999 EPG; and for hookworms these were 1–1 , 999 EPG , 2 , 000–3 , 999 EPG and >3 , 999 EPG , respectively . In addition , the robustness of the three FECR formulae was explored by comparing the FEC reduction rate obtained from all samples containing STH and those obtained from samples containing more than 150 EPG as recommended in the anthelmintic resistance guidelines of the World Association for the Advancement of Veterinary Parasitology [9] . Finally , putative factors affecting the CR and the FECR ( 3 ) were evaluated . For the CR , generalized linear models ( binomial error ) were built with the test result ( infected /uninfected ) as the outcome , ‘trial’ ( 7 levels: trials in Brazil , Cambodia , Cameroon , Ethiopia , India , Tanzania and Vietnam ) and ‘sex’ ( 2 levels: female and male ) as factors , and ‘age’ and the log transformed pre-intervention FEC as covariates . Full factorial models were evaluated by the backward selection procedure using the likelihood ratio test of χ2 . Finally , the CR for each of the observed values of the covariate and factor was calculated based on these models ( The R Foundation for Statistical Computing , version 2 . 10 . 0 [14] ) . For analysis of the data from FECR ( 3 ) , non-parametric methods were used , because models based on parametric statistics , even with negative binomial error structures , or based on transformed data would not converge satisfactorily as a consequence of the high proportion of FEC with zero EPG . Hence , the impact of the factors ‘trial’ and ‘sex’ were assessed by the Kruskal-Wallis test ( for more than 2 group comparisons ) and the Mann-Whitney U test , respectively . The correlation between the outputs of FECR ( 3 ) and the covariates ( age and pre-intervention FEC ) was estimated by the Spearman rank order correlation coefficient ( SAS 9 . 1 . 3 , SAS Institute Inc . ; Cary , NC , USA ) . The overall protocol of the study was approved by the Ethics committee of the Faculty of Medicine , Ghent University ( Nr B67020084254 ) and was followed by a separate local ethical approval for each study site . For Brazil , approval was obtained from the Institutional Review Board from Centro de Pesquisas René Rachou ( Nr 21/2008 ) , for Cambodia from the National Ethic Commitee for Health Research , for Cameroon from the National Ethics Committee ( Nr 072/CNE/DNM08 ) , for Ethiopia from the Ethical Review Board of Jimma University , for India from the Institutional Review Board of the Christian Medical College ( Nr 6541 ) , for Tanzania ( Nr 20 ) from the Zanzibar Health Research Council and the Ministry of Health and Social Welfare , for Vietnam by the Ministry of Health of Vietnam . An informed consent form was signed by the parents of all subjects included in the study . This clinical trial was registered under the ClinicalTrials . gov Identifier NCT01087099 .
Overall , the highest CRs were observed for A . lumbricoides ( 98 . 2% ) , followed by hookworm ( 87 . 8% ) and T . trichiura ( 46 . 6% ) . However , as shown in Table 2 , the CRs varied across the different trials , age classes and pre-intervention FEC levels . The differences in CRs between trials were most pronounced for T . trichiura , ranging from 21 . 0 ( Tanzania ) to 88 . 9% ( India ) . The T . trichiura CRs of 100% for the trials in Brazil and Cambodia are not considered here as they were based on only 1 and 2 individuals , respectively . For hookworms and A . lumbricoides , the CRs varied from 74 . 7 ( India ) to 100% ( Vietnam ) and from 96 . 4 ( Tanzania ) to 99 . 3% ( Ethiopia and Cameroon ) , respectively . The CRs for A . lumbricoides in Cambodia ( 100% ) and India ( 95 . 2% ) are not considered here as they were based on fewer than 50 individuals . The CRs increased over the three age classes ( A . lumbricoides: 95 . 8 to 100%; T . trichiura: 44 . 7 to 54 . 1% ) , except for hookworms where the CRs ranged from 86 . 1 to 88 . 3 , and then to 87 . 5% . For each of the three STH , there was a decline in the CR with increasing levels of infection intensities at the pre-intervention survey . The largest drop was observed for T . trichiura , which decreased from 53 . 9 to 12 . 5% . For the two other STH , the drop in the CR was less pronounced , ranging from 88 . 6 to 76 . 9% for hookworms and only from 98 . 3 to 95% for A . lumbricoides . The observed differences between sexes were negligible for all three STH . Differences in CR by trial , age and pre-intervention FEC are illustrated in Figure 2 . The variability in the CR of the three parasites was significantly associated with these three factors ( predictive value >75% ) . The pre-intervention FEC was probably the most important as it had a considerable effect on the CR of A . lumbricoides ( χ21 = 4 . 14 , p<0 . 05 ) , T . trichiura ( χ21 = 66 . 3 , p<0 . 0001 ) and hookworms ( χ21 = 11 . 9 , p<0 . 001 ) . Age only contributed to variation in the CR of A . lumbricoides ( χ21 = 6 . 8 , p<0 . 01 ) . Differences among the trials ( countries ) in the CR were observed for T . trichiura ( χ23 = 33 . 8 , p<0 . 0001 ) and hookworms ( χ26 = 35 . 1 , p<0 . 0001 ) , but not for A . lumbricoides . In addition , there was an interaction between the pre-intervention FEC for A . lumbricoides ( χ21 = 4 . 7 , p<0 . 05 ) and for T . trichiura ( χ23 = 18 . 4 , p<0 . 0005 ) with age and trial ( country ) respectively ( lines cross one another ) . The impact of pre-intervention FEC on the CR of A . lumbricoides was more pronounced for older individuals than younger ones . For T . trichura the effect of pre-intervention FEC varied considerably across the trials conducted in the different countries , particularly for the trial in Ethiopia where the CR dropped from almost 100 to nearly 0% as the pre-intervention FEC increased . The pre-intervention FEC for the different STH ranged from 50 to 170 , 500 EPG for A . lumbricoides ( arithmetic mean = 6877 EPG ) , from 50 to 23 , 200 EPG for T . trichiura ( arithmetic mean = 824 EPG ) and from 50 to 13 , 800 EPG for hookworm ( arithmetic mean = 650 EPG ) . The data in Table 3 show that there was considerable variation in the arithmetic means of the FEC from the trial groups in the 7 participating countries for each of the three STH species . As illustrated in Figure 3 , pre-intervention FEC were highly aggregated among the subjects , and high FEC were only observed in relatively few subjects . The FEC reduction rate calculated using all three formulae ( based on FECR 1-3 ) in turn for A . lumbricoides , T . trichiura and hookworms across the 7 trials ( countries ) , age classes , sexes and pre-intervention infection intensities are summarized in Table 4 . Overall , the FEC reduction rate for FECR ( 1 ) was the highest for A . lumbricoides ( 99 . 5% ) , followed by hookworm ( 94 . 8% ) and T . trichiura ( 50 . 8% ) . However , there was considerable variation in the FEC reduction rate among the 7 trials , age classes and infection intensities at pre-intervention survey . For A . lumbricoides , the FEC reduction rate remained roughly unchanged over these variables , only ranging from 97 . 8 to 100% . This contrasts with T . trichiura , for which the FEC reduction rate differed between the trials ( from 39 . 2 [Cameroon] to 92 . 4% [Ethiopia] ) , age classes ( from 45 . 4 [B] to 62 . 7% [A] ) and pre-intervention infection intensity ( from 40 . 0 [high] to 58 . 7% [moderate] ) . There was no difference in the FEC reduction rate between the sexes . For hookworms , only small differences in the FEC reduction rate were observed between the trials , ranging from 87 . 1 [India] to 100% [Vietnam] . However , there were only negligible differences between the age classes ( from 94 . 7 [B] to 96 . 4% [C] ) . Compared to the results of FECR ( 1 ) , the outputs of FECR ( 2 ) resulted in higher values for all three STH , except for A . lumbricoides where the FEC reduction rate already showed a ceiling effect ( 100% ) . Considerable variation in the FEC reduction rate ( FECR ( 2 ) ) occurred with T . trichiura among the trials ( from 82 . 6 [Tanzania] to 99 . 1% [Ethiopia] ) and pre-intervention infection intensity ( from 88 . 6 [high] to 94 . 3% [low] ) . For hookworms , the differences between the trials were virtually negligible , all indicating a potent effect just short of the maximum 100% ( FECR ( 2 ) >99 . 3% ) . The results of FECR ( 3 ) mostly yielded comparable or lower values than those from FECR ( 1 ) . The low values ( sometimes negative ) can be explained by subjects for whom the post-intervention FEC exceeded the pre-intervention FEC . These subjects contributed to a negative FEC reduction rate which had a significant impact on the final FEC reduction rate calculated with FECR ( 3 ) . This became apparent in the FEC reduction rate for A . lumbricoides , where a Cameroonian male subject of 7 years with a pre-intervention FEC of 100 and a post-intervention FEC of 22 , 050 EPG , contributed markedly to lowering the overall values for the data-set from the trial in Cameroon ( FECR ( 1 ) : 99 . 2%; FECR ( 3 ) : 26 . 0% ) . This lowering of FECR ( 3 ) compared to FECR ( 1 ) for A . lumbricoides also occurred with age class A ( FECR ( 1 ) : 98 . 9%; FECR ( 3 ) : −2 . 7% ) and the low pre-intervention infection intensity level ( FECR ( 1 ) : 97 . 8%; FECR ( 3 ) : 66 . 6% ) , but not for the remaining variables . The number of negative individual FEC reduction rates , and the magnitude of the difference between pre- and post-intervention FEC , both contributed to the discrepancies found for T . trichiura ( 176 subjects ) and hookworms ( 10 subjects ) . Table 5 summarizes the FEC reduction rates restricted to samples of more than 150 EPG indicating that the results of FECR ( 1 ) and FECR ( 2 ) remained roughly unchanged . The values from FECR ( 3 ) increased and were mostly comparable with those obtained by FECR ( 1 ) . This change in the results of FECR ( 3 ) is due to the exclusion of negative individual FEC reduction rates which mostly occurred among the subjects with low pre-intervention FEC ( see also Table 4 ) . Differences of more than 5% between the results of FECR ( 3 ) and FECR ( 1 ) were limited to T . trichiura ( country: Cameroon , India , Tanzania and Vietnam; age class: A and C ) . The assessment of putative factors affecting the results from FECR ( 3 ) was restricted to samples containing more than 150 EPG . Due to the limited variation in the FEC reduction rates ( FECR ( 3 ) ) of A . lumbricoides across the different variables , this species was not analyzed further . Also , because of the limited number of infected subjects ( <50 ) , the trials in Brazil , Cambodia and India were excluded from analyses of T . trichiura . For hookworms , and for the same reasons , subjects from the trials in Brazil and Vietnam were not included . Significant differences in the FEC reduction rates between the trials were found for both T . trichiura ( χ23 = 117 . 3 , p<0 . 0001 ) and hookworms ( χ24 = 20 . 2 , p = 0 . 0005 ) . High pre-intervention FEC of T . trichiura yielded lower FEC reduction rates ( 3 ) ( Rs = −0 . 18 , n = 701 , p<0 . 0001 ) , but this was not found for hookworm ( Rs = −0 . 04 , n = 601 , p = 0 . 34 ) . In addition , there was an interaction between the pre-intervention FEC of T . trichiura and trial ( country ) , reflected in the negative correlations in the trials in Cameroon ( Rs = −0 . 28 , n = 233 , p<0 . 0001 ) , and Ethiopia , ( Rs = −0 . 34 , n = 72 , p = 0 . 0034 ) , but a positive correlation for the trial in Tanzania ( Rs = +0 . 28 , n = 325 , p<0 . 0001 ) and a non-significant correlation for the trial in Vietnam ( Rs = −0 . 07 , n = 71 , p = 0 . 58 ) . Host sex and age did not contribute significantly to variation of the results of ( FECR ( 3 ) ) in any of the STH examined .
To our knowledge , the present study is the first to evaluate drug efficacy for STH in school children across different endemic regions using a protocol which was standardized in terms of the treatment ( a single-oral 400 mg dose of ALB originating from the same batch ) , the follow up ( between 14 and 30 days after ) and the detection technique ( the McMaster counting technique ) . Moreover , efficacy was evaluated by both the CR and the FECR , and compared statistically between the seven trials which took place in geographically disparate parts of the world . Overall , this study supports previous reports that indicated that single dose ALB treatment is most effective for infection with A . lumbricoides , followed by hookworm , but is relatively ineffective for T . trichiura , confirming the efficacy studies reviewed by Bennet and Guyatt [3] , and by Keiser and Utzinger [8] . The low efficacy observed for T . trichiura compared to the two other STH , is in keeping with previous studies , where a 3-day dose schedule of ALB has been shown to be necessary to achieve acceptable therapeutic efficacy [3] . At present , the most commonly reported indicator of drug efficacy in this field is the CR [3] . Our results support the view that the CR should not be the recommended parameter , as it is sensitive to variation in the intensity of infection before treatment . The CRs declined in all three STH with increasing intensity of infection ( FEC ) at the pre-intervention survey . Hence , comparison between populations ( countries , villages , schools , etc . ) differing in pre-intervention FEC are guaranteed to arrive at different conclusions about drug efficacy . Differences in the outputs of calculations based on processing quantitative data in different ways also showed variation that requires careful review if standard operating procedures for data processing are to be adopted . The observation that therapeutic efficacies based on arithmetic means were mostly lower than those based on geometric means is in agreement with other studies [15] , and arises because the arithmetic means captures the variation more effectively , while the geometric means compress the data such that efficacies are highly overestimated . Our exploratory analysis of different statistical approaches for analyzing data also indicates that FECR based on individuals was highly affected by excluding subjects with pre-intervention FEC below 150 EPG . Therefore , we conclude that the group based formula using an arithmetic mean is the best summary statistic to employ in analysis of therapeutic efficacy in future large scale drug administration trials , since it represents a robust indicator that is sensitive to changes in drug efficacy . The efficacy ( CR and FEC reduction rate ) varied widely across the trials , except for A . lumbricoides . Possible explanations for the observed differences include ( 1 ) treatment history , ( 2 ) geographic differences within STH species , ( 3 ) fecal consistency and ( 4 ) diet . It is therefore pertinent to comment on each . Although the lowest efficacies for T . trichiura ( Cameroon and Tanzania ) and hookworms ( India ) were obtained in countries with a treatment history , the observed low efficacies are not likely to be attributable to large scale anthelmintic treatment in Cameroon and India . In these countries , a comparison between different study sites with a history of large scale anthelmintic treatment ( Cameroon: Loum; India: Vellore ) and without such a history ( Cameroon: Yoyo; India: Thiruvanamalai ) indicated that these large scale programs did not result in a reduced efficacy compared to sites were they were absent ( data not shown and to be published separately ) . For Tanzania , the impact of large scale anthelmintic treatment programs could be ruled out , as studies before and during these interventions have shown similar drug efficacy figures for T . trichuria [16] , [17] . Current molecular studies indicate that geographical differences exist within STH species [18] , [19] . For T . trichiura varying anthelmintic efficacy has been suggested to be attributable to the presence/absence of the β-tubulin codon 200 polymorphism that has been linked to BZ resistance [20] . Strain differences have been demonstrated in some species with different drug tolerance as assessed both by efficacy and molecular studies [20] , [21] . Nevertheless , the exact impact of genetic differences within the 3 STH in this study on the efficacy of specific anthelmintics remains speculative . Of note , even at a higher taxonomic level , information on the relative therapeutic efficacy of a single dose ALB on N . americanus and A . duodenale is scarce , this despite the distinct and well known biological differences between these hookworms [22]-[24] . FEC was calculated in the current study without compensation for fecal consistency . It is well recognized that well-formed stools can concentrate helminth eggs , compared to looser or diarrheic feces where they are diluted [25] , thus confounding assessment of drug efficacy . Finally , the diet of subjects varied considerably across the seven participating countries . Differences in the quality of food consumed would have created differences in fat and roughage content and/or increased the rate of passage of substances through the gastrointestinal tract . This may have reduced the period over which ALB could have acted on the parasites , thereby reducing efficacy [26]-[28] . Kopp et al . [29] demonstrated that a reduction in adult canine hookworm ( A . caninum ) counts following chemotherapy did not always yield a reduction in FEC , due to an increase in fecundity among the small residual worm population that survived the anthelmintic treatment ( i/e . , density dependent fecundity ) , consequently confounding the FECR . As described by Kotze and Kopp [30] , density dependent effects could be manifested in a FECR as a reduced drug efficacy for subjects with higher pre-intervention FEC . However , this did not occur in the present study for A . lumbricoides and hookworm . For T . trichiura , the efficacy did decrease with increasing pre-intervention FEC , but this should be interpreted with some caution . This effect was not consistent across the different trials ( e . g . , no correlation in Vietnam but a positive correlation in Tanzania ) , suggesting that other factors as discussed above may have confounded this result . It is also possible that increases in FEC may have arisen because of the inability of ALB to cure infections during the pre-patent period ( with an onset of patency after the pre-intervention egg count time point ) . This is a complication that cannot be avoided in studies taking place in endemic areas where transmission occurs daily because of soil and food contaminated with infective stages of the parasites , and is not interrupted in the population during the period of study . Finally , a negative correlation between the FEC and efficacy is expected , as the probability of having a FEC of zero after treatment in the follow-up survey , consequently a FECR of 100% , will be higher for low FEC than for high FEC before the administration of the drug . Our findings emphasize a need to adhere to strict standard operating procedures and methodologies , and to change the WHO recommended threshold levels for the efficacy of ALB [31] , where a FEC reduction rate below 70% in the case of A . lumbricoides or below 50% for the hookworms are the currently accepted thresholds . We recommend that in future monitoring of single-dose ALB-dependent control programs a minimum FEC reduction rate ( based on arithmetic means ) of >95% for A . lumbricoides and >90% for hookworms are appropriate thresholds , and that efficacy levels below this should raise concern . The great variability of the FECR for T . trichiura and the relatively low efficacy of ALB , confirmed in this present study , indicate that it is not possible to propose an efficacy threshold for this parasite based on our data . In conclusion , the present study is the first to evaluate drug efficacy of a single-oral dose of ALB on such a scale and across three continents . The results confirm the therapeutic efficacy of this treatment against A . lumbricoides and hookworms , and the low efficacy against T . trichiura . Efficacy varied widely across the seven different trials , particularly in the case of T . trichiura and it remains unclear which factors were principally responsible for this variation , although pre-intervention FEC and age played clear roles in this respect . The FEC reduction rate based on arithmetic means is the best available indicator of drug efficacy , and should be adopted in future monitoring and evaluation studies of large scale anthelmintic treatment programs . Finally , our findings emphasize the need to revise the WHO recommended efficacy threshold for single dose ALB treatments .
|
Soil-transmitted helminths ( roundworms , whipworms and hookworms ) infect millions of children in ( sub ) tropical countries , resulting in malnutrition , growth stunting , intellectual retardation and cognitive deficits . Currently , there is a need to closely monitor anthelmintic drug efficacy and to develop standard operating procedures , as highlighted in a World Health Organization–World Bank meeting on “Monitoring of Drug Efficacy in Large Scale Treatment Programs for Human Helminthiasis” in Washington DC at the end of 2007 . Therefore , we have evaluated the efficacy of a commonly used treatment against these parasitic infections in school children in Africa , Asia and South-America using a standardized protocol . In addition , different statistical approaches to analyzing the data were evaluated in order to develop standardized procedures for data analysis . The results demonstrate that the applied treatment was highly efficacious against round- and hookworms , but not against whipworms . However , there was large variation in efficacy across the different trials which warrants further attention . This study also provides new insights into the statistical analysis of efficacy data , which should be considered in future monitoring and evaluation studies of large scale anthelmintic treatment programs . Finally , our findings emphasize the need to update the World Health Organization recommended efficacy threshold for the treatment of STH .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"public",
"health",
"and",
"epidemiology/global",
"health",
"infectious",
"diseases/epidemiology",
"and",
"control",
"of",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"microbiology/parasitology"
] |
2011
|
Assessment of the Anthelmintic Efficacy of Albendazole in School
Children in Seven Countries Where Soil-Transmitted Helminths Are
Endemic
|
In India , dengue disease is emerging as the most important vector borne public health problem due to rapid and unplanned urbanization , high human density and week management of the disease . Clinical cases are grossly underreported and not much information is available on prevalence and incidence of the disease . A cross sectional , stratified , facility based , multistage cluster sampling was conducted between May 4 and June 27 , 2017 in Pune city . A total of 1 , 434 participants were enrolled . The serum samples were tested for detection of historical dengue IgG antibodies by ELISA using the commercial Panbio Dengue IgG Indirect ELISA kit . Anti-dengue IgG-capture Panbio ELISA was used for detection of high titered antibodies to detect recent secondary infection . We used this data to estimate key transmission parameters like force of infection and basic reproductive number . A subset of 120 indirect ELISA positive samples was also tested for Plaque Reduction Neutralizing Antibodies for determining serotype-specific prevalence . Overall , 81% participants were infected with dengue virus ( DENV ) at least once if not more . The positivity was significantly different in different age groups . All the adults above 70 years were positive for DENV antibodies . Over 69% participants were positive for neutralizing antibodies against all 4 serotypes suggesting intense transmission of all DENV serotypes in Pune . Age-specific seroprevalence was consistent with long-term , endemic circulation of DENV . There was an increasing trend with age , from 21 . 6% among <36 months to 59 . 4% in age group 10–12 years . We estimate that 8 . 68% of the susceptible population gets infected by DENV each year resulting into more than 3 , 00 , 000 infections and about 47 , 000 to 59 , 000 cases per year . This transmission intensity is similar to that reported from other known hyper-endemic settings in Southeast Asia and the Americas but significantly lower than report from Chennai . Our study suggests that Pune city has high disease burden , all 4 serotypes are circulating , significant spatial heterogeneity in seroprevalence and suboptimal immunity in younger age groups . This would allow informed decisions to be made on management of dengue and introduction of upcoming dengue vaccines in the city .
Dengue disease is an important emerging public health problem in countries of tropical and subtropical regions . [1–3] Estimated annual global burden of disease is approximately 390 million infections , 96 million clinical cases , and 20 thousand deaths , with almost 34% of total dengue cases occurring in India . [4] According to recent estimates , 2·9 million dengue episodes and 5906 deaths , with an economic burden of $950 million occur annually in Southeast Asia ( SEA ) alone . [5] It is known that disease intensity and disease burden is highly variable between different places within a country or region . [6] In India , dengue is a reportable disease and all confirmed cases are expected to be reported to government of India through NVDCP , Delhi . [7] Recent studies using various models have suggested gross underreporting of dengue cases . It is estimated that each case reported may be multiplied by 200 to get fair estimate . [8 , 9] There are 4 antigenically distinct DENV serotypes ( DENV 1–4 ) . Dengue can result from infection with any one of four viral serotypes . Infection with one serotype provides long-term protection to that serotype , but not to others . Thus , DENV seropositive individuals could be monotypic due to primary infection or multitypic due to secondary infections . Presence of certain serotypes , including primary infection with DENV-3 from the SEA region and secondary infection with DENV-2 , DENV-3 , and DENV-4 also from the SEA region , as well as DENV-2 and DENV-3 from non-SEA regions , increased the risk of severe dengue infections . [10] Thus , age specific distribution for different serotypes and their contributions in monotypic and multitypic cases are worthy of special consideration . Dengue infection results into subclinical disease in majority of the cases and clinical disease in about 25% cases . Proportions of asymptomatic , mild cases and severe cases are highly variable in different areas . Differential diagnosis between clinically similar diseases caused by DENV , Chikungunya virus and other febrile illnesses is almost impossible in resource limited countries like India . Therefore clinical surveillance data which already suffers with tremendous reporting bias is inadequate to estimate true burden of disease . In such situations , properly designed seroprevalence studies may adequately quantify and characterize the extent of transmission . Currently there is no effective drug for treatment of dengue . Sustained effective vector control has become impractical in developing countries . Therefore vaccination has become focus of attention in management of dengue . Several vaccines are in different phases of developments and clinical trials . The first live attenuated ( recombinant ) tetravalent dengue vaccine , Dengvaxia , produced by Sanofi Pasteur , has been licensed for use in some countries in Asia and Latin America . World Health Organization ( WHO ) Strategic Advisory Group of Experts ( SAGE ) recommends that countries consider introduction of this dengue vaccine only in populations where epidemiological data indicate a high burden of disease . In order to maximize public health impact and cost effectiveness , the populations to be targeted for vaccination , as measured by seroprevalence , should be approximately 70% or greater in the age group targeted for vaccination . [11] Seroprevalence typically increases with age , and countries may choose to target vaccination to the youngest age ( 9 years or older ) for which seroprevalence exceeds the recommended 70% threshold . [12] Since such data is not available for most of the endemic places in India , well designed serosurveys are recommended to support decision making for vaccine introduction for public health as well as for conducting clinical trials with dengue vaccines . In view of these concerns , a stratified serosurvey was conducted in Pune city , Maharashtra , India . Pune is fast growing city , chosen under Smart Cities Mission scheme of the Prime Minister of India for speedy and orderly infrastructure development . The city has been experiencing seasonal , annual dengue outbreaks . It is pertinent to generate data on epidemiological determinants including disease burden estimates for proper planning of dengue management .
Pune , the second largest city in the state of Maharashtra after Mumbai and the seventh most populous city in the country is situated 560 meters above sea level on the Deccan plateau . Pune is the administrative headquarters of Pune district and is one of the fastest growing cities in the Asia-Pacific region . It lies between 18° 32" North latitude and 73° 51" East longitude . Pune is 149 kilometers , southeast of Mumbai by road . Average temperatures ranges between 19 to 33°C . Pune experiences three seasons: summer , monsoon , and winter . Typical summer months are from mid-March to June with maximum temperatures sometimes reaching 42°C . The monsoon lasts from June to October , with moderate rainfall and temperatures ranging from 22 to 28°C . Most of the 722 mm of annual rainfall in the city falls between June and September , and July is the wettest month of the year . In winter , the daytime temperature hovers around 26°C while night temperature is around 10–14°C , sometimes dropping to 5 to 6°C . The population of the Pune city is 3 , 124 , 458 and Pune Urban Agglomeration is 5 , 057 , 709 as of the 2011 census . [13] Annual exponential growth rate of population was 2 . 08 per year ( for 2001–2011 ) , with birth rate of 19 . 3 live births per thousand of population per year . [13 , 14] In 2017 , the estimated population of Pune is 3 . 99 million . [15] Pune city is divided into 5 administrative zones , having 15 administrative units called wards . Each ward has one or 2 clinics managed by Pune Municipal Corporation , many private clinics managed by General Practitioners , and some tertiary care hospitals . A cross sectional , stratified , facility based , multistage cluster sampling was conducted in Pune city between May 4 and June 27 , 2017 , following the principles of WHO guidelines . [12] The dengue season in this area is typically from July to December . The present survey was planned to capture activity of dengue from the previous 2016 dengue season . Medical clinics are the first contact point between febrile cases and health seeking facilities . In all 15 wards , a corporation clinic was chosen as first point for sampling . Additional 3 clinics of general practitioners were chosen in such a manner to provide fair representation to the ward . This ratio was based on assumption that about 25% of the primary healthcare in the city is provided by the corporation clinics and the rest by the private practitioners . Fig 1 shows approximate locations of the collection sites ( health facility ) . The data on dengue prevalence in Pune city were not available . However , dengue prevalence of 59% was reported from an urbanized village near Pune city . [16] Assuming that prevalence in Pune city will be higher than the adjoining urbanized village , for the purpose of sample size calculations we assumed 65% prevalence in Pune city . The minimum sample size of 1 , 396 participants was calculated under the assumption of 65% prevalence for dengue infection , α ± 5% error , Confidence level 95% . Accounting for the multistage sampling , the sample size considered a design effect of 4 . 0 . Sample allocation to each ward and age groups was in proportion to the population of the ward and age group with respect to the Pune population . Allowing 5% additional samples to meet contingencies like insufficient sample , leakage and spoilage we targeted 1 , 465 samples . A team visited each health facility . Each non-febrile patient and/or the person accompanying them visiting the facility and resident of the same ward were invited to participate in the study . The willing persons were enrolled until the target sample collection was achieved for that site . Each enrolled person was requested to provide a blood sample following administration of ethical consent/assent approved by the Institutional Ethics Committee of the University . We collected blood samples from a total of 1 , 434 participants , 31 less than the original 1 , 465 sample target . About 5 mL blood was collected from each participants in anti-coagulant free vacutainer tubes ( BD Bioscience ) by trained phlebotomists and kept overnight at 4°C . Serum samples were separated by centrifugation at 3 , 000 rpm for 10 minutes and stored at -80°C . Each serum sample was tested for dengue IgG antibodies by ELISA using the commercial Panbio Dengue IgG Indirect ELISA kit ( Panbio Diagnostics , Brisbane , Australia , Cat no . 01PE30 ) according to manufacturer’s instructions . The presence of detectable IgG antibodies indicates past exposure to dengue infection . Panbio units were calculated by dividing the sample absorbance by the cut-off value and then multiplying this value by 10 . Samples were considered positive if Panbio units were >11 , <9 Panbio units were considered negative and if Panbio units were between 9 to 11 , samples were considered equivocal and retested to confirm the result . An anti-dengue IgG-capture ELISA ( Panbio Diagnostics , Brisbane , Australia , Cat no . 01PE10 ) was performed according to the manufacturer’s instructions . Anti-dengue IgG Panbio units were calculated by dividing the sample absorbance by the cut-off value and then multiplying this value by 10 . Using this criteria , a value of >22 Panbio units was used to identify secondary infection . <18 Panbio units were considered negative for secondary infection and if Panbio units were between 18 to 22 , samples were considered equivocal and retested to confirm the result for secondary infection . [17] High Panbio units are indicative of elevated levels of IgG antibodies which suggest that the patient has been recently exposed to dengue virus due to secondary infection . As WHO recommends use of PRNT90 titers to minimize serum cross-reactivity with other dengue serotypes and flaviviruses prevalent in DENV endemic areas [12 , 18] , we opted for PRNT90 method for this study . Due to resource constraint , we decided to process 120 indirect ELISA positive samples for PRNT . The selection of samples was based on Panbio units of IgG-positives ( Indirect ELISA ) arranged at the interval of 5 units and represented comparable proportions of total positives in each category . We followed WHO guidelines for the PRNT90 test . However , since we were interested in assessing neutralizing antibodies ( NAbs ) against the currently circulating Indian strains , necessary modifications were made . The DENV strains used were DENV-1 ( S19 ) ( Accession no . MG053115 ) , DENV-2 ( S15 ) ( Accession no . MG053142 ) , DENV-3 ( S111 ) ( Accession no . MG053151 ) and DENV-4 ( 1028 ) ( Accession no . MG272272 ) isolated during 2016 in Pune city [19] . The viruses actually used for PRNT were passaged 4–5 times , titrated using plaque assay and stored at -80°C at smaller aliquots . The test included two controls in duplicates; cell control without any virus or serum and virus control for different serotypes , without serum were used in the assay . For the test , early passage Vero cells ( CCL-81 , ATCC ) were seeded at the density of 1 x 105 cells/mL in Minimum Essential Medium ( MEM ) ( GIBCO ) with 10% Fetal Bovine Serum ( FBS , GIBCO ) in 24-well plate ( 1mL/well ) . The following day , serum samples ( diluted 1:5 in MEM with 2% FBS ) were heat inactivated at 56°C for 30 min and then serially diluted 4-fold in the same diluent in 96-well microtiter plates . Serially diluted serum samples were mixed with an equal volume i . e , 1:2 of diluted virus that gives 40–100 plaques/control well with each serotype . The final serum dilutions were 1:10 to 1:2560 . After incubation for 1 hr at 37°C , 5% CO2 incubator , the medium was removed from 24-well plate and 100µl of each dilution of serum/virus mixture was added onto the cells in duplicate . The plates were then incubated for 1 hr for DENV-1 , 2 , 3 and 2 hr for DENV-4 at 37°C , 5% CO2 incubator to allow virus adsorption . After adsorption , 1ml of overlay media containing 1% Aquacide-II ( Calbiochem ) were added onto the cells and incubated for 3 days at 37°C , 5% CO2 incubator . Three days post infection , the overlay medium was discarded from the plates , and the cell monolayer was fixed with formalin for 30minutes at RT and permeabilized with 0 . 2% Triton X-100 in PBS for 5min . The cells were washed three times with PBS-T ( 0 . 02% Tween-20 in PBS ) and stained with HB112 pan-flavivirus mouse monoclonal antibody ( D1-4G2-4-15 , ATCC ) at 1:500 dilution in PBS for 2 hr . Cells were washed three times with PBS-T and incubated with goat anti-mouse IgG horseradish peroxidase ( HRP ) at 1:1500 dilution in PBS for 1 hr . After washing three times with PBS-T and two times with PBS , cells were stained with True Blue peroxidase substrate ( KPL , Sera Care , MA , USA ) and blue color staining of virus infected cells were counted as plaques . PRNT90 titer was calculated using NIH LID Statistical Web tool . [20] PRNT90 titer ≥ 1:10 to one dengue serotype at least was considered seropositive . A monotypic response was defined by the presence of NAbs against only one of the four DENV serotypes . A multitypic response was defined as a concomitant detection of NAbs against more than 1 serotype . Statistical analyses were performed using ‘R’ Version 3 . 4 . 1 . , Microsoft windows Excel 2010 , SPSS v . 17 . 0 ( SPSS Inc . , USA ) and Graphpad Prism v . 7 . 0 ( Graphpad Software USA ) . [21] The logarithm ( Log10 ) values of antibody titers of the serotypes were used for analysis and graphical representation . The statistical comparison of the means of the antibody titers of the serotypes was performed using analysis of variance ( ANOVA ) . The association between the numbers of DENV serotypes ( one , two and three simultaneous serotype infections ) and mean age was performed using ANOVA with POST HOC Least significant difference ( LSD ) test , whereas their association with gender was tested through χ2 ( chi-square ) test for trend . Mann-Whitney U test was performed to check the association of PRNT90 titers against all 4 serotypes across different age groups . Studies were conducted at Interactive Research School for Health Affairs ( IRSHA ) , a constituent unit of Bharati Vidyapeeth ( deemed to be University ) , Pune . The study was approved by the Institutional Ethics Committee ( IEC/2017/04 ) . Written consent/assent to participate in the study , reviewed and approved by the Ethics committee , was administered to each participant or to their legal guardian . All data were handled anonymously and confidentially .
In this study , 1434 participants were recruited from 15 wards of Pune city . Of these , 723 ( 50 . 4% ) were men and 711 ( 49 . 6% ) women , 401 ( 28 . 0% ) were children ≤18 years and 1033 ( 72 . 0% ) were adults >18 years . The age ranged from 1 month to 85 years with a mean of 31 . 2 years and a median of 29 years ( Table 1 ) . Ward-wise sample seropositive for anti-DENV IgG antibody by indirect IgG ELISA is presented in Fig 2 . Overall percent seropositivity was 81% . The median age of seropositives was 33 . The percent seropositivity between wards was significantly different ( p< 0 . 001 ) . The proportion of seroprevalence varied among the wards from moderate high in Aundh ( 61 . 8% ) to very high in Wanawadi ( 94 . 9% ) . The difference in percent seropositivity between males ( 81 . 5% ) and females ( 80 . 7% ) was not significant ( p = 0 . 745 ) . Similarly , there was no significant difference ( p = 0 . 786 ) in percent seropositivity between the participants visiting GP clinics ( 80 . 96% ) and Corporation clinics ( 82 . 12% ) . Only 92 of 1 , 205 seropositive individuals ( 7 . 6% ) could remember having dengue in the past . Distribution of seropositive samples in different age group is presented in Table 1 . There was an increasing trend with age , from 21 . 6% among < 36 months group to 77 . 3% in age group 16–18 years . The positivity was significantly different ( p<0 . 001 ) in different age groups in children ≤ 18 years but not significantly different in adults ( Fig 3A and 3B ) . In adults > 70 yrs ( n = 42 ) all the persons were seropositive . A third order linear polynomial model is best fit to the overall data ( R2 = 0 . 97 ) . Our estimated seroprevalence at 9 years age ( SP9 ) was 54 . 17% ( 95% CI: 49 . 13% - 58 . 97% ) , which is classified as a low-to-moderate DENV transmission intensity . This test is designed to detect high levels of anti-DENV IgG antibodies indicative of a secondary infection . A total of 150 of 1 , 363 samples tested were positive ( 11 . 01%; 95% CI: 9 . 3%-12 . 6% ) . Overall seropositivity was highly variable between wards , ranging from 2 . 91% in Kondhwa to 20 . 95% in Hadapsar ( S1 Table and S1 Fig ) . Only 4 of 229 children in age group ≤ 10 ( 1 . 7% ) were seropositive suggesting a very low rate of secondary infection in young children . The seropositivity in older age groups varied between 11 . 2 and 15 . 9% . Overall distribution of positive proportions was non-linear suggesting age independent phenomenon ( Fig 4 ) . A total of 120 indirect IgG ELISA positive samples were tested for the presence of neutralizing antibodies by PRNT . Of these , 119 samples were confirmed to be seropositive via the presence of neutralizing antibodies and PRNT90 titers of ≥ 10 . One sample had a PRNT90 titer of <10 against all 4 DENV serotypes and was considered seronegative ( Table 2 ) . Over 69 . 2% samples were positive for DENV 1–4 followed by 11 . 7% samples which were positive for 3 serotypes , DENV2 , DENV-3 and DENV-4 . There was significant difference in the percent positivity for different serotypes ( p<0 . 01 ) . Percent PRNT positives for all DENV serotypes in different age groups are shown in Table 3 . Amongst PRNT positives , DENV-2 was the most prevalent serotype across all age groups ( 94 . 4–100% ) . Only 5–6% individuals of age group up to 15 years were susceptible to DENV-2 and all individuals > 44 years of age were seropositive to this virus ( Table 3 ) . DENV-3 and DENV-4 follow age dependent linear distribution suggesting endemic nature of these serotypes for long duration but introduced late in comparison to DENV-2 . DENV-1 was also prevalent across all the age groups in comparatively lower proportion and follows time independent distribution suggesting recent introduction . There is significant difference for percent positivity among different DENV serotype for age group 15 to 44 years ( p = 0 . 006 ) and age group 60 years and above ( p = 0 . 026 ) ( Table 3 ) . The sample size for PRNT was not enough for serotype specific model building for force of infection . Higher titers of neutralizing antibodies ( log10 PRNT90 ) were detected in individuals infected with DENV-2 ( 2 . 524; 95% CI: 2 . 407–2 . 641 ) compared to other serotypes . The titer for DENV-4 was lowest among four serotypes ( 1 . 943; 95% CI: 1 . 844–2 . 041 ) . There was significant difference between overall neutralizing antibody titer of the four serotypes ( p<0 . 05; F = 23 . 568 ) . Post hoc ( LSD ) test showed that this difference is because of neutralizing antibody titer of DENV-2 ( 2 . 524; 95% CI: 2 . 407–2 . 641 ) which was significantly higher than the titers of all other serotypes ( p<0 . 05 ) and there is a significant difference between titer of DENV-3 ( 2 . 109; 95% CI: 1 . 987–2 . 231 ) and DENV-4 ( 1 . 943; 95% CI: 1 . 844–2 . 041 ) ( Fig 5 ) . The titer of DENV-2 was highest across all age groups followed by DENV-3 . In younger age groups , DENV-1 exhibited lower titer and in higher age groups DENV-4 showed lowest titer . However , differences in the titers across all age groups were not significant ( Fig 6 ) . To estimate the transmission intensity of dengue , two catalytic models were fitted to the age-specific seroprevalence for indirect ELISA data , time constant ( model A ) and time varying ( model B ) forces of infection ( Fig 7 ) . As per model A , dengue naive children seroconverted at the rate of 7 . 81% per year ( 95% CI: 7 . 24%-8 . 43% ) . Under model B , annual rate of seroconversion was 8 . 68% ( 95% CI: 7 . 52%-9 . 95% ) in younger population ≤ 18 years and 7 . 51% ( 95%CI: 6 . 87%-8 . 20% ) in individuals > 18 years . The LR test showed non-significant association to favor any model B ( p = 0 . 091 ) ( Table 4 ) . We also fitted model B with 15 and 12 years age break point without any significant difference in FOI . Thus the model B was consistent with a significantly higher force of infection during the period 2000–2016 ( λ = 0 . 868; 95%CI: 0 . 752–0 . 099 ) . Our estimated basic reproductive number ( R0 ) for dengue in Pune is 4 . 23 . These estimates assume endemic circulation of 4 serotypes and were derived using the FOI estimates and census data . The sample size was not enough to calculate FOI for individual wards . We pooled data by zone . Each zone consists of 3 adjoining wards . The mean R0 for Pune was 4 . 23 ( 95% CI: 3 . 58–4 . 87 ) , lowest 3 . 41 in zone 3 and highest 5 . 25 in zone 1 ( S2 Fig ) . For estimation of the burden of disease , we have taken FOI as 8 . 68% for primary infection and 7 . 51% for secondary infections ( Table 4 ) . Accordingly in Pune city with estimated population of ~3 . 99 million in 2016 , we estimate that this leads to approximately 65 , 800 ( 95% CI: 57009–75507 ) primary infections and 242 , 716 ( 95% CI: 222 , 032–265 , 016 ) secondary infections per year . Assuming that 69% immune population is positive for all 4 serotypes ( Table 2 ) , only 31% of secondary cases are likely to give rise to active dengue cases . Therefore , 75 , 242 secondary infections only are considered potentially secondary infections for estimation of dengue cases . The ratio between in-apparent and symptomatic dengue cases is highly variable , ranging from 1:1 to 3:1 . Considering a ratio of 3:1 , Pune city is burdened by about 47 , 000 symptomatic dengue cases each year . We found 11 . 1% seropositivity in Capture IgG ELISA . This test is designed to detect high levels of anti-DENV IgG antibodies indicative of secondary infection . This translates to 358 , 741 secondary infections; 111 , 210 potential secondary cases and 59 , 000 dengue cases each year in Pune .
Dengue was first reported in India from Calcutta in 1912 . [27] Now , it is a well established endemic disease in majority of Indian cities with occasional epidemics . [28 , 29] In Pune city , sporadic cases were reported in 1970s and 80s . Seasonal outbreaks have been recorded from 90s in different localities of the city with hemorrhagic involvement in some cases . [30] In spite of high prevalence of clinical disease , limited information is available on prevalence and incidence of the disease in India . Overall 81% IgG positivity by indirect ELISA with ~ 100% positivity in age groups > 45 years reported by us is much higher than 43% and 59% reported from 2 villages near Pune . In our study , seroprevalence of dengue was 50% in children of 6–10 years age group . In the same age group , high positivity is reported in Mumbai ( 80% ) , Delhi ( 60 . 2–66 . 5% ) , Wardha ( 69% ) , Bangalore ( 62% ) , Hyderabad ( 58% ) and low positivity in Kalyani ( 23% ) . [31] The seropositivity of 79 . 3% in age group from 5–40 years is lower than 93% reported in Chennai . [32] Seropositivity of 11% by Capture ELISA and 81% by Indirect ELISA in our study is similar to the report from Hyderabad . [33] High seropositivity is also reported in Asian countries like Thailand , Bangladesh , Indonesia etc . [34–36] Human population density is reported to be an important variable associated with a high historical incidence of dengue . [37–40] The level of seroprevalence seems to be also associated with the population size of the city . Small places like a village near Pune , population ( 2 , 621 ) and Kalyani in WB ( population 100 , 575 ) reported low seroprevalence . Hyderabad and Pune similar in population pattern have nearly similar dengue prevalence; Chennai , Mumbai and Delhi , the metropolitan cities reported higher seroprevalence . The lowest FOI and R0 for Indian subcontinent reported was based upon the data from Andamans island collected in 1988–89 . [6 , 41] Population of this region was also very small on individual islands . In our study , only 7 . 6% participants could recall having the disease which is suggesting of a high frequency of unapparent infection or mild undifferentiated fever in agreement with other epidemiological studies . [42–45] We estimated FOI , seroconversion rate , 8 . 68% in younger age group ≤ 18 years and 7 . 51% in older age groups . This is very different from 23% in Chennai . The reported seroconversion is highly variable in different places . In Sri Lanka , 8% seroconversion was reported in children ≤ 12 years age , 11 to 17% among children aged 2 to 15 years in Vietnam , 2 . 1 to7 . 9% in Thailand , 10% in Bangladesh , 13 . 1% primary infection per year in children in Indonesia , 17% in children aged 3 years in Salvador , Brazil . [22 , 32 , 35 , 46–52] Our estimated R0 for dengue , 4 . 3 is lower than 5 . 3 estimated in Chennai and is comparable to estimates in hyperendemic settings in Thailand and Brazil . [6 , 53 , 54] As reported in other places , we also found significant heterogeneity between different wards . In India , this is the first study to provide data on PRNT90 , the test recommended by WHO for survey for neutralizing antibodies . Over 69% indirect ELISA positive samples were positive for all 4 serotypes followed by 11 . 7% positive for 3 serotypes , DENV-2 , DENV-3 and DENV-4 . DENV-2 was the most prevalent ( 94 . 4% ) serotype across all age groups . This suggests widespread circulation of all the serotypes in Pune for quite some time . In another study , we reported active circulation of all the serotypes in Pune during 2016 dengue season . [19] Only other report from India , based on PRNT50 titers in children of 5–10 years age groups , reported overall positivity of 97 . 2% for at least one serotype , 79 . 7% for all four serotypes . DENV-1 was dominant serotype in Delhi; DENV-2 in Mumbai , Wardha , Bangalore and Hyderabad; DENV-3 in Kalyani . There is ample evidence that all 4 serotypes have been circulating in majority of the Asian countries . [55 , 56] For analysis of neutralization tests , some investigators used PRNT60 , others PRNT50 [31 , 51] making it difficult to compare the results of different studies . Following WHO recommendations , [12] we used PRNT90 . In case of DENV-2 , the multitypic response was positively associated with age because of the diversity of antibodies generated as a result of ongoing exposure . Highest neutralizing antibody titers observed for the DENV-2 suggests ongoing activity of this virus over the years and a multitypic response caused by the booster effect . [52 , 57 , 58] One of the limitations of the present study is that PRNT was performed in a subset of individuals since it is expensive and laborious . Therefore the study population may not be true representative of the entire city . However , in spite of limitations our results provide valuable data on previous immunity at population level . For estimation of number of dengue cases , whether average seropositivity ( 11 . 1% ) in capture ELISA for estimation of secondary cases in population can be extrapolated or not is an important issue . According to the manufacturer of the Panbio kit and others an IgG result of 22 Panbio units correlates with an HI titer of 1:1280 , the cut-off used to distinguish between primary and secondary dengue infection . [59–61] Therefore , percent positivity in capture ELISA was used for estimation of secondary dengue under assumption that the high titered antibodies wane to below 22 Panbio units within a year and before next dengue season . However , there is a need to generate region specific data on decay pattern of these high titered antibodies in population . Further , in absence of testing for IgM antibody , possibility of primary infection in some cases cannot be ruled out . Currently , the use and deployment of vector control as part of dengue outbreak response strategies is managed by public health in Pune city . It is highly unreliable and unsustainable due to limited resources and difficulties in management of human resources involved in vector control measures . There is no impact assessment in place for such a measure . There is also growing evidence that vector control is not a logical solution for control of dengue in large cities . [62] In the absence of specific drugs and limited usefulness of vector control measures , suitable vaccines are eagerly awaited . [63] Dengvaxia , a live attenuated ( recombinant ) tetravalent vaccine is a licensed vaccine for dengue in several countries for children 9 years of age or older living in DENV endemic areas having high endemicity among 9 year-olds . Children who are seronegative at the time of first vaccination may be primed for future risk of severe dengue illness in areas of low to moderate ( SP9 = 30%-50% ) and even moderate to high ( SP9 = 50%-70% ) endemicity . [11] Therefore it was suggested that average seropositivity of 70% may be minimum requirement for introduction of the vaccine because of variability from locality to locality . With estimated average SP9 = 54% in present study . This vaccine is not suitable for Pune at this stage for the specified age group ≥ 9 . It has been well-documented that passive surveillance involving case notifications does not accurately reflect the burden of dengue in most of locations . Cohort studies in different provinces of Thailand and in Nicaragua had revealed higher numbers of prospectively determined dengue incidences as compared with national reported figures , with a discrepancy of 8 to 21 . 3-folds . [64–66] According to Shepard et . al . ( 2014 ) disease burden of dengue in India is 282 times the reported number per year , substantially more than captured by officially reported cases . [9] In this study , we estimated 47 , 000 to 59 , 000 cases per year in Pune city alone . As per official government report , only 6 , 792 cases of dengue were reported from whole of the Maharashtra in 2016 . Therefore , it is strongly recommended that for a disease like dengue , serosurveys should be conducted periodically . It could shed light on the true dengue infections in the population and can be a good tool to monitor impact of interventions at population level .
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Dengue disease , transmitted through the bite of DENV infected mosquitoes , is an increasing health problem in the Asian subcontinent , including India . Dengue ranges from mild undifferentiated fever to circulatory shock and potentially death . Clinical disease gives an incomplete picture of the magnitude of dengue , because many infections are asymptomatic . Presence of antibodies to DENV provides evidence of past infection . This study provides the first estimate of the prevalence and incidence of dengue , based on the data collected from a well-designed , comprehensive serosurvey . By studying age–wise antibody prevalence , we estimated the force of DENV infection by applying a catalytic model to our serosurvey data . Over 81% individuals were positive for DENV antibodies suggesting intense DENV transmission in Pune city . We estimate that 8 . 68% of the susceptible population gets infected by DENV each year resulting into more than 3 , 00 , 000 infections and about 47 , 000 to 59 , 000 cases per year . The estimated seroprevalence at 9 years age ( SP9 ) , taken as benchmark for introduction of Dengvaxia vaccine by WHO , was 54 . 17% suggesting moderate transmission intensity of dengue , making introduction of the vaccine unsuitable in younger children .
|
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2018
|
Stratified sero-prevalence revealed overall high disease burden of dengue but suboptimal immunity in younger age groups in Pune, India
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Epigenetic mechanisms are emerging as one of the major factors of the dynamics of gene expression in the human malaria parasite , Plasmodium falciparum . To elucidate the role of chromatin remodeling in transcriptional regulation associated with the progression of the P . falciparum intraerythrocytic development cycle ( IDC ) , we mapped the temporal pattern of chromosomal association with histone H3 and H4 modifications using ChIP-on-chip . Here , we have generated a broad integrative epigenomic map of twelve histone modifications during the P . falciparum IDC including H4K5ac , H4K8ac , H4K12ac , H4K16ac , H3K9ac , H3K14ac , H3K56ac , H4K20me1 , H4K20me3 , H3K4me3 , H3K79me3 and H4R3me2 . While some modifications were found to be associated with the vast majority of the genome and their occupancy was constant , others showed more specific and highly dynamic distribution . Importantly , eight modifications displaying tight correlations with transcript levels showed differential affinity to distinct genomic regions with H4K8ac occupying predominantly promoter regions while others occurred at the 5′ ends of coding sequences . The promoter occupancy of H4K8ac remained unchanged when ectopically inserted at a different locus , indicating the presence of specific DNA elements that recruit histone modifying enzymes regardless of their broad chromatin environment . In addition , we showed the presence of multivalent domains on the genome carrying more than one histone mark , highlighting the importance of combinatorial effects on transcription . Overall , our work portrays a substantial association between chromosomal locations of various epigenetic markers , transcriptional activity and global stage-specific transitions in the epigenome .
In spite of worldwide efforts , malaria remains one of the most devastating illnesses with an estimated 216 million episodes leading to 655 , 000 deaths in 2010 [1] . The effectiveness of current treatment strategies is attenuated by increasing resistance of malaria parasites to the available chemotherapeutic drugs . The emergence of artemisinin resistance [2] , [3] has motivated researchers to develop alternate control mechanisms by identifying new drug targets . As such , there is a rapid advancement of genomic and epigenomic research to unveil unique molecular mechanisms associated with the growth and development of malaria parasites . Plasmodium falciparum , the causative agent of the most severe form of malaria , is also the model organism to study the parasite development due to its ability to be grown in vitro . The clinical manifestations of malaria are a result of the parasite development in the red blood cells where it completes its asexual intra-erythrocytic developmental cycle ( IDC ) . Even though transcriptional regulation is important for all developmental stages , the IDC transcriptome revealed a particularly distinct temporal transcriptional regulatory system in P . falciparum [4] . Such a broad and dynamic character of transcriptional regulation where each gene is expressed only at a specific time is unprecedented amongst known living organisms and likely represents a unique evolutionary adaptation of the parasite to its host . The presence of plant-like apicomplexan AP2 ( Api-AP2 ) transcription factors [5] and the general paucity of many other types of specific transcription factors [6] further contributes to the unique character of the parasite regulatory machinery . P . falciparum also displays several diverse features of its epigenome such as the absence of linker histone H1 [7] , the absence of RNA interference machinery [8] , the presence of DNA cytosine methyltransferase but apparent absence of DNA methylation [9] , [10] and the presence of unusual histone variants with a unique set of modifications [11] . Unlike the majority of higher eukaryotes , P . falciparum chromatin is predominantly in a euchromatic state with only a few heterochromatic islands marked by trimethylation of lysine 9 of histone 3 ( H3K9me3 ) [12] , [13] , [14] . Unlike Saccharomyces cerevisiae where K16 acetylation is the dominant modification present at 80% of all H4 molecules [15] , K8 and K12 are the favored acetylation sites in P . falciparum H4 [11] . Nevertheless , consistent with several studies from yeast and mammalian models showing that regulation of gene expression is mediated by chromatin structure [16] , [17] , epigenetic states in P . falciparum have been shown to affect transcription [18] , [19] . In our previous study , we have shown that a potent histone deacetylase inhibitor , apicidin , induces severe alterations in histone modifications as well as gene expression [20] . Recently , it was also shown that epigenetic factors affect clonally variant transcription in P . falciparum likely via switching between hetero- and euchromatic structures at several genetic loci that mainly encode factors involved in host-parasite interactions [21] . Moreover , there is also evidence suggesting links between the mode of action of artemisinin as well as its resistance mechanism with factors affecting histone modifications [3] . Taken together , these lines of evidence highlight the contribution of the chromatin environment in regulating transcriptional control in P . falciparum and stress the need to characterize the overall chromatin landscape as well as its effect on transcriptional regulation during the life cycle . A total of 44 different post-translational covalent modifications on P . falciparum histones including acetylations and methylations have been recently identified [11] . Here , we provide insights into the temporal relationship between twelve of these post-translational modifications and their effect on global transcriptional regulation associated with the complex IDC . The dynamic changes in the transcript pattern during the P . falciparum IDC were reflected in the genome wide epigenomic profiles of eight of the studied histone marks . The transcription linked patterns were associated with enrichment of most histone marks predominantly at the start of coding regions while only one modification , acetylation of H4 at lysine 8 ( H4K8ac ) was found predominantly at the putative promoter regions . Our data also demonstrate co-operative binding of acetylation marks in modulating gene expression across the IDC .
The overview of the occupancy of histone modifications along the P . falciparum genome during the IDC revealed that the most abundant histone marks are H4K5ac , H4K12ac , H3K14ac , H4K8ac , H3K4me3 , H3K56ac and H3K9ac which associate with >80% of the genome ( Figure 1B ) . This is followed by H4R3me2 , H4K20me1 and H4K16ac that associate with 65 to 80% , and finally H4ac4 , H4K20me3 and H3K79me3 that associate with less than 60% of the genome ( represented by the 14 , 773 microarray probes ) . This shows that the individual histone modifications exhibit specific occupancy patterns that reflect their distinct roles in the parasite chromatin structure and function . The visual display of the chromosomal distribution of histone mark occupancy further supports this observation showing distinct patterns of histone marks across the chromosomes but also the existence of some genetic loci marked by more than one modification ( Figure 1C , black boxes ) . Next we determined the enrichment of histone marks represented as log2 ChIP/input ratios with respect to their position within the Plasmodium genes . This was done separately for genes with different levels of expression: top , middle and bottom 10% in the rank of their mRNA levels in each IDC time point ( Figure 1D ) . Overall we could divide the studied histone modifications into two groups , ( i ) those with a biased distribution in the IGRs and/or 5′ termini of the ORFs and ( ii ) those with no preference in their occupancy within the gene structures . The first group comprises four H4 ( K8ac , K16ac , ac4 and K20me1 ) and four H3 ( K9ac , K56ac , K4me3 and K79me3 ) modifications with higher enrichment at IGRs and gradual decrease towards the 3′ end of the genes . The extreme example is H3K4me3 with sharp IGR occupancy , which is consistent with previous suggestion that the primary role of this modification is to demarcate the non-coding regions in between P . falciparum genes [22] . Interestingly , while some modifications such as H3K4me3 and H3K79me3 retained this IGR preference throughout the IDC , others showed stage specific changes in their positional enrichment . These include H4K8ac and H3K56ac that were found at the IGRs predominantly in trophozoites and early schizonts but show essentially no gene position preference in the extremes of the IDC , early rings and late schizonts . For most of the histone marks , there were only small , likely insignificant , differences in their occupancy between genes with high , medium or low levels of expression . The exceptions are , H4K8ac , H4K16ac , H3K9ac and H3K56ac that exhibited somewhat higher IGR enrichment for genes with high mRNA levels ( Figure 1D ) . In addition there was a slight tendency for all H4 acetylations to increase their enrichment towards the 3′ end of genes with low levels of mRNA during 16 to 30 hours post invasion ( hpi ) . In summary the differential occupancy of histone marks within genes suggest their distinct roles as chromatin remodeling factors that may be linked with gene expression during the P . falciparum IDC . The most significant observation made by these studies is the broad and dramatic dynamics of the occupancy of histone modifications across the IDC . Essentially all thirteen histone marks show some degree of variable occupancy at least for a small portion of the genetic loci with which they associate . For the purpose of this study , we define the occupancy variability by two criteria: ( 1 ) The statistical significance of the measured change in occupancy across the experimental time points with respect to experimental replicas ( P<0 . 05 ) , and ( 2 ) in addition to statistical significance ( P<0 . 05 ) , a minimum 1 . 5 fold change in the occupancy profiles across the IDC ( Figure 1B , Table S1 ) . Below , we refer to these as “dynamic histone marks” and “dynamic occupancy profiles” , respectively . Quantitative real time PCR was carried out to validate the dynamic occupancy profiles for three modifications in three genes ( Figure S2A ) . We also wished to evaluate the performance of the microarray probes representing the IGRs in comparison to the ORFs . The signal-intensity/signal-ratio distributions between the sets of ORF and IGR probes show essentially identical profiles with no measurement bias towards any ratio/intensity interval ( Figure S2B ) . This supports the fidelity of the applied microarray technology and ensures that the dynamic range and thus ChIP-on-chip measurements of histone occupancy are directly comparable between IGRs and ORFs . The two most dynamic histone modifications were found to be H4K8ac and H3K4me3 that showed significant changes of ChIP-on-chip signal at more than 50% of the loci with which these histone marks associate . Moreover H3K56ac , H3K9ac , H4ac4 , H4K16ac , H4K12ac , H4K20me1 and H4K20me3 exhibited dynamic occupancy profiles at more than 25% of their loci . In contrast , H4R3me2 , H3K14ac , H3K79me3 and H4K5ac represented the other side of the spectrum with a constitutive pattern of occupancy at the majority of the loci with only 20% or less showing variation across the IDC . The Chi-square test revealed a preference for localization of the dynamic histone marks , with H4K16ac , H4ac4 , H4K8ac , H4K12ac , H3K56ac , H4K20me1 , H4K20me3 and H3K4me3 showing overrepresentation in the ORFs , whereas H4R3me2 showed overrepresentation in the IGRs ( Figure S3 ) . The ORF preference occupancy of the dynamic histone marks is surprising as it is in contrast to their overall ( constitutive and dynamic ) occupancy in IGRs ( Figure 1D ) . This may suggest that while the constant occupancy of these histone marks at the IGRs may function as general demarcation elements , it is the nucleosomes linked with the 5′ termini of the ORF regions that play a dynamic role in the chromatin remodeling and possibly transcriptional regulation during the IDC ( see below ) . Similar to mRNA , the occupancy patterns of histone modifications exhibited single peak profiles with each locus being marked once at a specific time during the IDC ( Figure 2A ) . Investigating the time of peak occupancy , we observed no general trends , but instead each histone mark exhibited a distinct pattern ( Figure 2B ) . In particular , more than 50% of genetic loci are associated with the dynamic occupancy of H4K20me1 , H4K20me3 and H3K14ac which reach maximum levels between 0 and 16 hpi , whereas 40% of loci associated with the dynamic occupancy of H3K4me3 and H4K5ac peaked between 17 and 32 hpi . In contrast , H4K8ac , H3K9ac and H4R3me2 showed maximum occupancy ( more than 40% ) at the late schizont stage . Other histone marks were evenly distributed amongst the three stages . This global variation in the occupancy of histone marks again suggests their distinct role in chromatin remodeling with multiple events during the IDC affecting their overall distribution across the IDC . The dynamic character of the histone modifications and its similarity to the mRNA abundance profiles during the IDC suggests their possible role in transcription . To investigate this , we evaluated the correlations between the occupancy profiles of the dynamic histone marks and steady state mRNA levels of the corresponding genes . Here we hypothesize that a synchrony between the histone marks and mRNA substantiates a link between their deposition at a particular gene and transcriptional activity . Hence we calculated Pearson Correlation Coefficients ( PCC or r ) between mean-centered profiles of the dynamic histone modification occupancy and the corresponding mRNA . For example , 49% ( out of 7260 ) of the H4K8ac dynamic occupancy profiles showed positive correlations ( r≥0 . 4 ) with transcription while 22 . 8 and 22 . 5% showed no or negative correlation , respectively ( Figure 3A ) . The overall skew of the H4K8ac correlation values to the positive side suggests that this histone mark plays a role in transcriptional induction . Using the Kolmogorov-Smirnov test ( KS test ) against randomized data , we were able to evaluate the significance of the occupancy profile correlations with mRNA for all histone marks ( Figure 3B ) . In addition , we utilized the degree of skewness ( S ) to identify all histone marks that are positively correlated with transcription . With P<0 . 0005 and S>0 . 05 , we identified eight histone marks including H4K8ac , H4K16ac , H4ac4 , H3K56ac , H3K9ac , H3K14ac , H3K4me3 and H4K20me1 that showed positive correlations with transcription and thus we refer to these as “transcription-linked” histone marks . For these eight histone marks , the percentage of probes that show a positive correlation with expression ( r≥0 . 4 ) varied from 35 to 48% ( Figure S4A ) . The genes associated with these transcription-linked histone marks show no bias to any particular developmental stage but instead are more or less evenly distributed amongst all stages of the IDC ( Figure S4B ) . Interestingly , the histone marks which followed transcription are also amongst the most dynamic , with at least 25% of the loci changing their occupancy across the IDC ( see Figure 1B ) . There was a statistically significant link between one histone modification ( H4K5ac ) and transcription that is skewed towards a negative correlation . Although H4K5ac is predominantly a constitutive histone mark , this observation opens the possibility that this otherwise euchromatic mark may play a role in transcriptional repression in a small group of genes . Four histone modifications ( H4K20me3 , H4R3me2 , H4K12ac and H3K79me3 ) show essentially no association with transcription during the IDC ( Figure 3B ) . One interesting example is H4K20me3 which was shown to be present at both heterochromatic and euchromatic domains of the P . falciparum genome [12] . In the future , it will be interesting to study their potential roles in chromatin structure and remodeling which may be distinct from transcription . Next , we were interested in the biological significance of the transcription-linked histone modifications . We analyzed the distribution of mRNA correlating histone-marked loci with respect to their position in the gene and subsequently investigated the functional involvement of these genes ( Figure 4 ) . Interestingly only one dynamic , transcription-linked histone mark ( H4K8ac ) showed a strong presence for the IGRs and/or 5′ untranslated regions ( 5′UTR ) . This is in good agreement with its overall distribution in the genome ( see Figure 1D ) . All other modifications associated with transcription , including H3K9ac , H3K4me3 , H3K56ac , H3K4me3 , H4K16ac and H4ac4 , appear to accumulate mainly at the 5′ ends of the ORFs . Transcription-linked occupancy of H4K20me1 and H3K14ac showed no positional preference . We did not observe any positional bias for probes negatively correlated with expression ( r≤−0 . 4 ) . To assess the functional relationship of transcription-linked histone marks , we identified significantly represented functional groups ( P<0 . 05 ) based on Gene Ontology ( GO ) , Kyoto Encyclopedia of Genes and Genomes ( KEGG ) and Malaria Parasite Metabolic Pathway ( MPMP ) ( Figure 4 ) . The transcription-linked histone marks were mainly enriched in genes associated with growth ( ribosomal structure and assembly ) , metabolism ( fatty acid metabolism , nucleotide biosynthesis ) and host-parasite interactions ( Maurer's cleft , invasion ) . A small subset of genes associated with H4K8ac , H4K16ac and H4ac4 which negatively correlated with expression belonged to molecular motors or genes coding for kinetochore and centrosome organization . Interestingly , transcription profiles of genes involved in DNA replication correlated positively with H4K20me1 but negatively with H4K8ac and H4K16ac occupancy profiles . This implies that the DNA replication genes may be deacetylated and methylated at the onset of DNA replication . These observations are consistent with previous studies in other eukaryotic systems that have shown both co-existence [30] as well as competition [31] between H4K20me1 and H4K16ac , and imply the presence of a similar histone code in P . falciparum . In summary , these results clearly demonstrate the association of transient histone modification states with transcriptional activation where at least eight histone marks either individually or in various combinatorial patterns , have the potential to modulate gene expression during the P . falciparum IDC . Presently , very little is known about the mechanisms of chromatin remodeling in Plasmodium parasites . Given the highly dynamic character of histone modifications observed by this as well as previous studies [13] , [20] , [22] , these mechanisms are likely to be highly evolutionarily diverse . Transcription factors bound to promoter and upstream regions are known to recruit chromatin modifiers in other species . We therefore investigated the role of promoter regions in the recruitment of H4K8ac that we found mainly in the upstream regions of active genes . In particular , we wanted to assess the presence of any DNA elements which help to establish histone marks in promoter regions . Four promoters ( 1 . 5–2 Kb upstream of the ATG ) marked by H4K8ac were selected and cloned into luciferase reporter constructs including upstream regions of ring-specific ( MAL13P1 . 122 ) , trophozoite-specific ( PF14_0705 ) , schizont-specific ( PFD0240c ) and sporozoite-specific ( PFC0210c ) genes . Here , we made use of the strain Dd2attB [32] in which transgenes can be integrated at the cg6 locus ( Figure 5A ) . We found that the occupancy profile of H4K8ac was recapitulated on three of the four ectopic promoters ( Figure 5B ) . These profiles override an existing profile of the endogenous cg6 gene ( dashed line ) that is normally characterized by high levels in rings and gradually declines through trophozoites and schizonts . The luciferase activity profiles were also similar to the acetylation patterns of all transfected promoters ( data not shown ) . For one of the promoters ( PF14_0705 ) , there was an incomplete “carry-over” of the H4K8ac occupancy profile that was matched only in the ring stage . This may be due to unknown factors like insufficient promoter length . Overall our data suggest that the promoter regions of P . falciparum genes carry DNA regulatory elements that establish H4K8ac independently of their endogenous chromatin environment . The abundance of dynamic temporal regulation of individual histone marks suggests the existence of combinatorial patterns forming a putative “dynamic histone code” . To investigate this possibility , we carried out pair-wise analysis of occupancy profiles with all thirteen histone marks . To this end , we evaluated the concordance of the occupancy profiles using PCC distributions and subsequently the skewness and KS-test P value as described above ( Table S2 ) . Figure 6A shows examples of highly positive ( S>1 ) , moderately positive ( S between 1 and 0 ) and highly negative PCC distribution ( S<0 ) between the histone marks . Overall our analysis revealed that most of the overlapping marks ( present at the same loci ) exhibited a high level of correlation between their occupancy profiles . Here it is important to note that the mean size of ChIP DNA product generated by our protocol is 500 bp which corresponds to approximately three nucleosomes positioned in the vicinity of the genetic locus represented by a microarray probe . Hence the correlations in the occupancy profiles represent either a combinatorial histone modification at the same nucleosome or co-occurrence of these at directly adjacent nucleosomes . High correlations of the occupancy profiles are particularly evident for acetylations that exhibited positive correlations at essentially all overlapping loci ( Figure 6B ) . This suggests that , each nucleosome predominantly undergoes only one set of modifications during the IDC , presumably for one purpose ( such as transcriptional regulation ) . This situation contrasted with the lysine methylation profiles that showed loose or no correlations with each other or with acetylations . Interestingly , H4R3me2 exhibited a strong negative correlation with most of the other marks . This methylation is thought to be mediated by PfPRMT1 and might be playing a similar role to H3R2me2a ( asymmetrical histone H3 arginine 2 dimethylation ) which has been shown to have a mutually exclusive pattern with H3K4me3 in budding yeast [33] . Acetylation clusters ( Figure 6B ) comprising H4K8ac , H4K16ac , H4ac4 , H4K12ac , H3K9ac and H3K56ac were found to be enriched for specific functional groups which define biologically related genes . Hence , the histones within the nucleosomes associated with the genes within these groups appear to be acetylated at most of the ( studied ) lysine residues during active transcription . Comparing the associations of all thirteen modifications with mRNA , the most represented gene families associated with ribosome structure , protein biosynthesis , tRNA modifications , Maurer's cleft and invasion displayed positive correlations with the acetylation marks ( Figure S5 ) . One of the striking examples involves genes of the early transcribed membrane proteins ( ETRAMPs ) whose mRNA abundance displayed strong correlations to virtually all acetylated histone marks . Interestingly , genes coding for histones themselves showed an anti-correlation between expression and the histone mark occupancy profiles . However , the majority of the gene groups associated with IDC functionalities was mostly in strong positive correlation with the majority of these euchromatic histone marks . An interesting example involves the group of Api-AP2 transcription factors whose mRNA levels are mostly in positive correlation with all euchromatic marks with the exception of H4K16ac which appears to correlate negatively . Hierarchical clustering of members of each group showed that while the majority of genes in the basic cellular and biochemical pathways expressed during the IDC exhibit good correlation between histone marks and transcription , each of the functional groups contains at least a small subset of genes whose mRNA levels are negatively correlated with at least some histone marks . It will be interesting to investigate the implication of these histone marks on transcriptional regulation and thus the functional involvement of these outlying members . In order to validate the co-occupancy of more than one histone mark on a genomic region , we carried out sequential ChIP at ring stage parasites to identify bivalent domains having two histone marks: H3K56ac and H3K9ac ( Figure 6C , Table S3 ) . From individual ChIP results , a total of 2 , 560 probes common to H3K56ac and H3K9ac yielded ChIP-to-input ratios above 1 . Out of 3 , 318 probes recognized by sequential ChIP , 2 , 230 ( 67% , P = 0 ) overlapped with probes common to the individual ChIPs . As an example , the distribution of ChIP signals on chromosome 4 defined common areas of enrichment between chromatin immunoprecipitated with either H3K56ac or H3K9ac independently , or H3K56ac followed by H3K9ac sequentially ( Figure 6C ) .
The dynamic morphology during the life cycle development is believed to be an evolutionarily unique feature of Plasmodium as well as other eukaryotic parasitic organisms and likely reflects their adaptation to a specific host environment . It is clear now that these morphological switches are underlined by broad transcriptional shifts that , in the case of Plasmodium species , affect essentially the entire genome [4] , [34] , [35] . There is mounting evidence that epigenetic mechanisms contribute to the regulation of gene expression across the IDC by maintaining hetero- and euchromatin domains within the Plasmodium genome [12] , [14] , [22] , [36] , [37] . Although most previous studies focused on heterochromatic domains , here we provide a comprehensive epigenetic atlas of thirteen predominantly euchromatic histone marks and provide evidence suggestive of their unique functions during the parasite's asexual blood stage development . Our data suggests that at least 8 modifications are linked with the transcriptional activity of up to 76% of P . falciparum genes . The dynamic nature of histone modifications and their link with transcription during the P . falciparum IDC has been previously suggested . Using ChIP-on-chip , it was shown that H3K9ac and H3K4me3 exhibit highly dynamic patterns of chromosomal distribution between rings and schizonts , and that their occupancy correlates positively with transcription [13] . In the follow-up study by the same group , ChIP-seq results showed that both of these histone modifications associate mainly with promoter regions but only H3K9ac is correlated with transcription while H3K4me3 appears uncoupled from transcription [22] . In agreement with these two reports , our results show a strong accumulation of both H3K9ac and H3K4me3 in the IGRs ( Figure 1D ) . However , for both of these modifications , it is mainly their association with the 5′ end of coding regions that correlates with expression ( ∼22% and ∼28% of H3K9ac and H3K4me3 modifications associated with the 5′ ends of ORFs are positively correlated with transcript levels , respectively ) ( Figure 4 ) . On the other hand , only ∼12% and ∼10% of H3K9ac and H3K4me3 within IGRs correlate with transcript levels , respectively . This further highlights previous findings showing that H3K9ac and its association with IGRs plays a greater role in transcription compared to H3K4me3 . In addition to these two modifications , we demonstrated that at least six other euchromatic marks of H3 ( K14ac and K56ac ) , and H4 ( K8ac , K16ac , K20me1 and ac4 ) play roles in transcriptional regulation during the P . falciparum IDC . With the exception of H4K8ac which shows maximum enrichment at IGRs and/or 5′UTRs , the five others show maximum enrichment at the 5′ ends of ORFs of transcriptionally active genes . This is surprising as in other eukaryotes such as Toxoplasma gondii , Caenorhabditis elegans and Saccharomyces cerevisiae , most acetylations and methylations that are positively correlated with expression typically localize at the promoters and transcriptional start sites [24] , [38] . From this perspective , the chromatin structure of Plasmodium resembles that of plants where most of the euchromatic histone marks accumulate within the start of ORFs as compared to IGRs [26] , [27] . In the future , it will be interesting to study these features of epigenetic regulation , possibly in combination with another plant-like phenomenon in Plasmodium , the Api-AP2 transcription factors [5] . On the other hand , the striking shift in the accumulation of H4K8ac towards upstream regions is indicative of a distinct role in transcription that is more related to that of other eukaryotes such as mammals [39] . In eukaryotic organisms , distinct chromatin states are defined by multiple histone modifications acting sequentially and/or in combination , a phenomenon referred to as the histone code . Although a full understanding of the histone code is pending , distinct patterns of modified histones define groups of biologically related genes [29] , [39] , [40] . Here we show a good concordance of many transcription-linked histone marks suggesting a combinatorial effect in the regulation of gene expression during the P . falciparum IDC . Interestingly , these euchromatic marks ( individually or in combination ) associate with distinct functionalities that could be broadly divided into two main biological categories: ( i ) growth ( e . g . protein biosynthesis , nucleotide metabolism , and DNA replication ) , and ( ii ) host parasite interaction ( e . g . merozoite invasion , Maurer's cleft proteins ) ( Figure S5 ) . This may reflect a regulatory link between the two most crucial functions determining parasite virulence during infection: multiplication rate and interaction with the host immune system . Given the essential role of epigenetic regulation in gene expression , unique factors associated with these processes are presently considered as drug targets for malaria as well as other human parasitic diseases [41] , [42] . One such factor is histone deacetylase ( HDAC ) which plays a pivotal role in chromatin remodeling and thus transcriptional activity . In P . falciparum , the HDAC inhibitor apicidin causes a massive hyperacetylation of H3K9 and H4K8 ( and demethylation of H3K4 ) residues leading to global deregulation of the IDC transcriptional cascade [20] . This deregulation can be induced by at least three other HDAC inhibitors including a 2-aminosuberic acid derivative , Trichostatin A and SAHA , the latter being currently approved for cancer therapy [43] . Moreover , these inhibitors are able to effectively inhibit Plasmodium HDACs [44] and oral administration of apicidin at 2–20 mg/kg for 3 days cures P . berghei infection in mice [45] . This suggests that inhibition of epigenetic mechanisms in Plasmodium represents a promising target area for malaria drug development , and more efforts in developing new compounds with higher selectivity as well as bioavailability are ongoing [46] . However , the development of new antimalaria “epi-drugs” may not be restricted to HDACs , but could also target their opposing histone acetyl transferases or other factors such as chromatin remodeling complexes and signaling pathways impinging on these processes . Our results with the histone modification landscape in the most pathogenic malaria parasite , P . falciparum , will provide a solid reference for all epi-drug development in malaria as well as other parasitic diseases in the future .
Highly synchronized cells of P . falciparum strain T996 were cultured at 5% parasitemia and 2% hematocrit under standard conditions [47] . For ChIP , saponin-lysed parasites were cross-linked with 0 . 5% formaldehyde and harvested at 8 , 16 , 24 , 32 , 40 and 48 hpi . Samples were also collected for RNA and protein isolation from the same time points . Equal amounts of total protein lysate obtained from parasite pellets from the 6 time points were separated by 12% SDS PAGE and transferred onto nitrocellulose membrane . Western hybridizations were carried out using antibodies ( Millipore , Upstate ) directed against the modified histones . Horseradish peroxidase conjugated secondary antibody was purchased from GE Healthcare . We also performed immuno-localization with these antibodies as described [48] . Cross-linked cells were homogenized with 200 strokes of a dounce homogenizer and lysed using 1% SDS . The resulting nuclear extract was sonicated with 8 bursts of 10 sec with 50 sec rest between bursts to shear DNA to a final length of 200 to 1000 bp . The sonicate was then centrifuged for 10 min at 13 , 000× g , and sheared DNA incubated with the immunoprecipitating antibody overnight at 4°C followed by incubation with salmon sperm DNA/Protein A agarose slurry ( Millipore ) for 1 h at 4°C . Protein A agarose was gently pelleted followed by extensive washes . The DNA bound to protein of interest was reverse cross-linked using 0 . 2 M NaCl and incubation overnight at 65°C . Recovered DNA was purified using the QIAEX II kit ( QIAGEN ) . Amplification of immunoprecipitated DNA as well as sonicated genomic DNA ( input ) was carried out as described [49] with a few modifications [50] . Equal amount of Cy5-labeled amplified ChIP DNA was hybridized to Cy3-labeled amplified input DNA . For sequential ChIP , the eluted complex from the first ChIP was subjected to immunoprecipitation using second antibody as described [51] . During the second round of immunoprecipitation , no antibody control was included . RNA was isolated to carry out transcriptional profiling at the appropriate time points . RNA extraction and cDNA synthesis were carried out as described [52] . Cy5-labeled cDNA was hybridized against a Cy3-labeled reference pool which was made by combining equal amounts of RNA from each time point . Equal amounts of Cy5 and Cy3 labeled samples were hybridized to P . falciparum microarrays containing 5 , 402 50-mer intergenic oligonucleotide probes and 10 , 416 70-mer ORF probes representing 5 , 343 coding genes [53] . The intergenic regions were represented by one highly specific probe ( up to 1 . 5 kb upstream of the start codon ) whose microarray hybridization parameters were matched to the intragenic probe set using the OligoRankPick algorithm [53] . Using PlasmoDB version 8 . 2 , we were able to remap 14 , 773 probes to the P . falciparum genome providing an even coverage with at least one probe per 1 . 542 kb . P . falciparum strain T996 was chosen to carry out these experiments due to an exact IDC length of 48 h and the ease with which it can be synchronized . Since the probes on the array have been designed for 3D7 , we excluded vars , rifins and stevors from our analysis . The microarray hybridization was carried out at 63 . 5°C or 65°C in the automated hybridization station ( MAUI , USA ) for ChIP DNA or cDNA , respectively , as described [4] . The microarrays were scanned using the GenePix scanner 4000B and GenePix pro 6 . 0 software ( Axon Laboratory ) . Lowess normalized data was processed to filter out spots with signal intensity less than twice the background intensity for both Cy5 and Cy3 fluorescence . The relative occupancy of histone marks is represented by log2 ChIP/input ratios where high and low ratios represent strong and weak enrichment respectively of modified histones . For expression analysis , each gene profile was represented by an average expression value calculated as an average of all probes representing a particular gene . For ChIP-on-chip , all microarrays were done in triplicates . For each time point , probes with data present in at least 2 out of 3 triplicates were included . Data was presented as an average of triplicates after Kth nearest neighbor ( KNN ) imputation . Data was further filtered to include only those probes where signal from ChIP DNA was obtained in at least 2 consecutive time points . To address dynamics across the life cycle , the average ChIP/input ratio of each time point was used to detect the summit and bottom time point of enrichment for every probe . P value was assigned based on a student's t-test between the replicates . Significantly oscillated occupancy profiles ( relative occupancy defined by log2 ChIP/input ratio ) between the summit and bottom time points were defined as P value<0 . 05 and fold change ≥1 . 5 across the IDC referring to all detectable dynamic probes ( within the limit of the applied ChIP-on-chip technology ) and probes showing the highest level of change in marked histone occupancy , respectively . To assess overall ChIP enrichment at every time point , probes were divided into 3 groups based on expression of the respective genes at each time point: top , middle and bottom 10% of total probes with highest , intermediate and lowest levels of expression , respectively . For each group , ChIP/input log2 ratios were plotted against probe position from −1000 bp to +3000 bp with respect to ATG at every time point . Phaseograms for expression and ChIP data were generated by fast Fourier transform method where probes/genes were sorted according to phase from −π to π with the mean-centered log2 ratios across all the time points . Pearson's Correlation coefficient ( PCC or r ) was calculated between the histone mark profiles and corresponding mRNA profiles across the IDC and skewness of correlation distribution was calculated . Negative skew values indicate a long tail on the negative side ( higher frequency on positive side ) . For ease of understanding , skew is represented as the negative of skew throughout the manuscript such that positive skew means a higher frequency on the positive side . To test how statistically significant the histone levels correlate with expression levels at each probe , we randomly generated marked histone and expression profile pairs by randomizing profiles between probes 100 times . The two-sample Kolmogorov-Smirnov ( KS ) test was used to test whether our observed correlation distributions are different from the random ones ( P value<0 . 0005 ) . The same analysis was also performed for correlations between histone modifications . Dice's coefficient was calculated in order to assess the overlap between any two histone modification profiles across the IDC . ChIP enrichment with respect to position in the gene was calculated using probes with oscillated profiles ( P<0 . 05 and fold change ≥1 . 5 across the IDC ) . For each histone mark , the number of ChIP-enriched probes showing positive ( r≥0 . 4 ) and negative ( r≤−0 . 4 ) correlation with expression profiles of corresponding genes were normalized to total probes in the input . Data were arranged into bins ranging from −1500 bp upstream of ATG to +3000 bp into the gene and plotted against the percentage of probes falling in each bin . RTQ-PCR was carried out on immunoprecipitated and input DNA using the SYBR Green PCR Master Mix ( Roche ) according to manufacturer's instructions . ChIP enrichment was calculated by using the ΔCt method ( Ct of immunoprecipitated target gene - Ct of input target gene ) where Ct is the threshold cycle . All PCR reactions were done in duplicates or triplicates . Vector pLN-ENR-GFP and P . falciparum strain Dd2attB were provided by D . Fidock . The GFP cassette from the vector pLN-ENR-GFP [32] was replaced by the firefly luciferase gene and hsp 86 3′ UTR from the plasmid pPF86 [54] at the Bam HI/Apa I sites to create the plasmid pLN-Luc . All constructs were confirmed by sequencing . For transfection , promoter regions of various genes were cloned upstream of the luciferase gene at the Bam HI/Sph 1 sties of pLN-Luc . Transfections of P . falciparum strain Dd2attB and subsequent drug selection were carried out as described [32] . Vehicle vector lacking the luciferase gene was used as a negative control and all transfectants were checked for firefly luciferase activity 48 h post infection using a reporter assay from Promega . Plasmid integration at cg6 locus was confirmed by PCR . All primer sequences used in the current study are listed in Table S4 . The microarray data have been submitted to NCBI GEO with accession number GSE39238 .
|
Malaria is a devastating parasitic disease caused by the protozoan protist Plasmodium falciparum . The complex life cycle of P . falciparum comprises various morphological and functionally distinct forms and is completed in two different hosts . Various regulatory mechanisms are employed by these parasites to complete their life cycle and survive in human hosts . Epigenetic mechanisms , though not fully explored , have been implicated as one of the key players in gene regulation , morphological differentiation and antigenic variation . Here , we present a comprehensive epigenetic map of 12 histone post-translational modifications during the intraerythrocytic life cycle of P . falciparum . We have been able to identify at least eight histone modifications whose dynamic patterns correlate with the transcriptional regulation across the life cycle . In particular , we have shown that a set of euchromatic histone marks work in synergy , creating a dynamic unique histone code that is linked with gene expression during the progression of the Plasmodium intraerythrocytic developmental cycle . These findings enhance our knowledge of complex gene regulation and will help to identify novel targets for fighting malaria .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biotechnology",
"systems",
"biology",
"functional",
"genomics",
"model",
"organisms",
"biology",
"genomics",
"microbiology",
"genetics",
"and",
"genomics",
"parasitology",
"parasite",
"physiology"
] |
2013
|
Dynamic Epigenetic Regulation of Gene Expression during the Life Cycle of Malaria Parasite Plasmodium falciparum
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The Fanconi anemia ( FA ) -BRCA pathway mediates repair of DNA interstrand crosslinks . The FA core complex , a multi-subunit ubiquitin ligase , participates in the detection of DNA lesions and monoubiquitinates two downstream FA proteins , FANCD2 and FANCI ( or the ID complex ) . However , the regulation of the FA core complex itself is poorly understood . Here we show that the FA core complex proteins are recruited to sites of DNA damage and form nuclear foci in S and G2 phases of the cell cycle . ATR kinase activity , an intact FA core complex and FANCM-FAAP24 were crucial for this recruitment . Surprisingly , FANCI , but not its partner FANCD2 , was needed for efficient FA core complex foci formation . Monoubiquitination or ATR-dependent phosphorylation of FANCI were not required for the FA core complex recruitment , but FANCI deubiquitination by USP1 was . Additionally , BRCA1 was required for efficient FA core complex foci formation . These findings indicate that FANCI functions upstream of FA core complex recruitment independently of FANCD2 , and alter the current view of the FA-BRCA pathway .
Fanconi anemia ( FA ) is a rare genetic disorder characterized by bone marrow failure , congenital malformations and cancer susceptibility [1] . Eighteen FA genes have been identified ( FANC-A , -B , -C , -D1/BRCA2 , -D2 , -E , -F , -G , -I , -J/BRIP1 , -L , -M , -N/PALB2 , -O/RAD51C , -P/SLX4 , -Q/XPF , -S/BRCA1 and -T/UBE2T ) [2–9] . The FA proteins function in a common DNA repair pathway ( the FA pathway or the FA-BRCA pathway ) , which coordinates the repair of interstrand-crosslinks ( ICLs ) . Disruption of this pathway renders cells sensitive to ICL-inducing agents , such as mitomycin C ( MMC ) [10] . Several FA genes are also breast and ovarian cancer susceptibility genes ( FANCD1/BRCA2 , FANCJ/BRIP1 , FANCN/PALB2 , FANCO/RAD51C , and FANCS/BRCA1 ) . Among these , BRCA2 , PALB2 , RAD51C and BRCA1 have well-defined roles in homologous recombination ( HR ) , linking the FA pathway to HR-mediated repair [11–13] . Eight of the FA proteins ( FANC-A , -B , -C , -E , -F , -G , -L and -M ) form a ubiquitin ligase complex ( the FA core complex ) with other associated proteins ( FAAP-10/MHF2 , -16/MHF1 , -20 , -24 and -100 ) in the nucleus [14–16] . Among the FA core complex subunits , FANCM , in complex with FAAP24 , is a platform for recruiting the rest of the FA core complex to chromatin [17 , 18] . The FA core complex is involved in sensing the DNA lesions and monoubiquitinates FANCD2 and FANCI [14–16 , 19] . Monoubiquitination of FANCD2 and FANCI is required for their localization to sites of DNA damage and efficient ICL-repair [20 , 21] . FANCD2 and FANCI work together as a protein complex ( the ID complex ) [21 , 22] . Optimal monoubiquitination of FANCD2 and FANCI also requires the ATR-dependent phosphorylation of FANCI at the S/TQ cluster domain [23 , 24] . Many proteins involved in DNA repair , including several FA proteins ( FANCD2 , FANCI , FANCJ , BRCA2 , BRCA1 , PALB2 , SLX4 , XPF and BRCA1 ) , accumulate at sites of DNA damage . This accumulation can be visualized as distinct nuclear foci . Visualization of foci has been a fundamental technique for understanding DNA repair pathways: ( i ) uncovering new players , ( ii ) identifying sequential steps in the pathways , ( iii ) understanding the interplay between different DNA repair pathways , and ( iv ) identifying mechanisms of regulation [25] . However , the recruitment of the FA core complex is poorly understood , due to the difficulty of detecting FA core complex proteins as nuclear foci , although FA core complex accumulation at sites of DNA damage has been sporadically reported [26–28] . To address this , we have optimized the immunocytochemical detection method , allowing the visualization of the FA core complex foci . This enabled us , for the first time , to comprehensively analyze how this early process in the activation of the FA pathway is regulated . Surprisingly , we have found that FANCI , which has been shown to work downstream of the FA core complex , is also required for efficient accumulation of the FA core complex at sites of DNA damage . This FANCI function was independent of its binding partner FANCD2 , FANCI monoubiquitination and FANCI phosphorylation . USP1 regulated FA core complex foci formation by deubiquitinating FANCI . Additionally , BRCA1 was required for efficient FA core complex foci formation . Our work challenges the linear , canonical model of the FA-BRCA pathway , and expands on the mechanism of its activation .
We carefully optimized immunostaining methods ( described in Methods ) and successfully detected nuclear foci of FANCA , FANCC , FANCE , FANCF , FANCG and FANCL in U2OS cells treated with MMC ( Fig 1A ) . All of these foci were reduced in FANCA-depleted cells , suggesting that foci formation of the FA core complex proteins is FANCA-dependent and are likely to represent foci formation of the canonical FA core complex . Specificity of the antibodies was also confirmed by western blotting ( S1G , S2E , S3C and S3D Figs ) . The formation of FANCA , FANCG and FANCE foci was induced by treatment with various DNA damaging agents ( MMC , cisplatin , hydroxyurea , a PARP inhibitor ( AZD2281 ) and ionizing radiation ( IR ) ) , in U2OS cells ( Fig 1B and S1A Fig ) . FANCA and FANCG foci were also observed in HeLa cells and fibroblasts ( S1B , S1C and S1E–S1G Fig ) . FANCA substantially colocalized with FANCD2 and γH2AX , but not TRF1 , suggesting that FA core complex proteins localize at sites of DNA damage and not at telomeres ( Fig 1C and S1D Fig ) . Recruitment of FANCA , FANCG , FANCC and FANCF to laser-induced localized DNA damage was also detected ( Fig 1D ) . We were also successful in detecting foci formation of exogenously expressed myc-tagged FANCG ( S4 Fig ) . In this case , detection of the foci was dependent on the level of expression . While high FANCG expression ( pMMP-FANCG construct ) resulted in pan-nuclear FANCG staining , foci were clearly detected ( both with antibodies against FANCG and MYC-tag ) when a low expressing ( pLentiX1-mycFANCG ) was used ( S4A and S4B Fig ) . Both high-expression and low-expression FANCG constructs rescued MMC sensitivity at the same level ( S4C Fig ) . The fact that all FA core complex proteins we tested formed foci at sites of DNA damage and that their recruitment was dependent on FANCA ( Fig 1A ) suggests that these proteins are present at sites of DNA damage as part of the FA core complex . To test this further , we examined the effects of depleting other components of the FA core complex on FA core complex foci formation . Depletion of FANCA , FANCC , FANCF or FANCL abolished the formation of FANCA , FANCG , FANCC , FANCL and FANCD2 foci , but not γ-H2AX foci ( Fig 1E and S2A–S2C Fig ) . Similarly , formation of FANCA and FANCG foci was impaired in FANCF-deficient cells ( TOV21G ) , but was restored in the corrected cells ( S2D and S2E Fig ) . The FA core complex is loaded onto chromatin through its interaction with FANCM/FAAP24 [17] . RNF8 has also been reported to mediate the recruitment of the FA core complex to psoralen- and laser-induced localized DNA damage [29] . FANCM and FAAP24 depletion abrogated the formation of FANCA , FANCG , FANCC and FANCL foci after MMC without affecting their protein levels ( Fig 1E and S2A , S3A and S3B Figs ) . On the other hand , RNF8 depletion did not affect FANCA , FANCG , FANCC , FANCL or FANCD2 foci , while BRCA1 foci were reduced ( Fig 1E and S2A , S2B , S2F and S2G Fig ) , consistent with the previous reports [30] . These findings suggest that the whole FA core complex including FANCM/FAAP24 , but not RNF8 , is critical for recruitment of the FA core complex to sites of DNA damage . The foci formation kinetics of FA core complex was similar to that of FANCD2 , both after MMC pulse treatment ( Fig 2A ) and IR exposure ( S5A Fig ) . Cell synchronization after release from nocodazole arrest revealed that FANCA foci were efficiently induced in S and G2 phases , in untreated cells or after IR , but not in G1 ( Fig 2B ) . Furthermore , more than 95% of cells with FANCA or FANCG foci were cyclin A ( a S/G2-phase marker ) -positive ( Fig 2C ) . These data indicate that FA core complex foci form during S and G2 phases . Similar results were obtained for FANCD2 foci ( Fig 2C ) . BRCA1 , which normally forms foci in S and G2 phases , is able to localize to sites of DNA damage during G1 when 53BP1 or RIF1 are absent [31 , 32] . Consistent with these reports , the proportion of BRCA1 foci-containing cells was vastly greater than the proportion of cyclin A-positive cells when 53BP1 or RIF1 were depleted ( S5C and S5D Fig ) , demonstrating that BRCA1 foci formed in G1 in these conditions . In contrast , depletion of 53BP1 or RIF1 did not significantly alter the cell cycle distribution of FANCA ( or FANCD2 ) foci-containing cells , with more than 90% of them corresponding to cyclin A-positive cells ( Fig 2D and S5B Fig ) . These results indicate that the FA core complex and FANCD2 foci form almost exclusively in S and G2 , even in the absence of 53BP1 or RIF1 , and suggest that the mechanisms of recruitment are distinct from those of BRCA1 . The ability to detect recruitment of FA core complex proteins at sites of DNA damage allowed us to search for factors required for the formation of these foci and therefore gain deeper insight into how the FA pathway is regulated . ATR is the primary kinase that controls FA pathway activation [23 , 24 , 33] . However , whether ATR is required for FA core complex recruitment is unknown . A strong reduction in the number of cells containing FANCA , FANCG , FANCC , FANCL or FANCD2 foci was observed in the ATR-deficient cells ( F02-98 fibroblasts and ATR-depleted U2OS cells ) , but not in ATR-proficient control cells ( Fig 3A and S6A and S6B Fig ) . ATR specific inhibitor ( VE-821 ) [34] , but not ATM inhibitor , impaired the formation of both FA core complex and FANCD2 foci ( Fig 3B and S3C , S6C and S6D Fig ) , indicating that ATR kinase activity is required for FA core complex foci formation . FANCD2 and FANCI function together downstream of the FA core complex [21 , 22] . Unexpectedly , we observed a decrease in FANCA , FANCG , FANCC and FANCL foci formation when FANCI , but not FANCD2 , was depleted in U2OS cells ( Fig 4A and 4B and S7 Fig ) , suggesting that only FANCI is required for efficient FA core complex foci formation . Consistent with this , FANCD2-deficient fibroblasts ( PD20 ) and PD20 transfected with wild-type FANCD2 or non-ubiquitinatable K561R mutant of FANCD2 showed similar degrees of FANCA foci formation ( Fig 4C ) . In the absence of FANCD2 , FANCI was not ubiquitinated ( Fig 4B ) and was not bound to chromatin ( S10C Fig ) , suggesting that FANCI ubiquitination and chromatin binding are dispensable for FA core complex foci formation . In a FANCI-deficient fibroblast cell line F010191 [22] , wild-type FANCI , but not a non-ubiquitinatable K523R mutant or a DNA-binding mutant K294E/K339E [35] , were able to sustain FANCD2 foci ( Fig 4D and S7B Fig ) , as previously described [36] . In contrast , the three FANCI constructs ( wild-type , K523R and K294E/K339E ) rescued FANCA foci ( Fig 4D and S7B–S7D Fig ) , indicating that FANCI is required for FA core complex foci independently of its ubiquitination and DNA binding . Next we tested if ATR-mediated phosphorylation of FANCI is required for FANCA foci formation . FANCI-deficient F010191 fibroblasts were transduced with wild-type FANCI , a series of FANCI phosphomutants ( Ax2-Ax6 ) or a phosphomimetic mutant ( Dx6 ) ( Fig 5A ) . Consistent with a previous report [24] , increasing the number of mutated phosphorylation sites ( Ax2-Ax6 ) resulted in progressively stronger suppression of FANCD2 foci formation and monoubiquitination ( Fig 5B and S8 Fig ) . Expression of the Dx6 mutant resulted in increased basal FANCD2 and FANCI ubiquitination and partial restoration of FANCD2 foci ( Fig 5B and S8 Fig ) . In contrast , all FANCI phosphomutants were able to rescue FANCA foci , while the Dx6 mutant rescued FANCA foci partially ( Fig 5B and 5C ) . Similar results were obtained using FANCI-knockout HCT116 cells ( S9 Fig ) . In this cell line , however , transduction of the Dx6 mutant resulted in a full correction of FANCA foci . These results demonstrate that FANCI promotes FA core complex foci formation independently of the phosphorylation status of the S/TQ cluster domain . ATR could also promote FA core complex foci formation through FANCI phosphorylation at other sites outside the S/TQ cluster domain . To test if ATR and FANCI promote FA core complex foci formation through shared or distinct mechanisms , we depleted ATR in FANCI-deficient cells ( F010191 ) . Compared to FANCI deficiency only , ATR depletion resulted in an additional defect in FANCA foci formation ( Fig 5D ) , suggesting that ATR and FANCI promote FA core complex recruitment through independent mechanisms . Identical results were obtained when ATR and FANCI were co-depleted in U2OS ( S10A Fig ) . F010191 cells express a C-terminal truncated form of FANCI [22 , 36] . Depletion of this truncated FANCI did not further suppress FANCA foci , indicating that the truncated FANCI does not support FA core complex foci formation ( Fig 5D ) . To better understand how ATR and FANCI promote recruitment of the FA core complex to sites of DNA damage , we analyzed the binding of FANCA , FANCC and FANCL to chromatin using cell fractionation . Relative to untreated cells , the amount of chromatin-bound FANCA , FANCC or FANCL was not increased in MMC-treated cells ( S10D Fig ) , indicating that FA core complex chromatin binding and foci formation are two discrete steps . In U2OS cells , FANCI depletion , but not ATR or FANCD2 depletion , significantly reduced the amount of FANCA bound to chromatin ( insoluble fraction ) ( S10B and S10C Fig ) . This data further supports the previous observations that FANCI regulates FA core complex recruitment at sites of DNA damage in a FANCD2- independent manner , and through a mechanism distinct from ATR-mediated recruitment . However , the same defect in FANCA chromatin binding was not observed when comparing FANCI-knockout and wild-type HCT116 ( S10E Fig ) , although depletion of FANCM did not cause the expected reduction of chromatin-bound FANCA either . The cause of discrepancy between the two cell lines is not obvious . It suggests that FANCI may facilitate two different steps in the regulation of FA core complex recruitment at sites of DNA damage in a context-dependent manner: 1 ) binding to chromatin , and 2 ) accumulation at sites of DNA damage ( or foci formation ) . Our data indicates that non-phosphorylated FANCI is able to perform part of its functions within the FA pathway by promoting FA core complex recruitment . Therefore , we hypothesized that non-phosphorylated FANCI may contribute to cellular resistance to ICL-inducing agents . To test this , we analyzed MMC sensitivity of FANCI-deficient F010191 cells transduced with wild-type , K523R , Ax6 or Dx6 FANCI constructs . Consistent with our hypothesis , K523R , Ax6 and Dx6 mutants partially rescued MMC resistance , at similar levels ( Fig 5E ) . Similar results were obtained using FANCI-knockout HCT116 cells ( S9D Fig ) . This suggests that FA core complex foci formation may play a role in conferring cellular resistance to ICL-inducing agents . Our demonstration of a role for FANCI upstream of FA core complex recruitment suggests that the FA pathway may not be as linear as current models suggest . It also raises the possibility that other factors known to work downstream of the FA core complex may also regulate FA core complex recruitment . To test this , we analyzed FA core complex foci formation in cells deficient in several other proteins involved in the FA pathway ( BRCA1/FANCS , BRCA2/FANCD1 , CtIP , FANCJ/BRIP1 , FANCN/PALB2 , FANCP/SLX4 , FANCQ/XPF and USP1 ) ( S2 Table ) . Surprisingly , both BRCA1 and USP1 were required for FANCA and FANCG foci formation . Depletion of BRCA1 resulted in a strong reduction in FANCA , FANCG , FANCC , FANCL and FANCD2 foci formation in U2OS and HeLa cells ( Fig 6A and S11A and S11B Fig ) , without affecting FANCA protein levels or FANCD2 monoubiquitination ( S11C Fig ) . A similar reduction in FA core complex foci-containing cells was observed in BRCA1-deficient HCC1937 , when compared to BRCA1-complemented HCC1937 ( Fig 6B ) . To better characterize how BRCA1 promotes FA core complex foci formation , we then tested whether this function was mediated by known BRCA1-interacting proteins ( S2 Table and S11D–S11H Fig ) . No defect in FANCA or FANCG foci was observed in cells deficient in FANCJ/BRIP1 , FANCD1/BRCA2 , FANCN/PALB2 , FAM175A/ABRAXAS , UIMC1/RAP80 or RBBP8/CtIP , suggesting that BRCA1 performs this function independently , or through a binding partner other than the ones tested . FANCA , FANCG , FANCC and FANCL foci formation were impaired in USP1-depleted U2OS cells and in cells treated with a USP1 specific inhibitor , ML323 [37] ( Fig 7A and 7B and S12A Fig ) . Increasing concentrations of ML323 resulted in decreased FANCA and FANCD2 foci , without affecting BRCA1 or γH2AX foci ( Fig 7B ) . ML323 treatment resulted in increased FANCD2 and FANCI ubiquitination , confirming USP1 inhibition ( S12B Fig ) . To confirm the siRNA and inhibitor data , USP1-depleted cells were complemented using an siRNA-resistant USP1 cDNA . Due to the high instability of overexpressed wild-type USP1 protein , a form of USP1 that is unable to cleave itself , GG670/671AA , ( described in [38] ) was used . The catalytic function of GG670/671AA mutant of USP1 is comparable to wild-type USP1 [38] . The FA core complex and FANCD2 foci defect was rescued when wild-type USP1 , but not the catalytic inactive form ( USP1 C90S ) , was expressed ( Fig 7C and 7D and S12C Fig ) . In both cases , an overall reduction in the number of cells with foci was observed , likely due to cellular toxicity of USP1 overexpression . These data indicate that catalytic activity of USP1 is required to promote efficient recruitment of FA core complex and FANCD2 to sites of DNA damage . They also indicate that impaired FA core complex recruitment at sites of DNA damage does not necessarily translate into deficient FANCD2-FANCI ubiquitination . Since both non-ubiquitinated FANCI and USP1 catalytic activity promoted FA core complex foci formation , next we tested the possibility that FANCI was the relevant substrate for USP1 in this function . We first tested epistasis between FANCI and USP1 . USP1 was depleted using siRNA from FANCI-deficient F010191 cells . As shown in Fig 8A , USP1 depletion did not result in an increased loss of FANCA foci in FANCI-deficient cells , suggesting that FANCI and USP1 promote FA core complex foci formation through the same mechanism . If FANCI is the relevant USP1 substrate to promote FA core complex formation , overexpression of a non-ubiquitinatable FANCI should be able to rescue FA core complex foci formation in USP1-depleted cells . Therefore , we overexpressed wild-type or K523R mutant of FANCI in cells that were either transfected with siRNA control or siUSP1 . As shown in Fig 8B , overexpression of the FANCI K523R mutant partially rescued FANCA foci formation in USP1-depleted cells , while the wild-type FANCI was not able to do so . These results suggest that FANCI needs to be deubiquitinated by USP1 to promote FA core complex foci efficiently .
Through the analyses of the FA core complex foci formation , we have elucidated several new regulatory mechanisms of the FA core complex recruitment . First , the FA core complex accumulated at sites of DNA damage in a manner dependent on the whole FA core complex , including FANCM/FAAP24 , the canonical platform that loads the FA core complex to DNA [17] . An alternative mechanism of FA core complex recruitment at laser-induced localized ICLs , involving FAAP20 binding to RNF8-catalyzed polyubiquitin chains , has been described [29] . However , this mechanism did not make a significant contribution in our system . The discrepancy may be attributable to the different systems used to induce DNA damage and exemplifies the importance of assessing recruitment using foci formation . Repair of DSBs in mammalian cells occurs mainly by two major different pathways: non-homologous end-joining ( NHEJ ) , which predominates during G1 , and homologous recombination ( HR ) , which predominates during S and G2 [39] . As with other proteins involved in the FA pathway and HR , FA core complex foci formed during S and G2 . Recently , some light has been shed on the molecular mechanisms that control DNA repair pathway choice , identifying 53BP1-RIF1 and BRCA1-CtIP as central players in this process [31 , 32 , 40–42] . These studies suggest that enabling BRCA1 to bind to DSBs by depleting 53BP1 or RIF1 , allows for CtIP-mediated resection in G1 . However , depleting 53BP1 or RIF1 was not enough to promote FA core complex or FANCD2 recruitment in G1 . This data is consistent with our observation that , although efficient FA core complex recruitment depended on BRCA1 , it did not depend on CtIP . Therefore , alternative activation mechanisms and/or DNA structures , not present in G1 , will be needed for FA core complex recruitment at DNA lesions . Consistent with this , in vitro studies showed that FANCM , together with FAAP24 and MHF1-MHF2 , have a strong affinity for branched DNA structures that resemble replication forks or Holliday junctions [43–45] . The recruitment of the FA core complex at sites of DNA damage is far from well understood . Our data suggests that the regulation of this process is more complex than initially envisioned . Through a candidate approach directed to proteins that participate in the repair of ICLs , we have identified four proteins that are required for FA core complex foci formation: ATR , FANCI , BRCA1 and USP1 . Among these , FANCI , BRCA1 and USP1 are especially interesting , since they have been previously thought to function exclusively downstream of FA core complex . Our findings suggest that they also act upstream , by promoting FA core complex recruitment to sites of DNA damage . We show that FANCI has a function upstream of the FA core complex and independent of FANCD2 . FANCD2 and FANCI were previously considered to be obligate partners: they require each other for ubiquitination , foci formation and , partially , protein stability [21 , 22 , 46] . More recent studies , however , have shown that FANCD2 and FANCI exhibit different responses to DNA damage [47] . Also , a FANCI-independent function of FANCD2 in promoting replication fork recovery through association with the BLMcx complex has been reported [48] . Our study supports the model that FANCD2 and FANCI have both dependent and independent roles in the DNA damage response , and identifies FA core complex foci formation as a novel FANCD2-independent function of FANCI . Unlike FANCI function in promoting FANCD2 foci formation and ubiquitination , FA core complex recruitment by FANCI was independent of FANCI DNA binding , ubiquitination and phosphorylation of the S/TQ cluster domain , and was also distinct from the ATR-mediated mechanism . All these data together show that FANCI has at least two independent functions within the FA pathway: ( i ) regulation of FANCD2 foci/ubiquitination and ( ii ) regulation of FA core complex foci . Both FANCI phosphomutant ( Ax6 ) and phosphomimetic mutant ( Dx6 ) , as well as the non-ubiquitinatable FANCI ( K523R ) , significantly rescued MMC sensitivity in two different human FANCI-deficient cell lines . This data differs from studies in chicken DT40 cells , where the Ax6 mutant did not rescue sensitivity to ICL-inducing agents [24] . The discrepancy may be attributable to differences between human and chicken systems . It is particularly interesting that a phosphomutant FANCI ( Ax6 ) , while unable to support FANCD2 foci and ubiquitination , largely rescued MMC sensitivity . This data is consistent with an additional role of non-phosphorylated , non-ubiquitinated FANCI in the repair of ICLs . It is tempting to speculate that the two different pools of FANCI ( phosphorylated-ubiquitinated and non-phosphorylated , non-ubiquitinated FANCI ) may contribute independently to ICL-resistance by performing different functions . In support of this model , a function of unphosphorylated FANCI in the regulation of dormant origin firing was recently reported [49] . Our findings together with this report emphasize that both phosphorylated FANCI and unphosphorylated FANCI are needed for cellular resistance to ICLs . In the work presented by Chen and coworkers [49] , FANCI phosphomimic mutant Dx6 cannot activate dormant origins or reverse MMC sensitivity in FANCI-depleted retinal pigment epithelial ( RPE ) cells . However , in our studies , the Dx6 mutant partially restored MMC resistance in FANCI-deficient patient-derived fibroblasts and FANCI-knockout HCT116 cells . This discrepancy may be explained by differential dependence on dormant origin firing for ICL tolerance among the cell lines . Our results indicate that FA core complex binding to chromatin precedes , and is independent of , foci formation ( S10D Fig ) . FANCI may affect the ability of the FA core complex to bind to chromatin in some cell lines such as U2OS . However , this phenomenon was not observed in HCT116 cells where FANCI was still required for the FA core complex to accumulate or persist at sites of DNA damage . Further investigations will be required to understand how this process occurs and to identify the reasons for these differences among cell lines . ATR kinase activity was required for FA core complex foci formation . This was not unexpected , as ATR is involved in the activation of the FA pathway [23] . However , our findings suggest that ATR mediates FA core complex foci formation independently of FANCI phosphorylation , a well-characterized mechanism of ATR-mediated FA pathway activation [24] . Therefore , ATR will control the FA pathway at , at least , two steps . The relevant substrate of ATR in the FA core complex foci formation remains unknown . Possible candidates include FANCA , FANCG or FANCM , as they are phosphorylated by ATR [33 , 50 , 51] . The search for other factors with roles upstream of FA core complex foci formation uncovered two additional positive regulators: BRCA1 and USP1 . This finding was surprising , since depletion of neither of these two proteins decrease the level of ubiquitination of FANCD2 or FANCI , and their ubiquitination is even increased in the case of USP1 depletion . These findings have , at least , two possible explanations: ( i ) residual levels of FA core complex foci formation observed in cells deficient for BRCA1 or USP1 are enough to induce normal ( or increased ) FANCD2-FANCI ubiquitination , or ( ii ) FA core complex foci formation and FANCD2-FANCI ubiquitination are uncoupled . Regardless of which of the two scenarios is correct , the fact that FA core complex foci and FANCD2-FANCI ubiquitination do not correlate suggests that the accumulation of the FA core complex at sites of DNA damage may serve additional functions . In support of this model , a mutation in FANCA ( I939S ) that does not impair FANCD2 monoubiquitination was recently described in an FA patient [52] . Also , the fact that depletion of USP1 results in increased ubiquitination of FANCD2-FANCI in the absence of DNA damage ( our data and [53] ) suggests that ubiquitination may not necessarily happen at sites of DNA damage , and may therefore be disconnected from FA core complex foci formation . It would be interesting to test if FA core complex foci are able to form in FANCA I939S and/or other separation-of-function mutants . We showed that deubiquitination of FANCI by USP1 is an important step to recruit the FA core complex at sites of DNA damage . This result uncovers the existence of a feedback loop , centered on FANCI: not only ubiquitination , but also deubiquitination , is required for the proper functioning of the FA pathway . We have also , for the first time , uncovered a role of USP1 as a positive regulator of the FA pathway . More experiments are required to assess if deubiquitination of FANCI alone can explain ICL sensitivity of USP1-deficient cells . It is important to note that BRCA1 or USP1 deficiency also leads to impaired FANCD2 foci formation [20 , 54] . How BRCA1 promotes recruitment of both FANCD2 and FA core complex remains enigmatic . A recent in vitro study shows that BRCA1 acts in ICL repair upstream of DSB formation by facilitating eviction of the replicative helicase [55] . Therefore , this step could be required for FANCD2 and FA core complex foci formation Taken together , we have optimized a protocol to visually detect the recruitment of the FA core complex to sites of DNA damage , which allowed us to identify regulators of FA core complex recruitment , and therefore , elucidate a previously unexplored aspect of the FA pathway . With the remarkable findings that FANCI , USP1 and BRCA1 , as well as ATR and FANCM , promote FA core complex foci formation , we provide evidence that the FA pathway is a non-linear , tightly regulated pathway , with several proteins ( ATR , FANCI , USP1 and BRCA1 ) performing roles at multiple stages of its activation ( S13 Fig ) .
U2OS , HeLa , HCC1937 and TOV-21G were purchased from the American Type Culture Collections . FANCF-corrected TOV21G [56] , SV40 transformed FA fibroblasts ( 326SV FA-G-/- , GM6914 FA-A-/- , PD20 FA-D2-/- ) and their corrected counterparts [20 , 57–59] , hTERT-immortalized ATR deficient F02-98 fibroblasts and their corrected counterparts [23 , 60] , VU423 fibroblasts and VU423 corrected with chromosome 13[11] , have been described . F010191 transformed fibroblasts [22] were a gift from Dr . Tony Huang ( New York University ) . AG656 fibroblasts and AG656+FANCJ were a gift from Dr . Sharon Cantor ( University of Massachusetts ) . EUFA1341 fibroblasts and EUFA1341+PALB2 were a gift from Dr . Paul Andreassen ( Cincinnati Children’s Hospital ) . HCC1937 cells were cultured in RPMI supplemented with 15% FCS . HCT116 FANCI-/- were grown in McCoy’s 5A media supplemented with 10% FCS and 1% glutamine . All other cell lines were grown in DMEM supplemented with 10% FCS . All cells were maintained in a humidified 5% CO2 atmosphere at 37°C . Cells were treated with MMC ( Sigma , M4287 ) , cisplatin ( Sigma , P4394 ) , hydroxyurea ( HU ) ( Sigma , H8627 ) , AZD2281 ( Selleckchem , S1060 ) ionizing radiation ( IR ) ( JL Shepherd Mark I Cesium Irradiator ( JL Shepherd & Associates ) ) , ATR inhibitor VE-821 ( Axon Medchem , 1893 ) , ATM inhibitor KU55933 ( Selleckchem , S1092 ) or USP1 inhibitor , ML323 [37] ( gift from Dr . Zhihao Zhuang , University of Delaware ) . siRNA transfections were conducted in 6-well plates using Lipofectamine RNAiMAX ( Invitrogen ) , following the manufacturer’s instructions . 20nM siRNA was used in each transfection . siRNA sequences are provided in S1 Table . FANCI-null HCT116 cells were generated using recombinant adeno-associated virus ( rAAV ) -mediated gene targeting [61] . Conditional and knock-out rAAV vectors targeting FANCI exon 10 were constructed as described [62 , 63] . The first round of targeting with the conditional vector replaced FANCI exon 10 with a floxed exon 10 along with a neomycin ( Neo ) drug selection cassette . G418-resistant clones were screened by PCR to confirm correct targeting and the Cre recombinase was subsequently used to remove the Neo selection cassette . Retention of the floxed exon 10 in the conditional allele was confirmed by PCR . The second round of gene targeting was performed with a knock-out vector that replaced exon 10 with a Neo-selection cassette . G418 resistant clones were again screened by PCR for correct targeting . Cre recombinase was subsequently used to remove both the Neo selection cassette and the floxed conditional allele and this resulted in viable FANCI-null clones . The PCR primers flanking FANCI exon 10 that were used to confirm both conditional and null alleles were FancIc_GG_LIF: GCAATGGCACAATCTTGG and FancIcond_GG_loxR: ATAGAACTTTCTGGCTTGCT . Cell fractionations were prepared as described [17] . Briefly , cells were resuspended in buffer CSK ( 10mM PIPES , pH = 6 . 8 , 100mM NaCl , 1mM EGTA , 1mM EDTA , 300mM Sucrose , 1 . 5mM MgCl2 , 0 . 1% Triton-X-100 and protease inhibitors ) and incubated in ice for 5min . Samples were centrifuged at 1500g for 5min . Supernatant was collected and stored ( soluble fraction ) . Pellets ( insoluble fraction ) were washed once in CSK buffer and then resuspended in sample buffer ( 0 . 05 M Tris-HCl ( pH 6 . 8 ) , 2% SDS , 6% β-mercaptoethanol ) and boiled for 5min . Western blotting was performed as described [64] . Briefly , cells were lysed in 0 . 05 M Tris-HCl ( pH 6 . 8 ) , 2% SDS , 6% β-mercaptoethanol and boiled for 5min . SDS-PAGE electrophoresis was done using NuPAGE 3% to 8% Tris-acetate or NuPAGE 4% to 12% Tris-glycine gels ( Invitrogen ) and proteins were transferred to a nitrocellulose membrane . Primary antibodies were diluted in blocking buffer ( 5% milk in TBS-Tween 20 ) and incubated overnight . Horseradish peroxidase—conjugated anti-mouse and anti-rabbit IgG ( Amersham ) were used as secondary antibodies . Images were acquired using ImageQuant LAS4000 system ( GE Healthcare ) . Human FANCI coding sequence was amplified by PCR to include a Myc tag sequence at the 5’ end of the gene and cloned into pLentiX1-puro ( a gift from Eric Campeau , Addgene plasmid # 20953 ) using SalI and XbaI sites . Ubiquitin C promoter was extracted from pUB-GFP ( a gift from Connie Cepko , Addgene plasmid # 11155 ) using a SalI digestion . The resulting 1 . 2 Kb fragment was cloned into pLentiX1-MycFANCI at the SalI site . K523R , Ax2-Ax6 , Dx6 and KKEE mutants of FANCI were generated using an overlap extension PCR method . Myc-USP1 and Myc-FANCG were cloned into pLentiX1-puro using the same strategy . FANCI-containing lentiviruses were produced as described [65] . F010191 fibroblasts or FANCI-deficient HCT116 cells were transduced with fresh lentiviruses-containing supernatants and selected for 2 days with 2μg/ml puromycin . Fresh transductions were used in each experiment , as exogenous FANCI expression was lost when cells underwent prolonged culture after transduction . Cells were grown on coverslips and then fixed and permeabilized for 30 minutes using 4% paraformaldehyde ( Santa Cruz , sc-281692 ) containing 0 . 5% Triton X-100 . After fixation , cells were washed with PBS and then blocked for 15 minutes in PBS containing 3% BSA+ 0 . 1% Tween20 . Primary antibodies were diluted in blocking buffer and incubation was performed at room temperature for one hour on a rocker . After 3 washes with PBS + 0 . 1% Tween20 , cells were incubated with secondary antibodies diluted in blocking buffer for another 45 minutes . AlexaFluor 488 Goat Anti-Rabbit IgG and AlexaFluor 594 Goat Anti-Mouse IgG were used as secondary antibodies ( Molecular Probes ) . At this point , 1μg/ml of 4' , 6-Diamidino-2-Phenylindole , Dihydrochloride ( DAPI ) was added to the cells and incubated for an additional 15 minutes . Cells were washed 3 times with PBS + 0 . 1% Tween20 and then coverslips were mounted using Vectashield Mounting Media ( VectorLabs , H-1000 ) . Image acquisitions were made with a TE2000 Nikon microscope equipped with a 60X immersion objective and a CCD camera ( CoolSNAP ES , Photometrics ) . Images were acquired and analyzed using MetaVue ( Universal Imaging ) and ImageJ . Manual counting was used and cells containing more than 5 foci were scored as positive . When possible , cells were seeded in glass-bottom 96 well-plates ( Greiner Bio-One , 655892 ) and images were acquired with Cellomics ArrayScan automated microscope equipped with a 20X objective . In this case , foci counting was automated using Cellomics software and reported as “foci per cell” . To generate localized DNA lesions , we used a published method [66] with some modifications . Cells were grown on 35mm glass bottom Fluorodish cell culture dishes ( World Precision Instruments ) for 24 hours before the experiment . One hour before the experiment , cells were placed in warm CO2-independent medium ( Life Technologies ) . Cells were pre-sensitized with 10 μg/ml viable Hoechst dye 33258 ( Sigma-Aldrich ) for 5 min at 37°C . We performed laser microirradiation using an Nikon Ti fully-motorized inverted spinning disk confocal microscope ( UltraView Vox , Perkin Elmer ) equipped with a 37°C heating chamber and a 405 nm laser focused through a Nikon Plan Fluor 40x/1 . 3 NA oil objective . We set the laser output to 100% of maximum power to generate in three iterations in a restricted region detectable localized DNA damage that was restricted to the laser path , dependent on prior pre-sensitization of the cells , and without noticeable cytoxicity . Cells were fixed 30 minutes after generating DNA damage and stained with the indicated antibodies . The following primary antibodies were used: anti-FANCD2 ( Abcam , ab2187 ) , anti-FANCA ( Bethyl , A301-980A ) , anti-Vinculin ( Sigma , V9131 ) , anti-ATR ( Santa Cruz , sc-1887 ) , anti-FANCI ( Santa Cruz , sc-271316 ) , anti-Actin ( Santa Cruz , sc-1616 ) , anti-BRCA1 ( Santa Cruz , sc-6954 ) , anti-CHK1 ( Santa Cruz , sc-8408 ) , anti-pCHK1 S345 ( Cell Signalling , 23415 ) , anti-FLAG M2 ( Sigma , F1804 ) , anti-γH2AX ( Upstate , JBW3001 ) , anti-CyclinA ( Abcam , ab16726 ) , anti-MYC tag 9E10 ( Upstate , 05–419 ) , anti-TRF1 ( Abcam , ab10579 ) , anti-RAD51 ( CosmoBio , BAM-70-001-EX ) , anti-FANCJ ( Sigma , B1312 ) , anti-BRCA2 ( Calbiochem , OP95 ) , anti-ABRAXAS ( Bethyl , A302-180A ) , anti-RAP80 ( Bethyl , A300-763A ) . Anti-FANCG [67] , FANCC [68] , FANCE [69] , FANCF [70] and FANCD2 pT691 [71] were gifts from Dr . Alan D’Andrea ( Dana-Farber Cancer Institute , Boston ) . Anti-USP1 ( C-ter ) [38] was a gift from Dr . Tony Huang . Anti-PALB2 [72] was a gift from Drs . David Livingston and Bing Xia . Anti-FANCL and anti-FANCM were gifts from Dr . Weidong Wang . Cell synchronization in M-phase was achieved by incubating cells in nocodazole ( Sigma , M1404 ) at 0 . 1μg/ml for 16 hours . Floating cells ( M-phase cells ) were then recovered by shaking the culture flask , and re-seeded in 6 cm dishes and on coverslips in fresh medium without nocodazole . During a period of 21 hours , cells were collected every 3h for cell cycle analysis and immunostaining . For each time point , coverslips were irradiated with 10Gy IR 60min prior the collection . For cell cycle analysis , cells were fixed in ice-cold 70% ethanol for 16 hours . Then , ethanol was removed and cells were incubated in PBS + propidium iodide 40μg/ml + RNase A 0 . 1mg/ml for 30 minutes at 37°C . After that , cells were kept on ice until analysis by flow cytometry . Flow cytometry analyses were performed on BD FACSCanto II flow cytometers and analyzed with Flow Jo software . Cell survival was measured by a crystal violet absorbance-based assay . Cells were seeded onto 12-well plates at a density of 9000 cells/well . The next day , cells were treated with increasing concentrations of MMC and incubated for eight more days . After that , plates were processed as described [65] . mRNA was converted to cDNA using Superscript III Reverse Transcriptase ( Invitrogen ) following manufacturer’s instructions . To measure specific gene expression , the following sets of primers were used: GAPDH: 5’ GGAGTCAACGGATTTGGTCG 3’ and 5’ CTCCTGGAAGATGGTGATGG 3’; RNF8: 5’ AAGATGGGTGCGAGGTGACT 3’ and 5’ ACGCGCTCTGTTCAGCCAAA 3’ . All statistical analyses were done using Student’s t-test ( 2-tail ) . P value < 0 . 05 was considered significant .
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Fanconi anemia is a genetic disease characterized by bone marrow failure , congenital malformations and cancer predisposition . Cells derived from Fanconi anemia patients have a dysfunctional FA-BRCA pathway and are deficient in the repair of a specific form of DNA damage , DNA interstrand-crosslinks , that are induced by certain chemotherapeutic drugs . Therefore , the study of FA-BRCA pathway regulation is essential for developing new treatments for Fanconi anemia patients and cancer patients in general . One of the first steps in the pathway is the detection of DNA lesions by the FA core complex . We have optimized a method to visualize the recruitment of the FA core complex to sites of DNA damage and , for the first time , explored how this process occurs . We have uncovered several factors that are required for this recruitment . Among them is a FA core complex substrate , FANCI . We report that non-phosphorylated FANCI , previously believed to be an inactive form , has an important role in the recruitment of the FA core complex and DNA interstrand-crosslink repair . Our findings change the current view of the FA-BRCA pathway and have implications for potential clinical strategies aimed at activating or inhibiting the FA-BRCA pathway .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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FANCI Regulates Recruitment of the FA Core Complex at Sites of DNA Damage Independently of FANCD2
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Msb2 is a sensor protein in the plasma membrane of fungi . In the human fungal pathogen C . albicans Msb2 signals via the Cek1 MAP kinase pathway to maintain cell wall integrity and allow filamentous growth . Msb2 doubly epitope-tagged in its large extracellular and small cytoplasmic domain was efficiently cleaved during liquid and surface growth and the extracellular domain was almost quantitatively released into the growth medium . Msb2 cleavage was independent of proteases Sap9 , Sap10 and Kex2 . Secreted Msb2 was highly O-glycosylated by protein mannosyltransferases including Pmt1 resulting in an apparent molecular mass of >400 kDa . Deletion analyses revealed that the transmembrane region is required for Msb2 function , while the large N-terminal and the small cytoplasmic region function to downregulate Msb2 signaling or , respectively , allow its induction by tunicamycin . Purified extracellular Msb2 domain protected fungal and bacterial cells effectively from antimicrobial peptides ( AMPs ) histatin-5 and LL-37 . AMP inactivation was not due to degradation but depended on the quantity and length of the Msb2 glycofragment . C . albicans msb2 mutants were supersensitive to LL-37 but not histatin-5 , suggesting that secreted rather than cell-associated Msb2 determines AMP protection . Thus , in addition to its sensor function Msb2 has a second activity because shedding of its glycofragment generates AMP quorum resistance .
Crosstalk between pathogens and the human host determines the outcome of microbial colonization and disease [1] . Pathogen-host communication occurs between cells and secreted proteins of both organisms . Surface structures of the important human fungal pathogen Candida albicans bind to dectin receptors on immune cells and trigger responses inhibiting fungal proliferation including the production of antimicrobial peptides ( AMPs ) and reactive oxygen species ( ROS ) ( for a review , see [2] , [3] . In addition , binding to immunoglobulins and complement factors by the fungal pathogen facilitate its phagocytosis and killing ( for a review , see [4] ) . Conversely , C . albicans partially overcomes host defenses by secreting hydrolytic enzymes and proteins that block the complement system ( for a review , see [4] , [5] ) . Furthermore , by switching its growth from a yeast to a hyphal growth form C . albicans is able to evade immune cells and to penetrate into host niches less accessible to the immune system . Survival of fungal pathogens in the human host requires that their cell surfaces are intact . Defects in the cell wall of C . albicans that occur under immune attack or by treatment with antifungals are sensed and activate compensatory activities [6] . Reduced glucan content leads to the activation of the protein kinase C ( PKC ) pathway that includes the Mkc1 MAPK module , which activates the glucan synthase activity and stimulates the transcription of genes involved in glucan and chitin biosynthesis [7] , [8] . In addition , defective N- or O-glycosylation activates the Cek1 MAPK module and recent results indicate that PMT genes encoding protein-O-mannosyltransferases are downstream regulatory targets [9] , [10] . Sensing through this pathway is accomplished by the Msb2 and Sho1 cytoplasmic membrane proteins , which signal via the Cdc42 GTPase to Cek1 . Intact N-glycosylation is detected by Msb2 and represses PMT1 transcription , while defective N-glycosylation induces Cek1 phosphorylation and de-represses PMT1 transcription [9] , [10] . In a different mode of regulation , defective Pmt1-type O-glycosylation is sensed by Msb2 , activates Cek1 and induces PMT2 and PMT4 expression . Induction of PMT2/PMT4 genes by inhibition of Pmt1 and damage of β1 , 3-glucan also requires Msb2 and Cek1 suggesting that cell wall damage is reported to Cek1 via Msb2 [10] . This function of Msb2 is supported by its associated partner membrane protein Sho1 [9] . Defects in either Mkc1 or Cek1 pathways lead to defective hypha formation on some semi-solid media , supersensitivity against antifungals and other stressors and reduce the virulence of C . albicans [9] , [11] , [12] . Msb2 is a type I membrane protein containing a single transmembrane region that separates a large extracellular from a small cytoplasmic domain; this structure is conserved in several fungal species [13]–[16] . Msb2 in the yeast Saccharomyces cerevisiae has been shown to be continuously cleaved by the Yps1 yapsin protease , releasing the extracellular domain into the growth medium [17] . This property , coupled with the high level of N- and O-glycosylation of the extracellular domain has led to the concept that fungal Msb2 proteins represent functional analogs of the mammalian MUC1/2 signaling mucins , which by proteolytic cleavage generate highly hydrated mucous glycoprotein layers around cells and at the same time confer transcriptional regulation by the cleaved cytoplasmic domain [18] . In fungi , intertwining of Msb2 hydrated glycostructures with cell wall components may be related to the sensing function of Msb2 . Cleavage of the ScMsb2 cytoplasmic domain has not been reported and its presence may be required for Cdc42 binding , which is an essential upstream element of the Kss1 MAPK pathway [13] . Here we report that the glycosylated extracellular domain of C . albicans Msb2 is released into the growth medium in considerable amounts and we show that the shed protein has the function to protect against AMPs produced by the host . In humans , the most prominent AMPs exhibiting strong antimicrobial and immunostimulatory activities are the histatins , which are produced by salivary glands and secreted into saliva and the cathelicidins and defensins , which are produced by neutrophils and macrophages ( for a review , see [19]–[21] ) . The human cathelicidin LL-37 occurs on mucosal surfaces at a concentration of 2–5 µg/ml but its concentration rises to 1 . 5 mg/ml in acute inflammation [22] . Histatin-5 and LL-37 are cationic AMPs that damage the cytoplasmic membranes of C . albicans [23]–[25] and histatin-5 also attacks intracellular targets [26] . The combined findings of this study suggest that shed Msb2 is a glycoprotein that effectively protects C . albicans against killing by AMPs LL-37 and histatin-5 , allowing C . albicans to evade immune reactions and to allow its persistence as a commensal .
To immunologically detect Msb2 we constructed a strain producing a variant Msb2 protein carrying an HA-epitope within the large extracellular domain and in addition a V5-epitope in the middle of the short cytoplasmic domain ( Figure 1 A ) . MSB2 was expressed either under the control of the constitutive ACT1 promoter when plasmid pES11a was integrated in the LEU2 locus ( strain ESCa3 ) or by the authentic MSB2 promoter when pES11a was integrated in the partially deleted msb2Δ1 allele of strain FCCa28 ( strain ESCa10 ) . The msb2Δ1 allele encoding 406 N-terminal residues of Msb2 was found to be completely non-functional in all phenotypic assays ( see below ) and it was fully complemented in transformants containing pES11a integrated in both genomic loci; complementation efficiencies were equal between transformants carrying singly HA-tagged or doubly HA-V5-tagged Msb2 versions . Thus , while several msb2Δ1 mutant strains were as supersensitive to caspofungin and tunicamycin as the pmt4 control strain [10] complementation by the epitope-tagged versions of Msb2 restored normal resistance ( Figure 1 B ) . While tunicamycin-supersensitivity indicates that msb2Δ1 mutants require intact N-glycosylation for growth , O-mannosylation by Pmt1 appears not relevant since mutants grew normally in the presence of the Pmt1 inhibitor . The tagged versions of Msb2 were also fully active to reverse the hyphal growth defects of the msb2Δ1 mutants [9] ( Figure 1 C ) . In addition , we constructed plasmid pES11c , which encodes the HA-tagged Msb2 variant carrying the V5 epitope at its C-terminal end ( allele MSB2HA-V5 end ) . The phenotypic results for pES11a- and pES11c-transformants were identical ( data not shown ) . Release of a Msb2 subfragment into the growth medium has been observed in S . cerevisiae and other fungi [13]–[16] . When we examined cells and growth medium of C . albicans transformants producing tagged Msb2 by immunoblotting we discovered that the majority of HA-carrying Msb2 was present in the medium and migrated as a diffuse band of >460 kDa ( Figure 2 A ) . No significant difference regarding the amount of immunoreactive protein was detected in strains either transcribing MSB2 from the ACT1 or MSB2 promoters ( compare lanes 3 and 5 ) suggesting that both promoters are of comparable strength . As expected , the tagged ER-membrane protein Pmt1HA was associated only with cells ( lane 2 ) . In contrast to HA immunodetection the V5-tagged Msb2 protein was found exclusively in association with cells and not in the medium , similar to the Pmt2V5 control protein ( Figure 2 B ) . The V5-tagged Msb2 protein migrated as a doublet of about 15 kDa and thus corresponded in size to the cytoplasmic domain of Msb2 . Thus , it appears that during growth in liquid culture the Msb2 full-length protein is mostly cleaved proteolytically into the large extracellular ( HA-tagged ) and the small cytoplasmic ( V5-tagged ) subfragments . Importantly , release of the Msb2HA fragment was almost quantitative during growth in complex YPD growth medium and was not altered significantly in YEPG medium containing galactose as in S . cerevisiae [17] or during hypha formation in YP medium containing 10% serum ( data not shown ) . The released extracellular fragment or Msb2 will now be referred to as Msb2* . To examine if Msb2* secretion would also occur during growth on a semisolid agar surface we used a double sandwich system consisting of a PVDF membrane used for immunoblotting topped by a membrane filter precluding the passage of cells , which were both placed on YPD agar ( Figure 2 C , a ) . Cells grew on the membrane filter ( Figure 2 C , b ) and immunoanalysis of the PVDF filter detected HA-proteins only released from cells producing Msb2* ( Figure 2 C , c 3 , 4 ) but not from cell producing tagged Pmt1HA protein . This result indicates that the extracellular Msb2 fragment is also detected in surface growth of C . albicans . Considering the possibility that Msb2 is cleaved immediately upstream of the transmembrane region it was expected that Msb2* has an approximate molecular mass of 131 kDa but the heterogeneity and apparent molecular mass in immunoblotting ( Figure 2 A ) suggested extensive glycosylation . To estimate its molecular mass more accurately we carried out fractionation of culture fluid containing Msb2* by gel filtration , using a column previously calibrated with standard proteins ( Figure 2 D , a , b ) . Fractions eluted from the column were examined by immunodetection and yielded a major peak from 468–614 kDa ( Figure 2 D , c ) in agreement with the above immunoblotting results . A minor peak in the void volume , presumably representing aggregated Msb2* of >1000 kDa , was also detected . Since this result suggested that glycosylation contributed equally to the mass of Msb2* as its protein content we attempted to clarify the type of protein glycosylation . Extensive treatment of the growth medium ( and of purified Msb2* , see below ) with PNGase F did not result in a significant alteration of its apparent molecular mass ( data not shown ) , while β-elimination led to a mass reduction to about 300 kDa ( Figure 2 E , a ) indicating that Msb2* is significantly O-but not N-glycosylated . On the other hand , complete chemical deglycosylation by trifluoromethanesulfonic acid ( TFMS ) reduced the mass of Msb2* to about 117–130 kDa ( Figure 2 E , b ) consistent with the proteolytic cleavage of the Msb2 precursor protein immediately upstream of the transmembrane region ( expected molecular mass of unmodified 1291 residue fragment is 130 kDa ) . It is yet unclear if the different deglycosylation results obtained for β-elimination and TFMS treatments is due to residual O-glycosylation not removable by β-elimination , by residual N-glycosylation , which is not removed by PNGase F or by yet unknown modifications . However , because clear evidence for O-glycosylation of secreted Msb2 was obtained we produced epitope-tagged Msb2 in C . albicans mutants lacking each of the 5 isoforms of protein-O-mannosyltransferases . Immunoanalysis of secreted Msb2* showed faster electrophoretic mobility in the pmt1 mutant , while in the pmt4 , pmt5 and pmt6 homozygous mutants no difference to the control strain was detected ( Figure 2 E , c ) . We conclude that Pmt1 is at least partially involved in Msb2 O-glycosylation , although the contribution of Pmt2 ( only testable in a heterozygous PMT2/pmt2 strain since it is essential for growth [27] ) cannot be excluded . Compensatory upregulation of other Pmt isoforms in a pmt1 mutant [10] , [28] may also account for remaining Msb2 O-glycosylation , which showed a very broad mobility distribution corresponding to apparent molecular masses from 240–480 kDa . It has been reported that in S . cerevisiae the yapsin-type protease Yps1 is responsible for cleavage and secretion of Msb2 [17] . In C . albicans the closest homolog to Yps1 is Sap9 ( 21 . 9% identity ) , while Sap10 is also structurally similar because it is GPI-anchored in the cytoplasmic membrane [29] . When we expressed the tagged MSB2HA-V5 allele in the sap9 mutant ( ESCa33 ) , the sap10 mutant ( ESCa34 ) or the sap9 sap10 double mutant ( ESCa35 ) we did not observe any difference in amounts and molecular masses of Msb2* ( data not shown ) . We also observed normal secretion of Msb2 in a mutant ( ESCa36 ) lacking the furin-type and Golgi-resident Kex2 serine endoproteinase , which in S . cerevisiae is required for cleavage and shedding of the Flo11 protein [30] . Furthermore , we repeatedly added high concentrations ( 15 µg/ml ) of the aspartyl protease inhibitor pepstatin , of the metalloprotease inhibitor amastatin ( 15 µg/ml ) or of a commercial mix of inhibitors for serine- and cysteine proteases ( complete mini tablets; Roche ) to growing cultures of ESCa3 but we did not find any effect on Msb2* release ( data not shown ) . We conclude that the processing mechanism of Msb2 in C . albicans requires an as yet unidentified protease and that Sap9 , Sap10 and Kex2 proteases are not involved . We constructed several C . albicans strains producing deleted Msb2 variants under the control of the ACT1 promoter in a msb2 mutant background and tested Msb2-dependent phenotypes including secretion of Msb2 , hypha formation and resistance to caspofungin; furthermore , the ability of variants to activate the Cek1 MAP kinase was examined . The results are summarized in Figure 3 A and presented in Figure 3 B and Figure S1 . Two major deletion variants either lacking 449 residues of the extracellular domain ( Msb2-ΔN ) or lacking the complete cytoplasmic tail of 103 residues ( Msb2-ΔC ) were fully able to complement all msb2 mutant phenotypes . In contrast , strains only producing the N-terminal region of Msb2 up to the transmembrane region ( variant Msb2-ΔTM-C ) or solely the 108 cytoplasmic variant Msb2 tail residues were as defective for Msb2 phenotypes as mutants REP18 carrying a complete deletion of the MSB2 ORF or strain FCCa27 only producing N-terminal residues 1–406 of Msb2 ( Msb2-Δ1 ) . Inactivity of the Msb2-ΔTM-C variant was not caused by lack of protein biosynthesis since amounts of Msb2* released into the medium were comparable for all HA-tagged variants ( Figure S1 ) . However , with regard to the activation of Cek1 a particular phenotype of these deletion variants was observed . The wild-type strain ESCa3 showed low levels of phosphorylation in stationary phase and phosphorylation was increased during logarithmic growth , which was stimulated further in the presence of tunicamycin [9] ( Figure 3 B ) . In contrast , strain ESCa25 producing the Msb2-ΔN variant activated Cek1 not only in stationary phase but also in the absence of tunicamycin to high levels . In addition , strain ESCa38 carrying the Msb2-ΔC variant was impaired in its ability to activate Cek1 in response to tunicamycin . Strains producing the Msb2-ΔTM-C and the Msb2-tail were completely unable to activate Cek1 phosphorylation . Thus , it appears that the Msb2 N-terminal , transmembrane and cytoplasmic domains region convey different functions in Cek1 phosphorylation . C . albicans ESCa3 expressing ACT1p-MSB2HA-V5 released considerable amounts of the Msb2* glycoprotein into the complex YPD growth medium , amounting to 76 µg/ml and 150 µg/ml in logarithmic growth ( OD600 = 1 ) and in stationary phase ( OD600 = 6 ) . Msb2* was quantitated immunologically by a dot-blot procedure , because its high glycosylation status prevented quantitation by standard methods . We considered that this glycoprotein could contribute to defense against immunological responses of the human host , in particular to the attack by AMPs [20] . To verify this concept we first tested if the presence of Msb2 would contribute to basal levels of AMP resistance of C . albicans . Wild-type strains were significantly more LL-37-resistant than msb2 mutants ( Figure 4 A ) . Sensitivity of a msb2 sho1 double mutant was only slightly increased compared to a msb2Δ1 single mutant and a sho1 single mutant showed wild-type resistance indicating that Msb2 but not Sho1 mediates LL-37 resistance . The increased LL-37 sensitivity of msb2 mutant strains versus a wild-type strain was also correlated with increased fluorescent staining of mutant cells [26] , [27] by TAMRA-labelled LL-37 ( Figure 4 B ) . We also observed that in the presence of LL-37 the msb2 mutant tended to aggregate more readily than wild-type cells [31] . We next tested the LL-37 sensitivity of the above series of transformants producing truncated Msb2 variants . Interestingly , while the transformant only synthesizing the C-terminal tail of Msb2 was as sensitive as the msb2Δ1 mutant all other transformants showed wild-type sensitivity ( Figure 4 C ) . Even the transformant producing Msb2 deleted for its transmembrane region and C-tail was not supersensitive , although as described above this Msb2 variant was inactive in complementing msb2 mutant phenotypes ( Figure 3 ) . It was concluded that the basal resistance of C . albicans to LL-37 depended on the secreted extracellular domain of Msb2 but its N-terminal domain was not required for this action . Since full-length and N-terminally deleted Msb2* are O-glycosylated to a large part by Pmt1 ( Figure 2 ) transformants were constructed producing doubly tagged Msb2 in C . albicans strains defective in each of the 5 Pmt proteins ( a heterozygous strain was used in case of PMT2 because of its essentiality for growth ) . Among these transformants only the pmt1 mutant was LL-37 supersensitive supporting the notion that Pmt1-directed O-glycosylation of Msb2* is required to provide resistance to LL-37 . In conclusion , these results suggest that the secreted extracellular Msb2* domain is required for LL-37 basal resistance of C . albicans . Several mechanisms are possible to explain the requirements of Msb2 ( and Sho1 ) for LL-37 resistance and one mechanism is inactivation of LL-37 by the secreted Msb2* . To verify this concept we first purified Msb2* fragment from the growth medium by affinity chromatography using anti-HA antibody and verified that the purified material consisted solely of the heterogeneous >460 kDa protein by silver staining and immunoblotting ( Figure 5 A ) . Next we asked if the purified Msb2* would proteolytically attack cathelicidin LL-37 . Msb2* and AMPs were co-incubated and then assayed AMPs on a 18% SDS-PAGE gel ( which excludes Msb2* ) . Msb2* co-incubation did not diminish amounts of LL-37 and no degradation products were observed ( Figure 5 B ) even if a 22 . 5% SDS-PAGE gel was used ( data not shown ) . Furthermore , long term incubations ( 16 h ) of Msb2* preparations with substrates of a protease detection kit able to detect a wide variety of protease did not detect any protease activity ( data not shown ) . Therefore , it was concluded that Msb2* preparations had no general proteolytic activity . In additional pre-tests we bound Msb2* ( or Msb2-ΔN* ) to wells of microtiter dishes and checked if TAMRA-labelled LL-37 would absorb to these wells . Msb2* coating did indeed stimulate binding of LL-37-TAMRA significantly , while preincubation with unlabelled LL-37 reduced subsequent binding ( Figure 5 C ) . This result indicates that LL-37 has a specific binding site on Msb2* . To test a potential function of Msb2* in AMP protection we set up an AMP activity assay , in which we treated C . albicans for 1 . 5 h with AMPs in the absence or presence of purified Msb2* and then assessed fungal viability by determination of colony-forming units ( CFU ) . The results show that added Msb2* rescued C . albicans from LL-37 killing , which was obvious for the wild-type strain and even more significant for msb2 and msb2 sho1 mutants; even an E . coli strain was protected against LL-37 by Msb2* ( Figure 5 D ) . Interestingly , even the shortened Msb2*-ΔN fragment secreted and purified from strain ESCa25 was able to provide protection , although a concentration dependence of its activity revealed that it is slightly less active in AMP inactivation compared to the full-length Msb2* protein ( Figure 5 E ) . AMP inactivating activity was also detected by merely using medium ( secretome ) of a C . albicans wild-type strain ( CAF2-1 ) for co-incubation with LL-37 ( Figure 5 F ) . As expected , medium of the msb2Δ1 strain ( FCCa27 ) had no protective effect , while medium of the pmt1 mutant ( SPCa2 ) had reduced inactivating activity . These findings demonstrate that the extracellular Msb2 domain has an additional function in C . albicans biology , e . g . in LL-37 defense , which is different from its roles in cell wall integrity and filamentation . C . albicans is known to be sensitive to low levels of histatin-5 [23]–[26] , [32] , [33] . We considered the possibility that higher Msb2* levels occurring in the vicinity of C . albicans colonies in the human host could protect against histatin-5 as we had found for LL-37 . Although we did not observe a significant higher sensitivity to histatin-5 in msb2 mutants ( as for LL-37 ) we found that added purified Msb2* did indeed protect C . albicans strains significantly against histatin-5 ( Figure 6 ) . As expected , HA peptide used for elution of Msb2* from the anti-HA antibody in affinity chromatography did not provide protection . The protective action of Msb2* was not restricted to C . albicans because even an E . coli strain was rescued from histatin-5 killing ( Figure 6 ) . Thus , we conclude that protection by the secreted Msb2 glycofragment is not specific for LL-37 but extends to other AMPs including histatin-5 and affects microorganisms other than C . albicans .
A complex interplay of responses and counter-responses characterizes the encounter of microbial pathogens with the human host . Opportunistic pathogens including C . albicans may be commensals , held in check by the immune system and supported by actions of the pathogen that favour a commensal life-style [1] , [34] . Conversely , immunological impairment or other conditions can favour propagation of pathogens and result in disease through microbial virulence traits and/or immune hyperstimulation causing autoimmune damage [35] Immune cells detect surface structures of C . albicans including glucan and mannoproteins and trigger IL-17-dependent reactions [2] , [3] including the production of AMPs , which kill the pathogen and attract immune cells [19] , [20] . The C . albicans protein Msb2 has a dual function to stabilize the fungal cell wall and we show here that it is also required to block an important aspect of the immune response by inactivating AMPs ( Figure 7 ) . Fungal pathogens have a relatively high ability to resist attack by hydrolytic enzymes or small toxic molecules including antifungals in the human host . Cell wall damage is restored or compensated for by signaling pathways that sense the defect and initiate appropriate rescue responses [6] . In C . albicans defects in glucan or chitin are sensed especially by pathways containing the Mkc1 or Hog1 MAP kinases that trigger enhanced glucan or chitin biosynthesis [7] , [36] . Defects in protein glycosylation are transmitted mainly via the Cek1 MAP kinase pathway and lead to activation of individual isoforms of protein-O-mannosyltransferases [9] , [10] . Blockage of N-glycosylation by tunicamycin depends on Cek1 and upregulates PMT1 transcription , while inhibition of Pmt1-O-glycosylation stimulates transcription of PMT2 and PMT4 genes . Interestingly , we found that the Msb2 membrane sensor protein functioning at the head of the Cek1 pathway is itself a highly glycosylated protein as in other fungal species . Despite the presence of 5 potential acceptor sites no evidence for N-glycosylation of Msb2 was obtained but the secreted Msb2 migrated faster in a pmt1 mutant ( not in other homozygous pmt mutants ) indicating that Pmt1 is partially responsible for Msb2 O-mannosylation . Residual O-chains in a pmt1 strain were removed by chemical treatment suggesting that they are contributed by the Pmt2 isoform , which is essential for growth [27] . Lack of Pmt1 glycosylation was previously shown to increase phosphorylation of Cek1 and to activate PMT2/4 transcription [9] , [10] and we add here that lack of the N-terminal Msb2 glycodomain leads to constitutive Cek1 phosphorylation . Conceptually , lack of Msb2 O-glycosylation could trigger Cek1 phosphorylation but other O-glycosylated proteins interacting with Msb2 could also provide the triggering signal . Signaling by proteins interacting with Msb2 is suggested by the finding that tunicamycin-treatment induces Cek1 phosphorylation , although Msb2 does not appear to be N-glycosylated itself . In S . cerevisiae , however , Msb2 is N-glycosylated and O-mannosylated by the Pmt1 , 2 and 4 isoforms; furthermore , activation of the Cek1 homolog Kss1 occurred only in cells lacking Pmt4 and inhibited for N-glycosylation by tunicamycin [37] , [38] . Thus , Msb2 glycosylation and resulting MAP kinase activation proceed differently in C . albicans and S . cerevisiae . The single transmembrane region of Msb2 divides the protein in a large glycosylated extracellular and a small cytoplasmic domain in C . albicans , S . cerevisiae and other fungi . A S . cerevisiae Msb2-GFP fusion has been shown to get efficiently cleaved leading to release of the extracellular domain into the medium [17] . This processing occurs at a yet undefined site and requires the Yps1 yapsin-type protease suggesting that it is directly or indirectly involved in the cleavage . Similarly , using doubly epitope-tagged Msb2 we found that in C . albicans Msb2 is cleaved almost quantitatively , which sheds the extracellular domain into the medium and retains the cytoplasmic domain in the cells . However , in C . albicans the closest homologs of ScYps1 , Sap9 , Sap10 [29] , and serine endoproteinase Kex2 [30] were not required for CaMsb2 processing . Cleavage/release was found to occur both in liquid and on surfaces and the amount of secreted Msb2 depended on the number of growing C . albicans cells . Thus , importantly , the level of released Msb2 is a measure of C . albicans propagation . In agreement , Msb2 peptides were recently identified in the secretome of C . albicans yeast and hyphal cultures; peptides corresponded to the extracellular domain including residue 1290 upstream of the transmembrane region [39] . The relationship between Msb2 structure , processing/secretion and Cek1 phosphorylation was studied using C . albicans strains producing Msb2 variants . A large deletion of 450 N-terminal residues adjacent to the signal sequence ( Msb2-ΔN ) led to functional Msb2 able to complement defects of the msb2 mutant; this variant differed from the native protein , however , in that the Cek1 MAP kinase was constitutively phosphorylated . In agreement , S . cerevisiae Msb2 deletions of the extracellular domain have been found to hyperactivate the dedicated MAP kinase Kss1 [17] . Different phenotypes were obtained for C-terminal deletions of C . albicans Msb2 . While a Msb2 variant deleted for its C-terminal end and the transmembrane region ( Msb2-ΔTM-C ) was completely inactive , a deletion retaining the transmembrane region ( Msb2-ΔC ) was fully functional in complementing msb2 phenotypes . Unexpectedly , however , the latter variant did not respond to tunicamycin-treatment by induction of Cek1 phosphorylation , in agreement with results obtained for a similar S . cerevisiae Msb2 variant [38] . We conclude that the transmembrane region of Msb2 is absolutely required for Msb2 functions and furthermore , that tunicamycin-regulated signaling to the Cek1 MAP kinase requires the cytoplasmic domain . Conceivably , the cytoplasmic domain could be directly involved in regulation of Cek1 kinase activity or it could participate in gene regulation as has been reported for signaling mucins and the Notch protein in higher eukaryotes [18] , . In the human host C . albicans contacts surfaces of body cells including immune cells , which may phagocytose the pathogen and elicit a wave of antifungal activities . Resident or induced soluble defense molecules such as immunoglobulins , complement factors and AMPs kill or block the growth of the pathogen . AMPs have a wide range of antiviral , antibacterial and antifungal activities and provide an antimicrobial barrier on mucosal surfaces such as histatins produced and secreted by salivary glands or they are components of the antimicrobial armory of neutrophils that produce cathelicidins ( LL-37 ) and defensins [20] . Furthermore , AMPs act as chemoattractants recruiting leukocytes to sites of infection [19] , [21] . C . albicans is known to be sensitive to histatins , LL-37 and defensins , which inhibit fungal growth by cytoplasmic membrane disruption , interference with mitochondrial activity or yet undefined mechanisms [23]–[26] . Furthermore , binding of LL-37 or histatins to cell wall carbohydrates prevents adhesion of C . albicans to host cells and plastic surfaces [31] . It should be noted also that bacterially-produced AMPs such as the lantibiotic nisin secreted by Lactobacillus lactis contribute to the diversity and high concentration of AMPs in the human body [41] . Nevertheless , a myriad of microbial commensals including some opportunistic pathogens persist as cohabitants because they are at least partially AMP-resistant . Several AMP-resistance mechanisms have been reported . Cleavage of AMPs by soluble or membrane-bound proteases has been described for many bacterial species and it has been shown that C . albicans is also able to cleave histatin-5 by the yapsin-type protease Sap9 [42] , [43] . Another evasion mechanism known in bacteria is the secretion of AMP-binding proteins that act as decoys deflecting AMPs from their dedicated action at microbial cell surfaces . Examples include the secreted SIC , staphylokinase and FAF proteins by Streptococcus pyogenes , Staphylococcus aureus and the commensal Finegoldia magna , respectively [44]–[46] . Here we describe that an analogous mechanism is relevant also for fungal pathogens since shedding of a large glycosylated fragment of the Msb2 sensor protein renders C . albicans AMP-resistant . Msb2 shedding reached high levels during liquid growth ( about 150 µg/ml in stationary phase ) and was also observed during surface growth . Purified Msb2 fragment effectively blocked the fungicidal activity of histatin-5 and LL-37 even at a >20 fold molar excess of AMPs suggesting multiple binding sites . Interestingly , a C . albicans msb2 mutant was supersensitive to LL-37 but not to histatin-5 suggesting that the relatively small amount of cell-associated Msb2 suffices to protect against LL-37 but not against histatin-5 . This finding agrees with the recent finding that LL-37 but not histatin-5 binds to C . albicans cell-wall carbohydrates [31] . The underlying molecular mechanisms for AMP binding to Msb2* remain to be determined . We found that the Pmt1-type of O-mannosylation is partially required for Msb2 glycosylation , its binding to LL-37 and for LL-37 resistance of wild-type cells , which raises the question if the glycostructures of Msb2* directly or indirectly affect LL-37 binding . Previous work has established the binding of LL-37 to various glycostructures including bacterial lipopolysaccaride [47] , bacterial exopolysaccharides [48] , human glycosaminoglycans [49] and fungal cell-wall polysaccharides [31] . These glycostructures may provide anionic contact sites for cationic AMPs such as LL-37 and histatin-5 , which are enriched for basic amino acids ( net charge +6 and , respectively , +12 at physiological pH ) . Since O-mannosyl side chains of Msb2* do not add net charge ( unless they carry as yet undefined modifications ) they do not allow ionic interactions with cationic AMPs , although non-ionic interactions cannot be excluded . Possibly , the functional role of O-mannosylation is indirect by providing an extended , bottle-brush conformation of the protein , as it is often observed in highly O-glycosylated protein domains [50]; this conformation could help to expose carboxylate side groups of aspartate and glutamate residues in Msb2* that could interact with basic residues of AMPs . Other C . albicans components including members of the Hog1 MAP kinase pathway are also involved in basal AMP resistance [51]; since Msb2 is not an upstream element in the Hog1 pathway of C . albicans [52] it probably regulates AMP resistance independently of Hog1 . In a process that is analogous to functions of Msb2 , the Pra1 protein of C . albicans is partially shed and impairs immune responses , in this case by binding of human factor H in solution leading to downregulation of the complement system in the vicinity of fungal cells [53] . We reported previously that in the standard mouse model of systemic infection ( tail vein injection ) no significant attenuation of virulence was detected for a msb2 mutant [9] . However , the systemic infection model may not appropriately reflect growth of C . albicans in the form of biofilms or foci of infection within organs , which are expected to be surrounded by a diffusion cloud of shed Msb2 at high levels that cause quorum resistance depending on fungal cell numbers . Shedding of Msb2 may also be important for C . albicans commensal growth , e . g . survival in the gut , where it is confronted with AMPs of other microbial commensals such as nisin produced by Lactobacillus [41] . On the other hand , shed Msb2 is able to provide cross-protection for other species as we have shown for protection of E . coli against LL-37 and histatin-5 . Therefore , we propose that novel models for virulence and commensalism are needed to test the biological relevance of Msb2 and its shedding . Shed Msb2 may be of diagnostic value since its levels reflect fungal growth in the human host . Shed Msb2 is highly soluble and proteolytically stable because of its extensive glycosyl modifications and its presence in body fluids may be indicative of hidden localized fungal infections .
C . albicans strains are listed in Table 1 . In C . albicans strain REP18 the MSB2 ORF of both alleles is completely removed [9]; this msb2 mutant allele is referred to as msb2Δ0 . Strain FCCa27/28 contains partially deleted alleles designated msb2Δ1 ( encoding the 406 N-terminal residues of Msb2 ) , which were constructed using the URA-blaster method . A 3 . 8 kb genomic fragment encompassing MSB2 was PCR-amplified using primers IPF6003-NotI and IPF6003-SacII and cloned into pUK21 ( NotI , SacII ) . The large BamHI-KpnI fragment of the resulting plasmid was ligated to the hisG-URA3-hisG blaster cassette of p5921 to generate pUK-6003 . ko . Urab . The NotI-SacII disruption cassette of this plasmid was used according to the standard URA blaster protocol to partially delete both MSB2 alleles in C . albicans CAI4 generating FCCa27 ( Ura+ ) and FCCa28 ( Ura− ) . Strain FCCa28 allows integration of MSB2 expression vectors in the MSB2 locus by transformation with HpaI-cleaved plasmid and ectopically in LEU2 after digestion with EcoRV , which place MSB2 alleles under transcriptional control of the MSB2 and ACT1 promoter , respectively . The disruption was verified by colony PCR using primers IPF6003-3verif/ i-p2-Ura3ver and by Southern blottings ( data not shown ) . E . coli strain DH5αF′ was used for plasmid constructions and for AMP protection experiments . Strains were grown on/in standard YPD or SD media . Pmt1-inhibitor OGT2599 was resuspended in DMSO to prepare a stock solution of 10 mM [54] . Standard drop dilution tests ( 10 fold dilutions to 10−5 ) were used to determine sensitivity to inhibitors . Hyphal formation was induced by growth at 37°C on YPM medium containing 2% mannitol as sole carbon source or in liquid YP medium containing 10% serum [27] . Relevant restriction site used for the construction of MSB2 variant alleles are shown in Figure 1A . A MSB2 allele encoding heme agglutinin ( HA ) -tagged Msb2 was constructed by first PCR-amplifying the 5′-end of the MSB2 coding region using primers Msb2-ATG-XhoI and IPF6003-3′ ( all oligonucleotides are listed in Table S1 ) . The PCR fragment contained a novel XhoI site upstream of the ATG and extended to bp position 3227 of the ORF , 50 bp downstream of the PstI site . The XhoI-PstI subclone in pUC21 was mutagenized using the Quikchange kit ( Stratagene ) and primers HA-hin and HA-her were used to insert the sequence encoding a single HA epitope ( 11 amino acids ) 1500 bp downstream of the ATG start codon sequence . The 3′-end of the MSB2 ORF was then amplified by genomic PCR using primers Msb2-int2 und Msb2-Stopp-XhoI-NotI , which generated a fragment containing a MSB2 sequence from 61 bp upstream of the PstI site to the XhoI site downstream of the stop codon sequence that was generated in the PCR reaction . This 3′ PCR fragment was mixed with the above 5′ XhoI-PstI fragment and the full-length modified MSB2 allele was generated by overlap PCR using the flanking primers Msb2-ATG-XhoI und Msb2-Stopp-XhoI-NotI . The resulting XhoI fragment was cloned downstream of the ACT1 promoter in C . albicans expression vector pDS1044-1 to generate plasmid pES10 . To insert the V5 epitope-encoding sequence into MSB2 a 1037-bp region from upstream of the PstI site to the middle of cytoplasmic domain sequence was PCR amplified using pES10 as template and primers PCR1 Hin und PCR1 Mitte Her , the latter primer added V5 sequences to the PCR product . In addition , a second PCR fragment ( 712 bp ) was generated by PCR using primers PCR2 Mitte Hin ( containing the V5 sequence ) und PCR2 Her ( downstream of the ApaI site in the 3′-UTR ) . Because both fragments contained the V5 sequence an overlap PCR using flanking primers PCR1 Hin und PCR2 Her generated a 1695 bp PCR fragment that was cut with NheI and ApaI and then inserted into pES10 to replace the corresponding unmodified fragment . The resulting expression plasmid encoding the MSB2HA-V5 allele was designated pES11a . In a similar approach , an expression vector encoding a Msb2 variant carrying the V5 epitope at the C-terminal end of Msb2 was constructed using primers PCR1 Hin , PCR1 Ende Her , PCR2 Ende Hin and PCR2 Her; the resulting plasmid was designated pES11c ( MSB2HA-V5 end ) . Expression vectors encoding Msb2 variants were constructed by primer-directed mutagenesis of plasmid pES11a , using the Quikchange kit ( Stratagene ) . Plasmid pES14 encoding Msb2-ΔN lacking residues 33–481 of Msb2 was constructed using primers Cla1 Del1 next1/-2 , plasmid ES16 encoding the Msb2-ΔC variant lacking the cytoplasmic tail of Msb2 was constructed using oligonucleotides MSB2 Stopp nach TM Hin/-Her and plasmid ES17 encoding the Msb2-ΔTM-C variant lacking transmembrane region and cytoplasmic tail was constructed using oligonucleotides MSB2 Stopp vor TM Hin/-Her . Plasmid ES15 encoding the Msb2-tail variant was constructed by PCR-amplification of sequences encoding the cytoplasmic tail by primers C-Tail vor/-rück and inserting it into downstream of the PCK1 promoter in plasmid pBI-1 . Plasmids were integrated into the LEU2 locus of strain FCCa28 as described above . Strains were grown in 50 ml YPD or SD medium at 30°C to OD600 = 6–10 and cells were harvested by centrifugation . Cells were washed with water and resuspended in lysis buffer ( 50 mM HEPES/pH 7 . 5; 150 mM NaCl; 5 mM EDTA; 1% Triton X-100 ) containing protease inhibitors ( Complete , Mini , Roche ) . Cells were broken by shaking with glass beads at 4°C for 2×10 min on a vibrax ( Janke & Kunkel , 2200 rpm ) or with a FastPrep homogenizer ( MP Biochemicals ) . Cell debris and glass beads were separated from the crude cell extract by centrifugation . For immunoblottings proteins were separated by SDS-PAGE ( 8% , 18% or 4–20% acrylamide ) and blotted to PVDF membranes . Protein standards used were the PageRuler set ( Fermentas; 11–170 kDa ) or the HiMark set ( Invitrogen; 31–460 kDa ) of proteins . Membranes were probed using rat anti-HA monoclonal antibody ( 1∶2000; Roche ) or mouse monoclonal anti-V5 antibody ( 1∶2000; Serotec ) and visualized using peroxidase-coupled goat anti-rat or anti-mouse antibodies ( 1∶10000; Thermo ) and the SuperSignal West Dura chemiluminescent substrate ( Pierce ) . Gel filtration chromatography was done on a Superdex 200 10/300 GL column ( GE healthcare ) equilibrated with SD medium . Elution characteristics were established using a set of standard proteins ( Sigma ) containing carboanhydrase ( 23 kDa ) , BSA ( 66 kDa ) , ADH ( 150 kDa ) , β-amylase ( 200 kDa ) , apoferritin ( 434 kDa ) and thyroglobulin ( 669 kDa ) ; the void volume ( V0 ) was determined using Blue dextran ( 2000 kDa ) . Protein elution volumes ( Ve ) were monitored at 280 nm and fractions were collected by an ÄKTA prime plus ( GE Healthcare ) at a flow speed of 0 . 4 ml/min . To determine the molecular mass of secreted Msb2 , strain ESCa3 ( Msb2HA-V5 ) was grown in SD medium to OD600 = 10 . Cells were removed by centrifugation and 500 µl of the medium was degassed , sterile-filtered and applied to the Superdex column . 200 µl fractions were collected and 20 µl per fraction were tested for the presence of Msb2HA by immunoblotting . The approximate molecular mass of Msb2HA was calculated from the standard protein graph using the equation y = 62258e−3 , 695x ( x: Ve/Vo; y: molecular mass ) . Deglycosylation reactions using PNGase F and α-mannosidase ( jack bean ) were carried out according to the instructions of the manufacturers ( Roche; Sigma ) . To remove O-glycosylation the GlycoProfile β-elimination kit ( Sigma ) was used , either without or with pretreatment of the sample at 80°C . 200 µl of the ESCa3 growth medium was acetone-precipitated and resuspended in the same volume of water . 40 µl of the reagent mixture was added and the sample was incubated over night at 4°C . The sample was neutralized with HCl and 20 µl were analyzed by immunoblotting . The GlycoProfile IV kit ( Sigma ) was used to remove all forms of protein glycosylation by trifluoromethanesulfonic acid ( TFMS ) . 1 . 5 ml of the growth medium of strain ESCa3 was lyophilized and 150 µl of TFMS was added and the proteins incubated at 4°C for 25 min . 4 µl of 0 . 2% bromophenol blue was added and neutralization by precooled pyridine ( added drop-wise ) was monitored by the yellowish coloring . This latter step was carried out in a bath of dry ice in ethanol . Reagents in the samples were removed by dialysis against PBS using Slide-A-Lyzer cassettes ( Thermo ) . The secreted Msb2HA domain was purified by affinity chromatography from cultures grown in SD medium containing 2% casamino acids to an OD600 = 10 using a column ( 1 ml ) containing agarose beads covalently coupled to 3 . 5 mg of monoclonal anti-HA high affinity antibody ( Roche ) . The column equilibrated with buffer ( 20 mM Tris/HCl , pH 7 . 5; 0 . 1 M NaCl; 0 . 1 mM EDTA ) and 50–400 ml of the culture medium containing Msb2HA were loaded and the column was washed with 20 bed volumes of wash buffer ( 20 mM TrisHCl/pH 7 . 5; 0 . 1 M NaCl; 0 . 1 mM EDTA; 0 , 05% Tween 20 ) . The Msb2HA protein was eluted twice by 1 ml ( 1 mg ) of HA peptide ( Roche ) in Tris-buffered saline . Proteins on SDS-PAGE gels were routinely visualized by Coomassie blue or silver staining and protein concentrations were determined by the Bradford assay using a commercial assay kit ( BioRad ) . Because of the high glycosylation status of Msb2* its concentration could not be determined reliably by any of these methods . Therefore , we developed a dot blot procedure , in which known molar concentrations of HA peptide were compared to Msb2* ( or Msb2-ΔN* ) signals resulting from reaction with the anti-HA antibody . Dilutions of a HA peptide solution ( Roche ) were spotted on an activated PDVF membrane and a dilution series of the sample containing unknown amounts of Msb2* was spotted alongside . The membrane was processed as for immunoblottings and the resulting signals were recorded using a Fujifilm LAS400 mini image analyzer and evaluated with the Fujifilm Multi Gauge program . The standard curve derived from the HA peptide were used to calculate molar amounts of the Msb2* sample . Msb2* samples were assayed for protease contamination using the Protease Detection Kit ( Jena Bioscience ) that detects a wide variety of proteases , including serine proteases , cysteine proteases and acid proteases . Substrate solution ( 50 µl ) and incubation buffer ( 50 µl ) were mixed with 100 µl ( 50 µg ) of Msb2* in TBS and incubated at 37°C for 16 h . 120 µl precipitation reagent was added and samples were incubated at 37°C for 30 min . Tubes were centrifuged at 12 . 000× g for 5 min and 50 µl of the supernatant was transferred to a flat bottom 96 well plate , 150 µl assay buffer was added and absorbance at 492 nm was measured using a plate spectrophotometer ( Biotek ) . Strains were grown over night to stationary phase in YPD medium and diluted into YPD medium to an OD600 = 0 . 1 . Cells were grown to OD600 = 0 . 8 at 37°C and incubated further for 1 h in the presence ( + ) or absence ( − ) of tunicamycin ( 2 µg/ml ) . Immunoblots were prepared as described previously verifying equal loading by Ponceau red staining of the membranes [9] . Blots were probed with anti-phospho-p44/42 MAP kinase ( Cell Signaling Technology ) to detect phosphorylated Cek1 protein and ScHog1 polyclonal antibody ( Santa Cruz Biotechnology ) was used to detect all forms of Hog1 [9] . Over night cultures of C . albicans and E . coli DH5αF′ were diluted and grown in YPD at 30°C to an OD600 = 0 . 3 . Cells were harvested by centrifugation and washed with and resuspended in PBS . Triplicate assays containing 5 µl cell suspension and 0–10 µg LL-37 ( Sigma ) or histatin-5 ( AnaSpec Inc . ) in a total volume 25 µl were incubated 1 . 5 h at 37°C , diluted 500 fold and plated on YPD . Colony forming units were determined after 2 d of growth at 30°C . The action of LL-37 on cells was visualized by fluorescence microscopy using LL-37-TAMRA ( Innovagen ) . To assay binding of LL-37 to Msb2* a microtiter plate assay was used . 10 µg Msb2* or Msb2-ΔN* in 200 µl PBS were allowed to bind wells of a 96 well flat bottom polystyrene plate over night at 4°C . The wells were washed three times with PBST ( PBS containing 0 . 05% Tween 20 ) . Then 200 µl of blocking buffer ( 5% w/v nonfat dry milk in PBST ) was added for 2 hours at room temperature . Wells were washed three times and incubated with 5 µg LL-37 5-TAMRA for one hour . After washing three times , the fluorescence was measured on a Tecan infinite 200 plate reader ( excitation 560 nm , emission wavelength 590 nm ) . In a competition experiment , following Msb2* binding , 3 µg LL-37 was added to wells and incubated for one hour before cells were washed and LL-37-TAMRA was added .
|
Microbial pathogens are attacked by antimicrobial peptides ( AMPs ) produced by the human host . AMPs kill pathogens and recruit immune cells to the site of infection . In defense , the human fungal pathogen Candida albicans continuously cleaves and secretes a glycoprotein fragment of the surface protein Msb2 , which protects against AMPs . The results suggest that shed Msb2 allows fungal colonies to persist and avoid inflammatory responses caused by AMPs . Msb2 shedding and its additional role in stabilizing the fungal cell wall may be considered as novel diagnostic tools and targets for antifungal action .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"infectious",
"diseases",
"biology",
"microbiology",
"fungal",
"diseases"
] |
2012
|
Msb2 Shedding Protects Candida albicans against Antimicrobial Peptides
|
Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes . Previously , we showed that food intake has a large impact on blood gene expression patterns and that these responses , either in terms of gene expression level or gene-gene connectivity , are strongly associated with metabolic diseases . In this study , we explored which genes drive the changes of gene expression patterns in response to time and food intake . We applied the Granger causality test and the dynamic Bayesian network to gene expression data generated from blood samples collected at multiple time points during the course of a day . The simulation result shows that combining many short time series together is as powerful to infer Granger causality as using a single long time series . Using the Granger causality test , we identified genes that were supported as the most likely causal candidates for the coordinated temporal changes in the network . These results show that PER1 is a key regulator of the blood transcriptional network , in which multiple biological processes are under circadian rhythm regulation . The fasted and fed dynamic Bayesian networks showed that over 72% of dynamic connections are self links . Finally , we show that different processes such as inflammation and lipid metabolism , which are disconnected in the static network , become dynamically linked in response to food intake , which would suggest that increasing nutritional load leads to coordinate regulation of these biological processes . In conclusion , our results suggest that food intake has a profound impact on the dynamic co-regulation of multiple biological processes , such as metabolism , immune response , apoptosis and circadian rhythm . The results could have broader implications for the design of studies of disease association and drug response in clinical trials .
Elucidating networks that define biological pathways underlying complex biological processes is an important goal of systems biology . Large-scale molecular profiling technologies have enabled measurements of mRNA and protein expression on the scale of whole genomes . As a result , understanding the relationships between genes and clinical traits , and inferring gene networks that better define biochemical pathways that drive biological processes , has become a major challenge to understanding large-scale data sets generated from these technologies . For the majority of published gene expression profiling experiments , they are carried out at a single pre-defined time point across all samples , where the implicit assumption is that the steady state for the corresponding biological system is well approximated at a single time point . The steady state in this context represents a baseline state of the system under study in which the system is least likely to change and has the least amount of variability due to environment . Because biological pathways and the complex behaviors they induce are dynamic [1] , transcriptional response , interactions among proteins and other such processes , take time and ultimately lead to time-dependent variations in mRNA , protein and metabolite levels . These types of temporal variation over time are difficult to study directly with measurements taken at only a single time point . Recently , studies applying time series to temporal gene expression data have been published , covering a range of experiments focusing for instance on the SOS DNA repair system in E . coli [2] , the cell cycle in yeast [3] , muscle development in Drosophila [4] and cell cycle processes in human cell lines [5]–[6] . Coexpression networks are based on pair-wise gene-gene correlations of expression data , revealing functional modules in the network that elucidate pathways that drive core biological processes [7]–[8] or pathways that underlie complex human disease [9]–[10] . Coexpression networks provide global views of network structures , but by themselves cannot yield causal relationship between genes or between genes and clinical traits . Using a Bayesian network approach to integrate genetic , expression , and clinical data in segregating populations , we have previously demonstrated that such causal relationships can be inferred [11]–[14] . While these network approaches have proven useful in elucidating complex traits emerging in complex systems at the population level , they have however been based on data sampled at a single time point . A static Bayesian network ( SBN ) is a graphical model that encodes a joint probability distribution on a set of stochastic variables , which can be decomposed as , where represents the parent set of . Similar to a static Bayesian network , a dynamic Bayesian Network ( DBN ) is also a graphical model with a joint probability distribution . The main difference between them is that DBN also captures temporal relationships between variables which is the vector for variables at the time point . If there are time points , then the joint probability distribution can be decomposed as , where represents the parent set of . In general , can include variables from the same time point or the previous time points ( represented as ) . There are many ways to simplify the complexity of the DBN model and data required to train the model . First , we can assume first order Markov property for transitional dependence , then the parent set can be simplified as which corresponds to a general two-slice model ( Figure 1A ) . The intra-slice links represent causal relationships inferred at static states or causal relationships happens in a shorter time than the sampling time between and . We will refer to this model as DBN in our present study . Second , we can further simplify the model and assume ( the variables in current time only depend on the previous time point ) , then the DBN corresponds to a simplified two-slice model without intra-slice interactions ( Figure 1B ) . Third , if we assume that the variable is self regulated ( ) , then the DBN can be represented as a two-slice model in Figure 1C , which is equivalent to a Granger causality test with a stationary Bivariate Auto-Regressive model ( BVAR ) . We will refer this model as the Granger causality test in our result . The DBN is a popular approach in computer sciences , such as Kalman filter and Hidden Markov Model ( HMM ) in voice recognition [15] or more recently in inferring transcriptional regulatory networks from time series data [2] and protein fragmentation process [16] . Another independent line of research of inferring causal relationship from time series is “Granger causality” . The Granger causality concept was originally developed for economic time series data [17] , but has since been applied to time series data in many different domains . The Granger causality networks under some assumptions are similar to special cases of the DBN . For example , the model in Figure 1C is a DBN and a Granger causality network with a stationary BVAR model . However , while the Granger causality and the DBN have recently been applied to elucidate temporal causality networks in a number of experimental works , such as SOS DNA repair in E . coli [2] , cell cycle in yeast [3] , muscle development in Drosophila [4] , and cell cycle in human cell lines [5]–[6] , no studies to our knowledge have expanded on this concept of temporal causality to gene expression time series data collected in vivo in humans . One of challenges of applying the Granger causality test to human samples is how to generate long time series data . We overcome the problem by combining multiple short time series . Our simulation results show that data combined from multiple short time series is as informative as a long time series . One of challenges of applying DBN to human samples is limited sample size . We tackled this problem by reconstructing the intra-slice structure from a large data set generated at static states , then reconstructing the inter-slice structure from the time series data . In the present study we have applied methods based on Granger causality and DBN to a set of human blood gene expression profiles generated at multiple time points during the course of a day , shown in Figure 2 . The blood gene expression data was generated from 40 apparently healthy males participating in a randomized , two-arm cross-over design study to assess the effects of fasting and feeding on the blood transcriptional network [18] ( see Materials and Methods section for details ) . The fasted and fed arms of the study provided the necessary data to characterize the dynamic changes in gene expression and corresponding pathways associated with fasting and feeding states in human blood samples [18] . After removing individual scaling effects by referencing individual's time point 0 , short time series were combined into virtual long time series ( shown in Figure 2 ) . Using the Granger causality test , we identified PER1 as the key regulator of the blood gene expression pattern in which multiple biological processes were under circadian rhythm regulation . Furthermore , the genes under PER1 regulation in the fed network are enriched for obesity causal genes . Finally , using the DBN , we show that over 72% of all inter-slice links are self links and when the SBN and the DBN were compared , we found that different processes such as inflammation and lipid metabolism , which are disconnected during fasting , are now dynamically linked together in response to food intake .
The two-way or three-way ANOVA analysis defining time- and state-dependent gene expression signatures provides meaningful way to characterize expression changes on a global scale [18] . However , these methods on their own do not provide any information on the causal regulators driving the time-dependent gene expression behavior . To leverage the time series data more maximally towards this end , we applied Granger causality test to gene expression traits scored systematically in the fasted/fed cohort blood samples at roughly 1 hour intervals during the course of a day ( Figure 2 ) . A gene expression trait is said to be Granger causal for gene expression trait if , at previous time points , provides significantly more information on time-dependent changes in than the historical information provides on itself . In our implementation of the Granger causality test , we test this by fitting to an autoregressive model with respect to the different time points , and then testing whether extending the autoregressive model by including improves the fit ( see Materials and Methods for details ) . If there is a statistically significant improvement testing the model fit ( assessed by comparing the models using the F test ) , then we declare that is Granger causal for , or simply as . Traditionally , a long time series is required to apply Granger causality test . However , it is hard to obtain a long time series of human samples collected in vivo . We have previously shown that over 80% of transcripts have significant inter-individual variances [18] , which is comparable to previously reported result [19] . Thus , we can treat time series data from 40 patients as 40 independent short time series . Assuming these 40 time series have similar dynamic behavior , but with different starting points , we can combine them together to generate a virtual long time series ( shown in Figure 2 , and see Materials and Methods for details ) . Our simulation results show that the virtual long time series are as informative as long time series with similar data points ( shown as Supplementary Figures S1 and S2 ) . We constructed causal networks for the fasted and fed states by applying the Granger causality test to all gene expression trait pairs generated in the fasting/feeding cohort described in Figure 2 . For gene expression traits scored in the fasting/feeding cohort , a link was inserted into the causal network if the p-value associated with the Granger causality test was less than 0 . 01 after multiple testing correction . The resulting fasted and fed networks were comprised of 2010 and 967 causal links ( listed in Supplementary Tables S1 and S2 ) , respectively . The corresponding false discovery rates ( FDR ) [20] for the causal links in the fasted and fed networks were and , respectively . Bootstrapping test results ( see Materials and Methods for details ) show that 80% and 90% of links in fast and fed networks have confident values above 0 . 5 , respectively ( shown in Supplementary Figure S3 ) . Both networks were observed to exhibit the scale-free property for out-degree distributions ( shown as Supplementary Figure S4 ) . From these data it was possible to identify all expression traits supported as Granger causal for at least one other expression trait in the network ( referred to here as causal regulators ) , and then rank order the causal regulators according to the number of genes for which they were supported as causal , shown in Table 1 . There are more causal links inferred for fast time series than for fed time series . The fasted network consists of many small subnetworks and the fed network consists of mainly two subnetworks ( shown in Figures 3A and 3B ) . The top causal gene in the fasted network is RNF144B , a putative ubiquitin-protein ligase that plays a role in mediating p53-dependent apoptosis . Genes under RNF144B regulation including PTEN are enriched for the GO biological process of negative regulation of cellular metabolic process ( p-value = 0 . 008 ) . The top causal gene in the fed network is PER1 , a transcription factor regulating the circadian clock , cell growth and apoptosis . The genes under PER1 regulation are enriched for genes correlated to plasma concentration of triglyceride ( p-value = 0 . 00045 ) in the Icelandic Family Blood ( IFB ) cohort [10] . PER1's downstream genes are involved in diverse biological processes including CREB5 , in circadian rhythm , PTEN and P53INP2 in apoptosis , IL1R1 , IL1RAP and TLR2 all involved in inflammation response , FASN and ACSL1 in fatty acid metabolism and MVK in cholesterol biosynthesis . These results suggest that food intake interacts with circadian rhythm and the circadian rhythm has impacts on many biological processes as has been previously shown in mouse studies [21]–[22] . Further , previous research has demonstrated circadian gene ( PER1 , PER2 , PER3 etc . ) mRNA expression rhythm in human peripheral blood cells and linked that to individual's circadian phenotype [23]–[24] . Our blood causal network where PER1 is a top causal gene illustrates a potential mechanism of how the CNS control and environmental influences ( e . g . external sunlight ) can affect circadian rhythm gene expression which in turn regulating a host of other biological functions . More specifically , circadian rhythm genes ( PER1 in particular ) play important roles in cell cycle regulation and cancer processes [25]–[26] . These reports support our observations in the fed network that several genes under PER1 control are involved in apoptosis and cell cycle regulation ( e . g . , PTEN and P53INP2 ) . 380 human genes are cataloged as obesity causal genes in the human obesity map [27] . In recent years , many large genome-wide association studies ( GWAS ) have convincingly identified a number of genes causing human obesity . 34 genes including FTO were replicated in many populations [28]–[31] Taking consideration of these two sources , there are 409 obesity causal genes , and 246 of them were expressed in our blood data set . When the obesity causal genes were overlapped with the fasted and fed networks , 7 genes ( ADA , BBS5 , CBL , CCND3 , FASN , FTO and SCARB1 ) overlapped with PER1's downstream genes in the fed network ( Fisher's Exact Test p-value = 0 . 037 ) ( shown in Figure 3C ) . It has been shown that circadian rhythm links to metabolic processes in mouse [32]–[33] . For instance , mutations in mouse genes involving circadian rhythm regulation , such as Clock , can lead to obesity [34] . Our results provide evidence that human obesity causal genes are under circadian rhythm control in a peripheral tissue like blood . Constructing DBN using the model described in Figure 1A , requires a large amount of data and computational resources . However , when the intra-slice structure ( the SBN ) is known , then there is a dramatically reduced demand for large amounts of both data and computational resources . A large dataset of profiled peripheral blood samples ( IFB ) is already described and available [10] . The fasting feeding study group and the IFB cohort are derived from the same population both in terms of geological location and genetic background , therefore the static networks based on these two studies are assumed to be similar . The IFB data set consists of both gene expression measured in the fasting state and genotype data . Previously , we demonstrated that Bayesian networks constructed by integrating gene expression data and genotype data were of high quality [12]–[13] , [35] . To match for gender , data from 455 males in the IFB cohort was used to construct a static Bayesian network which consisted of 7310 nodes ( genes ) and 11047 links ( see Method Section for details ) . The static Bayesian network was fixed as the intra-slice network in the DBN model shown in Figure 1A , and then the time series data ( fast or fed ) were used to construct inter-slice connections . The fasted and fed DBNs consisted of 1125 and 1290 inter-slice links ( listed in Supplementary Tables S3 and S4 ) , respectively . Among them , 846 ( 75% ) and 936 ( 73% ) were self links . 404 self links are common between the fasted and fed DBNs . The genes under self control ( with self links in DBNs ) are enriched for cis expression quantitative traits ( cis eQTLs ) in blood ( enrichment p-values = and for the fasted and fed DBNs , respectively ) . One important goal for utilizing time series data is to study the dynamic changes in molecular networks . Under static condition , many biological processes may be disconnected or loosely connected , whereas under a perturbation , these processes will change coordinately . 409 obesity causal genes mentioned above were collected from two resources , namely the human obesity map [27] and recent GWAS data [28]–[29] , [36] . 138 out of the 409 genes are included in the DBNs . These 138 genes were used as seeds to construct obesity related sub-networks for fast and fed DBN and the SBN as previously described [13] . The fasted and fed subnetworks were compared with the subnetworks constructed from the SBN . The largest change was from the fed subnetwork , where three segmented subnetworks in the SBN were connected in the fed DBN by two inter-slice links ( shown as red in Figure 4 ) . CDCA7 , a transcription regulator for the cell cycle , is found in the center of the connected subnetworks . It connects genes involved in lipid metabolism such as NPC1 , FABP5 and APOE to the large subnetwork on the left which consists of inflammatory response genes such as STAT3 , STAT5 , GPR109A , TNF , NTSR1 , ORM1 and IL1RN . This suggests that the expression of genes involved in either inflammatory response or lipid metabolism change coordinately in response to food intake . It is also worth noting that the circadian rhythm regulator PER1 is in the subnetwork on the left , which consists of many genes involved in inflammatory response pathways . As well , in the fed DBN , both cell cycle regulation and lipid metabolism processes are linked to the circadian rhythm .
Designing experiments to generate large-scale molecular phenotyping data and to enable inferring causal relationships among genes and between genes and clinical endpoints is now a feasible task . Genetic variants ( e . g . nonsynonimous , nonsense , eSNPs etc ) , genetically modified animals ( e . g . , knockouts , transgenics , RNAi knockdown ) , and chemical perturbations have all been used successfully to establish a causal relationship between genes and phenotypes in mammalian systems . Here we have detailed the use of time series data in a human population to predict causal regulators using a Granger causality test and a DBN . Our Granger causality networks showed that multiple biological processes such as apoptosis , inflammation response and lipid metabolism are under circadian rhythm regulation and obesity causal genes are under circadian rhythm regulator PER1 in the fed networks . For the DBN , we showed that over 73% of inter-slice links are self links . When the SBN and the DBN were compared , we find that different processes such as inflammation and lipid metabolism are linked together during the dynamic changes in responding to food intake . The time series data provided a path to go beyond the characterization of interesting patterns of expression and network differences associated with complex states ( like fasting and feed status ) , by allowing for the identification of putative causal regulators driving these differences . While extensive experimental validation will be required to assess the full utility of the approach detailed in the present study , we believe these methods and the characterizations of time and state dependent changes in gene expression and network topology , will motivate a need to integrate a time domain into gene expression experiments that aim to elucidate complex system behavior . Our data consist of many short time series from multiple individuals instead of a single long time series . Our approach for combining multiple short time series was based on the assumption that individual response slopes are similar . First , the population under study is relatively homogeneous , i . e . only males , similar age , same population , same ethnicity and each individual consumed the meal of same size and composition . Second , we reduced the individual specific variance by normalizing each individual data according to its own expression data at the first time point . This essentially reduces the number of parameters to fit in the model , at the cost of reducing the number of time points available to feed into the model . In contrast , if the population under study was genetically heterogeneous , we would treat the response slope differently for different individuals and would employ the mixed-effects model as suggested by Berhane and Thomas [37] for combining time series . In that case , we wouldn't need to normalize data for each individual , and as a result there would be an increase in the number of parameters to fit as well as an increase in the available data points . We note in passing , that the Icelandic population is relatively homogenous as regards genetic makeup and environmental parameters . Our implementation of the Granger causality test is a special form of DBN where there is no causal structure within a single time slice . There are also many variations of the Granger causality test including stationary or non-stationary , dynamic or time-invariant Granger causality tests . Our simple implementation of Granger causality test identified the transcription factor PER1 as the main causal regulator in the fed time series . The intra-slice network ( SBN ) was reconstructed from an independent data set and is fixed in our current model of DBN . Even though the SBN was reconstructed using about 455 samples , there are still many uncertainties about the network structure and edge directions . Further researches on using the SBN as flexible priors for intra-slice structure rather than fixed one are warranted . Several simulation studies have been carried out to estimate the number of samples that are required to build SBNs or DBNs . Zhu et al . [12] showed that these numbers are related to the interaction strength between nodes . For instance , with networks consisting mainly of interactions at intermediate strength , over 80% of interactions in SBN can be recovered at 90% precision with 1000 samples . Similarly , Yu et al . [38] showed that over 85% of links in DBNs can be recovered with 2000 samples . In addition , Yu et al . showed that the sampling interval is also an important parameter . When the sampling interval is small , the difference between data at consecutive time points will be small . In other words , the independent information added is small . Our time series simulation result ( Supplementary Figure 2 ) and the results of Yu et al . , both show that network reconstruction accuracies drop when sampling intervals are large . In both our and Yu et al . 's time series simulations , all interactions have the same time lag . In reality , the time lags are different for different transcriptional regulations [39] . Zou and Conzen [3] showed that a better reconstruction accuracy of DBN could be achieved when considering time lag differences . The general DBN model shown in Figure 1a can represent mixed time lags with intra-slice interactions for zero or short time lags and inter-slice interactions for large time lags . Based on the complications discussed above , at least 1000 data points are needed to reconstruct an adequate DBN . Sachs et al . [40] suggests that even over 23 , 000 data points are not sufficient for reconstructing an accurate DBN . Obviously , additional priors can improve reconstruction accuracies with the same amount of data [3] , [12] . To accurately estimate the amount of data required to reconstruct DBNs under different interaction strengths using different mixtures of time lags and different priors , a systematic data simulation is warranted . The causal networks derived from either the Granger causality test or the Dynamic Bayesian network , both showed that the networks under the fasting state were fragmented ( loosely connected ) while the networks in the feeding state are more highly interconnected . It is well established , that the circadian rhythm interacts with metabolic [32] and immune response processes in rodents [41] . For instance TNF-alpha , which regulates immune cells and induces apoptotic cell death , is also shown to regulate key genes in the circadian rhythm , including Dbp and Per1-3 [41] . It is possible that increasing nutritional load directly affects the circadian rhythm system , possibly through ghrelin [42] . Our results in humans are consistent with the rodent data , showing that feeding is directly linked to the circadian rhythm system . Furthermore , our results suggest that the interconnections between different biological processes such as metabolic and immune responses and activated cell death are weak in the fasted state , while feeding dramatically enhances the interconnections between these different biological processes . Further experimental work is warranted to verify whether these changes still hold in the general population . Human peripheral blood is the most readily accessible human tissue for clinical studies . Our work on peripheral blood has demonstrated that feeding or increasing nutritional load affects the human circadian rhythm system , which becomes highly connected to other biological processes including metabolic and immune responses . And these effects can be observed in peripheral blood . We believe the results of the present work have broader implications for studies of drug response and for genetic and experimental studies on blood chemistry and vascular related clinical traits . Our results suggest that how blood networks respond to feeding is an important variable that may bring us closer to dissecting the underlying causes of obesity and associated disorders . Our results also provide a guideline on how much data are required for inferring causal relationship in human blood for future experiments .
40 healthy participants from an Icelandic company were recruited to participate in a randomized , two-arm , cross-over study to examine the effects of fasting and feeding on human blood gene expression [18] , shown in Figure 2 . For the first period of the study the 40 participants were randomized to two treatment groups , with 20 individuals making up each group . All participants began fasting at 9pm the night before the first period of the study . The first treatment group comprised the fasted arm of the study for the first period , where participants continued to fast through the day for the duration of the study ( participants were only allowed to drink water during this time ) . The second treatment group comprised the fed arm of the study for the first period , where participants were fed a standard meal in the morning and then fasted through the rest of the day for the duration of the study . The second period of the study was carried out one week later from the start of the first period . The protocol for the second period of the study was identical to the first period , except those in the fasted arm for the first period were switched to the fed arm , and those in the fed arm for the first period were switched to the fasted arm . Figure 2 shows the schematic for the experimental design . A total of 560 peripheral blood samples were collected from the 40 participants at 7 time points for each period of the study . Significant inter-individual variation has been noted in human blood gene expression profiles [43] . Previous analyses carried out on this data set detailed the inter-individual variation and overall expression differences between the fasted and fed conditions [18] . In the present study we focus mainly on using temporal information to infer causal relationship by applying a Granger causality test and a dynamic Bayesian network so that possible causal drivers of dynamic changes can be identified from the causal networks . To correct for the individual differences in gene expression we referenced each individual expression profile to the corresponding individual profile at time point 0 . This reduced the effective number of time points to 6 for this study . The time series based causality test was proposed by Wiener [44] as the notion that , if the prediction of one time series could be improved by incorporating the knowledge if a second one , then the second series has a causal influence on the first . Granger was the first to formalize the idea in the context of linear regression model [17] , so that time series based causality test is generally referred as Granger causality test . There is a variety of models for testing Granger causality , such as multivariate autoregressive model ( MVAR ) and bivariate autoregressive model ( BVAR ) . If coefficients in the regression model do not change depending on time , the model is referred as a stationary model . Otherwise it is referred a non-stationary model . The simplest model is stationary bivector autoregressive model . Even though comparing to MVAR , BVAR tends to infer many indirected links , the causal directions of these inferred links follow causal information flows [45] . To remove potential in-direct links , for each gene , we only keep one causal link pointing to it , which has the most significant p-value in the BVAR model . Traditionally , Granger causality test is applied to long time series . However , it is hard to collect long time course data from human samples . Our data consists of many short time series from multiple individuals . There are several theoretical studies related to combining multiple time series in a general regression frame work , including for instance that of Berhane & Thomas [37] and Guerrero & Pena [46] . Berhane & Thomas [37] proposed to use a mixed-effects model to combine time series from different locations , while Guerrero & Pena [46] outlined a weighted least squares approach . In both approaches , some constraints were applied after a number of assumptions were made . Our approach is a simplified version of the Berhane & Thomas approach [37] . Instead of using community-specific slopes , we assumed response slopes for individuals are similar . Further , in order to reduce individual specific variation which could affect the response slope , an individual's gene expression data were normalized according to its own expression data at the first time point . Our simulation study shows that causal relationship can be accurately inferred by combining these short time series . Under first order stationary BVAR model , a set of data was simulated for causal relationship as following: ( 1 ) There are independent time series of length , , . All coefficients and noises follow normal distributions as ( 2 ) The initial conditions are draw from an uniform distribution with mean 0 . 1000 independent time series were simulated , and each series consists of 240 time point ( shown as Supplementary Figure 1 ) . The test of Granger causality under BVAR model can be carried out by comparing the full model ( 3 ) with the autoregressive model ( 4 ) The significance of the Granger causality test ( full model explains more variance than the autoregressive model ) is then measured by F-test statistics ( 5 ) where and are sum of squared residuals of full model and autoregressive model , respectively; and is the length of the time series . For the 1000 time series simulated above , the p-values of Granger causality are estimated as Eq . 5 . If only partial time points are used , then the power to detect Granger causality decreases ( shown in Supplementary Figure 2 ) . It is worth to note when the same number of time points are used , it is more likely to inferred correct causality if the interval between time points is shorter . If only 6 time points are used , no Granger causality test is significant if considering the time series independently . If assuming and are similar , then these short series can be combined together to infer Granger causality , and the Eq . 3 can be modified as ( 6 ) where and are sum of squared residuals of full model and autoregression model , respectively . For example , a virtual time series by combining the first 6 time points of randomly selected 40 time series is as informative as a long time series with the same time points . To estimate the false positive rate , we permuted the assignment of 1000 time series generated above ( for example was assigned as where ) so that the autoregressive assumption was valid . For each permutated data set , we followed the same procedure mentioned above to calculate p-values for the Granger causality test . At different p-value cutoffs , we calculated the recall ( positive rate ) and the false positive rate ( shown in the Supplementary Figure 2 ) . It is of note that choosing the optimal time lag length in the autoregressive ( AR ) model normally requires comparing model residuals and statistics at different p-value thresholds . However , because of the small sample size ( 40 ) and limited number of time points ( 6 ) , we restricted our analyses here using AR models with only first order time dependency , similar to what has been done in previous studies [5]–[6] . Similarly , we assumed the Granger causal relations were stationary from time point 1 to 6 . That is , we were mainly interested in the mean α and β values in Eq . ( 1 ) , which represent the averaged Granger causality between genes from time point 1 to 6 . A bootstrapping procedure of re-sampling individuals with replacement , was used . At each time , one subject ( along the associated data at 6 time points ) was sampled from a pool of 40 individuals . A bootstrapped data set consisted of 40 sampled individuals ( 40×6 data points ) . The same Granger causality test outlined above was applied to the re-sampled data . The bootstrapping procedure was performed 100 times . The link confident value is the percentage of a link's p-values above a multiple testing corrected threshold in the results of the 100 bootstrapping tests . 455 male samples in IFB cohort [10] was used in reconstruction of the static Bayesian network . A set of informative genes were identified as follows: ( 1 ) a gene expressed in the blood ( with mean log intensity >−1 . 5 ) , ( 2 ) the variation of the mean log ratio was larger than 1 . 23 . Of the 23720 genes represented on the microarray , 7310 were selected for inclusion in the network reconstruction process as previously described [12] , [35] . One thousand Bayesian networks were reconstructed using different random seeds to start the reconstruction process . From the resulting set of 1000 networks generated by this process , edges that appeared in greater than 30% of the networks were used to define a consensus network . For a two-slice dynamic Bayesian network represented in Figure 1A , it can be decomposed as , where is the parent set of . The static Bayesian network reconstructed above was used as the intra-slice network . The intra-slice network is fixed and is not refined in the process of reconstructing dynamic Bayesian networks . Thus , only inter-slice links ( ) are added or removed during the reconstruction process . Similar to the static Bayesian network reconstruction process , 1000 networks were reconstructed using different seeds and the Bayesian information criterion ( BIC ) score [47] was used for the optimization . Edges appeared in 30% of the 1000 structures are included in the final network .
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Peripheral blood is the most readily accessible human tissue for clinical studies and experimental research more generally . Large-scale molecular profiling technologies have enabled measurements of mRNA expression on the scale of whole genomes . Understanding the relationships between human blood gene expression profiles and clinical traits is extremely useful for inferring causal factors for human disease and for studying drug response . Biological pathways and the complex behaviors they induce are not static , but change dynamically in response to external factors such as intake/uptake of nutrients and administration of drugs . We employed a randomized , two-arm cross-over design to assess the effects of fasting and feeding on the dynamic changes of blood transcriptional network . Our work has convincingly shown that feeding or increasing nutritional load affects the human circadian rhythm system which connects to other biological processes including metabolic and immune responses . We believe this is a first step towards a more comprehensive population-based study that seeks to connect changes in the blood transcriptome to drug response , and to disease and biology more generally .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/population",
"genetics",
"computational",
"biology/systems",
"biology",
"computational",
"biology/transcriptional",
"regulation"
] |
2010
|
Characterizing Dynamic Changes in the Human Blood Transcriptional Network
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The range of the Asian tiger mosquito Aedes albopictus is expanding globally , raising the threat of emerging and re-emerging arbovirus transmission risks including dengue and chikungunya . Its detection in Papua New Guinea's ( PNG ) southern Fly River coastal region in 1988 and 1992 placed it 150 km from mainland Australia . However , it was not until 12 years later that it appeared on the Torres Strait Islands . We hypothesized that the extant PNG population expanded into the Torres Straits as an indirect effect of drought-proofing the southern Fly River coastal villages in response to El Nino-driven climate variability in the region ( via the rollout of rainwater tanks and water storage containers ) . Examination of the mosquito's mitochondrial DNA cytochrome oxidase I ( COI ) sequences and 13 novel nuclear microsatellites revealed evidence of substantial intermixing between PNG's southern Fly region and Torres Strait Island populations essentially compromising any island eradication attempts due to potential of reintroduction . However , two genetically distinct populations were identified in this region comprising the historically extant PNG populations and the exotic introduced population . Both COI sequence data and microsatellites showed the introduced population to have genetic affinities to populations from Timor Leste and Jakarta in the Indonesian region . The Ae . albopictus invasion into the Australian region was not a range expansion out of PNG as suspected , but founded by other , genetically distinct population ( s ) , with strong genetic affinities to populations sampled from the Indonesian region . We now suspect that the introduction of Ae . albopictus into the Australian region was driven by widespread illegal fishing activity originating from the Indonesian region during this period . Human sea traffic is apparently shuttling this mosquito between islands in the Torres Strait and the southern PNG mainland and this extensive movement may well compromise Ae . albopictus eradication attempts in this region .
The Asian tiger mosquito Aedes ( Stegomyia ) albopictus , originally described by Skuse from Calcutta , India , in 1894 , is considered native to the Southeast Asian region where the larvae are often found in forest tree holes – a characteristic that assists its current global expansion via rapid adaptation to human-made container habitats [1] , [2] , [3] . This global expansion is also driven by human behavior , often facilitated by the transport of used tyres that contain desiccation-resistant eggs or , in some cases , by the movement of the containers themselves [4] , [5] . Prior to the 1980s , Ae . albopictus had spread to several islands in the Indian Ocean , as well as to the Hawaiian Islands in the Pacific [6] . It was discovered in Albania in Europe in 1979 [7] , and has also established in both North [8] and South America [9] , in Africa in 1992 [10] , and in southern Europe [11] . It is currently expanding into over 20 European countries [2] . Alongside this species' global expansion , its status as a vector of human pathogens is also of increasing concern . As a laboratory vector of over 25 arboviruses , its role in arbovirus transmission cycles has mostly been secondary to other incriminated vectors [12] , [13] , [14] . In the absence of the primary dengue vector , Aedes ( Stegomyia ) aegypti , Ae . albopictus has been the epidemic vector of dengue viruses in Hawaii , Macao and China [14] , [15] , [16] , [17] . In 2005 , it was implicated as the epidemic vector during a resurgence of chikungunya ( CHIKV ) , an alpha virus clinically similar to dengue , in the Indian Ocean and in Italy [17] , [18] , [19] . Subsequent studies revealed that Ae . albopictus is highly susceptible to the CHIKV , with the species not only responsible for these outbreaks but also able to transmit the virus after only two days [17] , [20] , [21] . In the Australasian region , Ae . albopictus was first detected in 1963 in Jayapura on the West Papua Province of Indonesia ( see Fig . 1A ) . Subsequent surveys during the early 1970s confirmed its presence in northern Papua New Guinea ( PNG ) near Madang [22] , [23] . By 1980 it had arrived in southern PNG's Port Moresby ( PNG's capital ) , and moved eastwards into Bougainville Province and the Solomon Islands [24] , [25] . Its detection in southern PNG's Western Province southern Fly River coastal fringe in 1988 ( see Fig . 1B ) , combined with surveys in 1992 revealing it on Daru Island in the northern Torres Strait region and in Kiunga Port over 700 km up the Fly River , established beyond doubt that the species was extant just 150 km from mainland Australia's Cape York [24] , [26] . Despite there being at least 28 collections of Ae . albopictus at six Australian seaports , this species has not yet established on Australia's mainland [27] . In 2005 , Ae . albopictus was detected on Masig Island in the central Torres Strait Islands and molecular identification of previously collected Ae . albopictus larvae [28] ( which discriminated it from local Ae . scutellaris species ) , dated its arrival to 2004 . Subsequent surveys in the Torres Strait revealed its presence on 10 of the 17 inhabited islands [29] . Considering the potential of both the human health and societal ( nuisance ) impacts of Ae . albopictus establishing on mainland Australia , the obvious question was why Ae . albopictus had only expanded into the Torres Strait Islands in 2004–05 when it was known to have been extant on Daru Island ( northern Torres Strait ) and in Kiunga in 1992 – 12 years earlier ? A number of potential sociological and ecological factors may have contributed to the mosquitoes' proliferation and led to its dramatic expansion into the Torres Strait islands in the mid-2000s . For example , the increase in human-made water storage containers and sundry smaller discarded disposable containers may have served as potential larval habitats , leading to a population expansion . The discovery of Ae . albopictus in the Torres Strait in the mid 2000s led to the question of whether recent adaptation to climatic variability had played a role in its expansion – as we had suggested in earlier work on other container inhabiting Aedes species in this region [30] . The 1997–98 El Nino conditions contributed to the worst drought in PNG for 100 years: traditional groundwater supplies were greatly affected , either drying up or becoming contaminated [31] . As local springs and streams dried up , it became necessary for villages to store water in large containers including 220 L ( 44 gal ) drums and rainwater tanks – both of which provide highly productive larval sites for container-inhabiting mosquito species [32] . As part of an international aid response , AusAID funded and transported 9 , 000 L polypropylene rainwater tanks and 200 L water containers to the southern Fly River region villages immediately adjacent to the Torres Strait in a project completed in 2002 [33] , [34] . This human adaptation to climate variability may have provided abundant productive larval sources for the population of Ae . albopictus already present in PNG , leading to its rapid population expansion and a subsequent spillover into the Torres Strait Islands . Once Ae . albopictus was established on the islands , the continuous human ocean traffic would have rapidly shuttled mosquitoes through the region . Thus our working hypothesis was that climate variability driven by the 1997–98 El Nino resulted in water storage management changes in PNG's southern Fly River coastal villages and was indirectly responsible for the invasion of a local Ae . albopictus population through the Torres Strait Islands . In this study , we use extensive regional mosquito collections and population genetics methodologies to investigate the origins and dynamics of the introduction of Ae . albopictus into and through the Torres Strait islands , as well as the population structure of this species throughout PNG . The maternally inherited mitochondrial DNA ( mtDNA ) cytochrome oxidase I ( COI ) is used as both a population genetics marker and a proxy for female movement between islands and southern PNG villages . The rationale here is that the DNA sequence of each female is ( barring mutation ) identical to that of her offspring , providing insights into the dynamics and diversity of the females' contribution to each population . This proxy would ultimately be an underestimate of movement as different females of the same sequence cannot be distinguished . Additionally , we developed and ran 13 microsatellites markers that permitted the evaluation of the nuclear background of these mosquitoes .
Container-inhabiting mosquitoes were collected from throughout the Torres Strait and PNG's southern Fly River region villages by Queensland Health between 2004 and 2010 ( Tables 1 and 2 , and Figure 1 ) . Populations of Ae . albopictus collected in 1992 from Daru Island ( northeastern Torres Straits ) and the Kiunga Port area in Western Province were provided by the Australian Defence Force . When samples were collected from private residences , permission was granted prior to entry . In most cases larvae were sampled from different containers at each location and preserved in 70% ethanol , and in some cases adults were collected . In many cases only a few individuals were collected at each location in order to reduce the chance of sampling siblings ( as larvae in the same container ) . Larvae were initially identified as Ae . albopictus using the morphological keys of [35] and then by either real-time PCR assays [36] or by a PCR-restriction digest procedure [28] to distinguish them from endemic members of the Aedes ( Stegomyia ) scutellaris taxonomic group . Specimens identified as Ae . albopictus had genomic DNA extracted using a salt extraction method [37] . For PCR amplification of a 445 bp ( final edited product size ) region of the mtDNA cytochrome oxidase 1 ( COI ) , the forward primer 5′ CAY CCT GGT ATA TTT ATT GG ′3 and reverse primer 5′AAT TAA AAT ATA AAC TTC TGG were modified from [38] . The reaction was carried out in 0 . 2 ml well PCR plates ( Astral Scientific ) using 25 µl final volume and oil overlay ( single drop ) . Final PCR mixture contained 16 . 6 mM [NH4]2SO4 , 67 mM Tris-HCl pH 8 . 8 ( at 25°C ) , 0 . 45% Triton X-100 , 0 . 2 mg/ml gelatin , 1 . 5 mM MgCl , 0 . 2 mM of each dNTP , 0 . 4 µM of each primer . One unit of Taq polymerase ( Bioline ) and 2–10 ng of purified genomic DNA ( 1 µl of gDNA ) were used per reaction . Cycling ( MJ research PTC200 or a BioRad C-1000 thermal cycler ) was 94°C for 3 min followed by 30 cycles of 94°C for 1 min , 40°C for 1 min , and 72°C for 1 min using minimum transition times between steps . The PCR products were visualized on a 1% agarose gel containing 0 . 5 µg/ml ethidium bromide and visualized at 312 nm . PCR product purification was via QIAGEN ( QIAquick ) PCR purification columns using manufactures recommendations . All sequences were edited and aligned using the Geneious software [39] . To examine phylogeographic relationships , we constructed maximum parsimony haplotype networks in TCS 1 . 21 [40] under a 95% connection limit . Pair-wise FST values were estimated in Arlequin version 3 . 5 ( distance method ) [41] to assess levels of differentiation between the regions for the COI locus: regions were designated Torres Strait Islands ( excluding Daru Island ) , southern Fly region , Daru Island , Kiunga , Port Moresby , Madang/Lae Region , Timor Leste and Jakarta . The significance levels of FST comparisons were assessed using permutation tests ( 1 , 023 permutations per comparison ) , also implemented in Arlequin . DnaSP 5 [42] was used to estimate haplotype diversity and nucleotide diversity within regions . We performed Tajima's D and Fu's Fs tests of neutrality for the COI data per population in the program Arlequin . Candidate microsatellite markers were isolated from Roche GS FLX 454 sequencing data ( 1/16 plate - 25 , 000 reads at ∼400 bp length ) generated from genomic DNA of Ae . albopictus and performed by Macrogen ( Korea ) . To design primers for microsatellite loci , we ran the resultant data through the program msatcommander [43] . We used this program to find primers for dinucleotide , trinucleotide and tetranucleotide repeats , and allowed the program to design primers with a melting temperature in the range of 50–62°C with a GC content between 30 and 70 percent . Long polynucleotide repeats ( >5 bp ) within sequences to be amplified were avoided and duplicate markers ( i . e . primers designed for sequence analogues ) were excluded . Screening of candidate markers involved PCR amplification of a subset of samples using standard primers and visualization of products on 1% ethidium bromide stained agarose gels . Positive product primer sets were re-amplified with M13 labeled forward primers and dyes ( VIC , FAM , PET and NED ) and standard reverse primers . The final PCR mixture contained 1× Mytaq buffer ( Bioline ) ( containing pre-optimized concentrations of MgCl and dNTPs ) , 0 . 4 µM of each primer , 0 . 5–1 . 0 unit of MyTaq polymerase ( Bioline ) and 5 . 0–10 . 0 ng of extracted genomic DNA ( 1 µl of extraction ) . The cycling involved an initial denaturation of 95°C for 3 min , then 13 cycles of 95°C for 30 s , 56°C for 40 s with a gradient decrease of 0 . 5°C/cycle , and 72°C for 30 s , followed by 25 cycles of 95°C for 30 s , 50°C for 40 s and 72°C for 30 s , and a final 72°C for 5 min using minimum transition times . M13 labeled products for 13 microsatellite markers ( see Table 3 for details ) that generated clean peaks and that amplified consistently were purified using ExoSap ( Antarctic phosphatase and Exonuclease I-New England Biolab ) and were sent to Macrogen ( Macrogen , Geumchun-gu , Seoul , Korea ) for genotyping . We attempted to genotype 199 individuals sampled from the Torres Strait Islands , New Guinea , Timor Leste and Jakarta ( see Table 2 for sampling information ) . Alleles for each marker were scored manually in the program GeneMarker [44] . We checked for the possible presence of null alleles for each marker at a population level ( based on the regions: Torres Strait , Fly Region , Daru , Kiunga , Madang , Port Moresby , Timor Leste and Jakarta ) using the program MICRO-CHECKER [45] . Using these same population definitions , we checked for HWE , as well as calculating observed ( Ho ) and expected ( He ) heterozygosity in the program GenAlEx , v6 [46] and the program GenoDive [47] was used to calculated Fis for each population ( Table 4 contains details of Null Alleles , HWE , etc ) . The Bayesian program STRUCTURE , v . 2 . 3 . 2 [48] , [49] , was used to infer the most likely number of genetically distinct groups ( K ) in the region sampled , based on the microsatellite data . STRUCTURE was run using the admixture model , and using sampling locations as priors . Including information on sampling locations in STRUCTURE analyses has been shown to be useful for detecting subtle genetic structure , without detecting structure that is not present [48] . Due to the potential presence of null alleles at a number of markers in some populations , we used a dominant marker model in STRUCTURE ( as recommended in the user manual ) . STRUCTURE was run for five iterations of K = 2 to K = 8 , for a total of 1 million generations per iteration with a burn-in of 200 000 iterations . STRUCTUREHARVESTER , a program that implements the Evanno et al . Delta K method [50] , [51] , was then used to infer the most likely value of K and CLUMPP v . 1 . 1 . 2 [52] was used to average the results of the replicates for K = 5 ( the most likely value based on the Delta K method ) . We used the Greedy algorithm in CLUMPP with 1000 repeats . The output from CLUMPP was run through DISTRUCT [53] , which provides more flexibility in generating figures than STRUCTURE . Additionally , the program GENETIX v . 4 . 05 [54] was used to perform a Factorial Correspondence Analysis ( FCA ) . FSTAT v2 . 9 . 3 [55] was used to test for linkage disequilibrium between loci , and finally Arlequin v . 3 . 5 [41] was used to estimate pair-wise FST values between the eight populations defined above .
A total of 16 mtDNA COI haplotypes were identified from 377 individuals ( haplotype diversity Hd = 0 . 769 ) collected throughout the southern Fly River villages , the Torres Strait islands , north and south PNG , Timor Leste and Jakarta – 10 of these haplotypes were present in the Torres Strait Islands and southern Fly River region ( see Table 1 and Fig . 1 for details on collections and mtDNA haplotype occurrence ) . All DNA sequences are available through GenBank ( KC572145 - KC572496 , KF042861-KF042885 ) and all tests of neutrality ( Tajima's D and Fu's Fs ) were non-significant . Haplotype diversity varied for each region with the Torres Strait Islands ( 122 individuals , 10 haplotypes , Hd = 0 . 801 ) and southern Fly River region ( 60 individuals , 6 haplotypes , Hd = 0 . 649 ) having substantially higher haplotype diversities than the PNG populations from Daru Island ( 35 individuals , 6 haplotypes , Hd = 316 ) , Kiunga ( 38 individuals , 3 haplotypes , Hd = 0 . 198 ) , Port Moresby ( 58 individuals , 5 haplotypes , Hd = 0 . 462 ) and Madang/Lae Region ( 39 individuals , 2 haplotypes , Hd = 0 . 391 ) as well as those from Timor Leste ( 17 individuals , 2 haplotypes , Hd = 0 . 118 ) and Jakarta ( 8 individuals , 2 haplotypes , Hd = 0 . 250 ) . The COI haplotype network ( Figure 2 ) suggests that there is some mitochondrial genetic structure between regions , with one of the most common haplotypes ( H1 ) sampled found predominantly in the populations extant in PNG ( the Madang/Lae region , Port Moresby , Kiunga and Daru Island – with Daru collections from both 1988 and 2008 ) , and only being sampled once ( in one individual ) in the Torres Strait Islands . The other common PNG haplotype ( H6 ) was sampled at relatively high frequency in the Torres Strait . Two other haplotypes ( H11 and H12 ) that were sampled relatively commonly in both the Torres Strait and in the Fly Region of PNG were also sampled in Daru . Haplotype 11 was also the most predominant haplotype sampled in both of the Indonesian populations ( Jakarta and Timor Leste ) , with Timor Leste also sharing H12 and Jakarta possessing one other private haplotype ( H16 ) . This suggests that there is a close affinity between Indonesian populations and those found in the Fly Region of PNG as well as those in the Torres Strait . Six private haplotypes were sampled in the Fly Region/Torres Strait ( H7 , H9 , H10 , H13 , H14 , H15 ) , three of which were only found singly in Torres Strait ( H7 , H9 , H13 ) and there were 5 private haplotypes sampled in PNG; H2 , H4 and H5 found only in Daru; with H3 and H8 found only in Port Moresby . Mitochondrial COI pair-wise FST relationships and significance comparisons supported the presence of structure between populations ( see Table 5 ) . Again the PNG Southern Fly Region and Torres Strait populations appeared highly distinct from the PNG populations with high and significant FST values between the populations ( roughly between 0 . 4 to 0 . 5 ) . The FST value between Torres Strait and Fly Region populations is significant but small ( 0 . 043 ) , and most comparisons between PNG populations are non-significant ( all FST<0 . 1 ) . Indonesian populations are not significantly differentiated from each other but have significant FST values in all other comparisons , with the Torres Strait populations being the most closely related to them , followed by the Fly Region and then by PNG populations . A total of 199 individuals were genotyped for the 13 microsatellites ( see Table 2 for mosquito sampling ) . Putative null alleles were found at some loci in some populations and tests for Hardy Weinberg equilibrium revealed that some loci did not meet the expectations of this model ( see Table 4 for detailed information ) , but no evidence of linkage disequilibrium between loci was found . The overall number of alleles per locus ranged from 8 to 17 ( Table 3 ) . The inbreeding coefficients ( FIS ) of the majority of loci were positive , and observed heterozygosity was less than expected heterozygosity in most cases , indicating an excess of homozygote genotypes at most loci ( see Table 4 ) . Although FST values are generally smaller for the microsatellite data than for the mitochondrial data ( Tables 5 & 6 ) , all microsatellite based pair-wise FST values between populations were significant , with the exception of the Torres Strait – Fly Region comparison ( FST = 0 . 00421 , Table 6 ) . Low FST values were found between PNG populations , as well as between Torres Strait/Fly Region populations and those from Indonesia , providing further evidence of close affinities between these populations . The most likely number of genetic clusters ( K ) inferred by STRUCTURE HARVESTER was K = 5 [50] , [51] . The bar plot generated in STRUCTURE ( Fig . 3 ) suggests five populations ( Figure 3 ) with three historically extant populations within PNG that may have experienced various levels of admixture , and one distinct population encompassing the Torres Strait Islands and the southern Fly River region ( purple ) . An additional population was found in Indonesia ( pink ) , and bar-plots suggest some similarity of these populations to some individuals in the Torres Strait and Fly Region . The populations from Daru Island ( collected in 1992 and 2008 ) , which sits geographically adjacent to the southern Fly River coastal region , are clearly differentiated from the introduced populations , with all individuals from these regions being assigned with high probability to a single population ( green ) . Samples from Kiunga are assigned with high probability to a distinct cluster ( red ) to which individuals from Port Moresby are also partially assigned , although these ( Port Moresby ) individuals are also assigned to another cluster ( yellow ) with higher probability . Individuals from Madang in northern PNG are assigned with highest probability to the green cluster ( mostly found in Daru ) and with a lower probability to the yellow cluster ( mostly found in Port Moresby ) . The factorial correspondence analysis performed in GENETIX ( Figure 4 ) supports the results of the STRUCTURE analysis . It clearly shows the close relationship between individuals from Daru Island and Madang , as well as between individuals from Port Moresby and Kiunga . Additionally , the introduced populations from the Torres Strait Islands and the southern Fly region are closely associated . The population with the greatest genetic affinities to the introduced population based on the FCA is Timor Leste , suggesting that the source of the introduction was more likely from the Indonesian region ( where Ae . albopictus is common ) than from the extant PNG populations , as had been previously hypothesized . The Jakarta population is relatively isolated on the FCA plot .
Detection of Ae . albopictus in southern Papua New Guinea ( PNG ) in 1988 and 1992 placed it only 150 km across the Torres Strait from mainland Australia . In 2004–05 it appeared on the Torres Strait Islands and we initially suspected a range expansion from PNG potentially driven by human adaptation to climate variability . The AusAID funded drought-proofing expansion of rainwater tanks and 200 L water containers into the southern Fly River region villages immediately adjacent to the Torres Strait was completed in 2002 as a response to climate variability in the region [33] , [34] . Thus it was reasonable to hypothesize that the population historically extant in PNG had undergone a range expansion , initially into the southern Fly River region and subsequently into the Torres Strait Islands . The introduction of the species into the Torres Strait Islands was traced back to 2004 [29] , at a date that appeared to correlate with the change in water management which had occurred a few years earlier . Initially the mtCOI suggested that two genetically distinct populations were present in this region , providing the first piece of evidence that the invading population may not have originated from the population previously extant in PNG . Shared haplotypes between the southern Fly Region , the Torres Strait Islands and Indonesian populations provided the first clue as to where the invading population may have originated . Despite haplotype diversity being biased by the larval sampling method ( which favors the collection of siblings of the same haplotype ) , the Torres Strait Island populations revealed four more haplotypes ( a total of 10 , Hd = 0 . 801 ) than the southern Fly River ( 6 haplotypes , Hd = 0 . 649 ) . This difference in haplotype diversity may suggest that the initial introduction into the region started in the Torres Strait Islands from the Indonesian region , however more COI sequencing from the southern Fly region may be needed to clarify whether or not this is the case . Interestingly however , genetic diversity appears higher in the Torres Strait and Fly Region than in the Indonesian populations , possibly suggesting multiple introductions of closely related populations from different parts of Indonesia , or that the founding population was more genetically diverse than those sampled from the Indonesian region . The mitochondrial DNA was informative at another level with the discovery that multiple Ae . albopictus mtDNA haplotypes ( representing different females contributing to the population ) were moving between islands . This suggests that attempts to eradicate the species from individual islands would likely be unsuccessful given the high potential for re-introductions . Indeed , this information has assisted Queensland Health – the regional state health authority – in its decision to move from the island eradication program implemented in 2006 to a cordon sanitaire in 2008 , whereby surveillance and control was focused on the inner Torres Strait islands of Waiben , Muralug and Ngurupai ( Thursday , Prince of Wales and Horn islands ) adjacent to mainland Australia . In particular , Muralug and Ngurupai act as the major regional transport hubs and are thus the most likely staging point for the species' introduction onto the Australian mainland . This cordon sanitaire was breached in 2009 and Ae . albopictus now exists on Waiben and Ngurupia , less than 30 km from Australia's Cape York Peninsula . In 2010 , Aedes albopictus larvae were collected from New Marpoon on mainland Australia's Cape York although no other individuals have been collected in this area since . The 13 microsatellite loci reaffirmed the findings of the mitochondrial data that the introduced population was genetically distinct to the populations already present in PNG . As microsatellites evolve more rapidly than mitochondrial sequence data , they were more informative , resolving five genetically distinct populations in total with three historically extant populations in PNG that have experienced various levels of admixture and one distinct population encompassing the Torres Strait Islands and the southern Fly River region – the introduced population . Samples from Madang in northern PNG , as well as from Port Moresby on the southern Papuan Peninsula , appear to be admixtures , with individuals from Port Moresby being more similar to samples from Kiunga , and individuals from Madang more similar to the Daru Island population ( Figures 3 and 4 ) . Although Daru Island is proximal to the introduced population , material collected there on two separate occasions ( 1992 and 2008 ) was assigned with high probability to a separate population with close genetic affinities to the Madang material . Interestingly , the material collected from the Indonesian region ( Timor Leste and Jakarta ) forms a distinct population with apparent genetic affinities to the introduced population in the Torres Strait and southern Fly Region . Timor Leste revealed the highest genetic affinities in the FCA analysis and there are records of Ae . albopictus being present in Timor Leste dating to the 1920s [23] . Thus , our combined data strongly suggests that the introduction of Ae . albopictus into the Torres Strait and southern Fly River region came from the Indonesian region . Since it now appears highly unlikely that the introduced population originated from PNG but rather came from the Indonesian region ( west of the Torres Strait ) , it is conceivable that the introduction was driven by foreign fishing vessels that travelled from the Indonesian region , harboring Ae . albopictus specimens which then infested the islands of the Torres Strait and/or the southern Fly region . Indonesian/Macassan visitations to the coastline of northern Australia have a long history predating European settlement , and Indonesian fishermen have been known to illegally enter Australian waters more recently to fish for shark fin ( Walker , J . Pers . Com . ) . This activity is reported to have peaked between 2005–07 , during which time several hundred landings occurred where fuel , water , shark fin , fishing nets and lines were often cached . Many of these landings were associated with well-established camps that received multiple visits . Uninhabited islands in the Torres Strait were one focus for these activities and evidence gathered from apprehended vessels indicates that most shark boats ( Type III - highly mobile , motorized vessels ) carried large open-topped water drums of which a significant proportion harboured Ae . aegypti and Ae . albopictus larvae . There have been documented collections of various Aedes ( Stegomyia ) species , including Ae . albopictus , from illegal fishing vessels that were intercepted at the port of Darwin in the Northern Territory that clearly indicate that the mosquitoes' survival in smaller vessels is possible [56] , [57] . With regard to the expansion and movement of Ae . albopictus in this region , populations on Daru Island ( which adjoins the Torres Strait ) appear to have been unaffected by this exotic invasion up until 2008 and follow-up collections are now warranted to determine if the introduced population has established there since . Despite Daru Island sitting geographically adjacent to the southern Fly River coastal region , its Ae . albopictus population seems to be clearly isolated from the introduced population . The occurrence of two genetically distinct populations adjacent to each other in the northern Torres Strait/PNG region can be best explained by their different jurisdictions . While the Torres Strait operates as part of Australia , Daru Island is politically part of PNG and its function as the international customs clearance station into Western Province means that it encounters different incoming and outgoing transport movements . The movement of Ae . albopictus appears to be extensive in the Australasian region , particularly where the Torres Strait Islands' junction separates New Guinea and mainland Australia . With the maximum reported flight range of Ae . albopictus being 1 km [58] , movement between the Torres Strait's islands has most likely taken place via human-mediated transport . Australia has an oceanic border with PNG and the Torres Straits , but unlike most other international countries , it has no clearly marked frontier with border policing or customs controls . Thus relatively free movement occurs between PNG and the Torres Straits with approximately 5 , 000 international shipping movements per year [59] , and countless domestic movements that would likely shuttle Ae . albopictus between the islands and back and forth from the southern Fly region's villages . This movement is sanctioned under The Torres Strait Treaty ( Miscellaneous Amendments ) Act 1984 that allows for cross-border movement for trade , fishing , and family gatherings or for seeking medical attention without the need for customs protocols [60] –all of which may compromise quarantine in the region . The primary mode of transport between PNG villages and the Torres Strait Islands is via open-topped , outboard-motor-powered boats . In addition to sheltered harborage sites for adult mosquitoes , these vessels contain fresh water – both within drums for human consumption , and where rainwater has collected – which in turn provides oviposition sites and larval habitats for container-inhabiting species such as Ae . albopictus . Given its current location , its mobility and its phenotypic fondness for containers , Ae . albopictus is more than likely to arrive in a town or city on the Australian mainland via human transport . Due to the intrinsic ecological plasticity in both larval habitat ( both natural and human-made ) and host feeding patterns [3] , it will likely be able to move between urban and sylvan habitat , and control will be extremely challenging once it enters the latter . Its effect on native virus vector systems in Australia represents an unknown risk both to humans and to native and domestic animals . However , its cool climate tolerant biology and plasticity will certainly present new risks for dengue and chikungunya transmission in summer throughout most of the Australian region [27] , [30] . Aedes albopictus's particular biology permits its container inhabiting ecological niche to once again facilitate its global expansion . It is important to consider it's potential to rapidly exploit the outcomes of any socio-economic or policy-driven interventions , such as the recent and dramatic expansion of domestic rainwater tanks throughout Australian urban regions as a drought-proofing adaptation to observed and forecasted climate change [30] . Although this adaptation did not ultimately explain the range expansion of the species on Australia's northern doorstep , it will nonetheless provide a valuable niche in the landscape , which may augment this vector's existence across Australia's urban regions . In considering its eventual arrival , the public health risks associated with arboviruses meet the possibility of substantial daytime nuisance biting that will also negatively impact Australia's urban alfresco lifestyle .
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The range of the Asian tiger mosquito Aedes albopictus is expanding globally , raising the threat of emerging and re-emerging arbovirus transmission risks , including chikungunya and dengue . Detection of Ae . albopictus in southern Papua New Guinea ( PNG ) in 1988 and 1992 placed it 150 km from mainland Australia . In 2004–05 it reached the islands in Australia's Torres Strait that separate the mainland from PNG . Suspecting a range expansion from PNG driven by human adaptation to climate variability , we employed population genetics methodologies to investigate possible origins of the introduced population , as well as population structure of previously existing populations from New Guinea . Mitochondrial cytochrome oxidase I sequences and 13 novel microsatellite markers revealed a clear genetic distinction between regional populations in PNG and the newly introduced population in the Torres Strait and Fly Region . The closest genetic relative to the introduced population was found in the Indonesian region to the west and it is now suspected that this species may have been brought into the Torres Strait by sea vessels involved in extensive illegal fishing activities during this period .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"mosquitoes",
"vector",
"biology",
"biology",
"microbiology",
"vectors",
"and",
"hosts"
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2013
|
Tracing the Tiger: Population Genetics Provides Valuable Insights into the Aedes (Stegomyia) albopictus Invasion of the Australasian Region
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Seminal fluid proteins have been shown to play important roles in male reproductive success , but the mechanisms for this regulation remain largely unknown . In Caenorhabditis elegans , sperm differentiate from immature spermatids into mature , motile spermatozoa during a process termed sperm activation . For C . elegans males , sperm activation occurs during insemination of the hermaphrodite and is thought to be mediated by seminal fluid , but the molecular nature of this activity has not been previously identified . Here we show that TRY-5 is a seminal fluid protease that is required in C . elegans for male-mediated sperm activation . We observed that TRY-5::GFP is expressed in the male somatic gonad and is transferred along with sperm to hermaphrodites during mating . In the absence of TRY-5 , male seminal fluid loses its potency to transactivate hermaphrodite sperm . However , TRY-5 is not required for either hermaphrodite or male fertility , suggesting that hermaphrodite sperm are normally activated by a distinct hermaphrodite-specific activator to which male sperm are also competent to respond . Within males , TRY-5::GFP localization within the seminal vesicle is antagonized by the protease inhibitor SWM-1 . Together , these data suggest that TRY-5 functions as an extracellular activator of C . elegans sperm . The presence of TRY-5 within the seminal fluid couples the timing of sperm activation to that of transfer of sperm into the hermaphrodite uterus , where motility must be rapidly acquired . Our results provide insight into how C . elegans has adopted sex-specific regulation of sperm motility to accommodate its male-hermaphrodite mode of reproduction .
A general feature of sexual reproduction is the generation of motile sperm that can navigate to an egg . To assist this process , males transfer their sperm along with seminal fluid , which enhances their reproductive success in a variety of ways ( reviewed in [1] , [2] ) . Seminal fluid factors promote sperm survival , motility and fertilizing ability both by directly interacting with sperm and by interacting with tissues of the female to make her reproductive tract a more permissive environment . These factors include seminal fluid-specific proteins , a variety of hormones , and energy sources [2] . In mammals , roles for seminal fluid factors include the regulation of sperm motility and capacitation and the modulation of immune function [2] , [3] . Extensive analysis in Drosophila has identified many seminal fluid proteins and uncovered roles for several of these factors in sperm storage , sperm competition , female reproductive behavior and physiology , and other processes [4] . Due to their potential for influencing reproductive success , components of seminal fluid represent a forum for both conflict and cooperation between the sexes [1] , [5] . The androdioecious nematode Caenorhabditis elegans provides an opportunity to analyze sperm development and function in a context where both sexes produce sperm and can differentially regulate gamete function to promote their fertility . Hermaphrodites are self-fertilizing; during development , they produce a store of “self” sperm , which can be used to fertilize their eggs . Males mate with and transfer sperm to hermaphrodites . Males are not required for reproduction to occur , and in their absence self sperm are used with extremely high efficiency; more than 99% of self sperm are used . However , if male sperm are present , then they preferentially fertilize eggs [6] . C . elegans sperm , like those of other nematodes , lack flagella; instead , they move by crawling using a pseudopod [6]–[9] . Motility is acquired during sperm activation , a process analogous to spermiogenesis in flagellate sperm , in which haploid spermatids undergo a dramatic cellular rearrangement to become competent for both directional motility and fertilization of an oocyte [6] . While most aspects of sperm development are similar in males and hermaphrodites , the timing and context of activation differ in the two sexes . In hermaphrodites , spermatids activate when they move into the spermathecae , regions of the gonad where sperm are stored and fertilization occurs . In males , sperm are stored in a non-activated form and become activated after mating and transfer to a hermaphrodite ( [6] and unpublished observations ) . Sperm also can be activated in vitro in response to treatment with a variety of factors , including an ionophore ( monensin ) , proteases ( Pronase ) , a weak base ( triethanolamine/TEA ) , and an ion channel inhibitor ( 4 , 4′-diisothiocyano-2 , 2′-stilbenedisulfonic acid/DIDS ) [10]–[13] . This ability , together with the observation that sperm generally activate in vivo in response to a change in location , suggests that activation is controlled by extracellular signals . Genes that regulate sperm activation show distinct requirements in hermaphrodites and males . The activity of a set of five genes termed the “spe-8 group” ( spe-8 , -12 , -19 , -27 , and -29 ) is required specifically for hermaphrodites to activate their self sperm; hermaphrodites mutant for any one of these genes are self sterile , while mutant males are fertile [12] , [14]–[19] . Mating of spe-8 group mutant hermaphrodites with males results in self-sperm activation ( “transactivation” ) and can restore self fertility , suggesting that males provide their own activator to which spe-8 group hermaphrodite sperm can respond [12] . spe-8 group functions are dispensable for production of this activator , since both wild-type and spe-8 group males are competent for transactivating hermaphrodite sperm . While these analyses indicate that there are differences in the intracellular pathways by which sperm are activated in the two sexes , the functions of individual activation genes are not strictly limited to a specific sex . spe-8 group mutant male sperm show some defects , failing to activate in response to Pronase in vitro [12] , [15] , [17] , [19] . Furthermore , some spe-8 group activity is likely required for sperm to transactivate , since animals harboring spe-8 group null alleles appear to be insensitive to male activator [17] , [19] . While most analysis has focused on hermaphrodite sperm activation , a gene with a male-biased effect has been identified as well . Activity of an extracellular trypsin inhibitor-like protein , SWM-1 , is required in males to prevent premature activation from occurring prior to mating , and swm-1 mutant males are infertile due to failure to transfer activated sperm [20] . swm-1 activity is dispensable in hermaphrodites , though loss of swm-1 improves fertility in a sensitized spe-8 group mutant background [20] . The finding that a protease inhibitor regulates activation in males , combined with the ability of proteases to activate sperm in vitro , suggested that protease activity could signal activation in vivo . However , the endogenous activator has not been identified as yet in either sex . Here , we report the identification of a trypsin-family serine protease , TRY-5 , which has the properties expected of a male sperm activator . Loss of try-5 suppresses mutations in swm-1 . Furthermore , during mating , TRY-5 is released from the somatic gonad and transferred along with sperm , thus coupling the onset of sperm motility to the time of their transfer to a hermaphrodite . Within the male gonad , TRY-5 activity must be held in check to ensure male fertility . Strikingly , TRY-5 is not required for male fertility , but strains lacking both try-5 and spe-8 group activation functions are totally sterile , confirming that while male and hermaphrodite sperm motility is induced by distinct signals , the two pathways are redundant . In summary , TRY-5 is the first factor demonstrated to be a transferred component of seminal fluid in C . elegans , where it plays a key role in male-specific regulation of sperm function .
In wild-type males , sperm are stored in the inactive form within the seminal vesicle ( Figure 1A ) and become activated after transfer to a hermaphrodite ( [6] and unpublished data ) . Mutations in the secreted protease inhibitor SWM-1 result in premature sperm activation within males ( Figure 1B , 1A , [20] ) . We predicted that loss of activation-promoting factors should suppress this phenotype . To identify such factors , we performed genetic screens for suppressors of premature sperm activation caused by the partial loss-of-function alleles swm-1 ( me86 ) or swm-1 ( me66 ) ( G . M . S . , unpublished; [20] ) . Among the swm-1 suppressor mutants , we identified three alleles of the serine protease gene try-5 ( Figure 1E and 1F ) . We subsequently obtained tm3813 , a deletion affecting the 5′ end of the try-5 coding region ( gift of S . Mitani , National Bioresource Project , Japan ) , and showed that it also suppressed swm-1 ( me86 ) ( Figure 1F ) . Suppression of the premature activation phenotype in swm-1 try-5 double mutants was rescued by a genomic fragment containing the full-length try-5 gene ( Figure 1G , Tables S1 , S2 , S4 and data not shown ) , confirming that try-5 was responsible for this effect . In parallel to our forward genetic screen , we also tested individual serine proteases for a role in sperm activation . We used RNA interference to reduce the function of individual protease genes in a swm-1 mutant background and screened for effects on premature activation in males . Among the tested proteases , only reduction of try-5 resulted in strong suppression ( Materials and Methods and data not shown ) , consistent with our finding that try-5 is a regulator of sperm activation . Based on conservation of its sequence and domain structure [21] with those of the trypsin-like superfamily , try-5 is predicted to encode a trypsin-class serine protease . This family of proteases contains numerous members in eukaryotes and regulates many processes , including blood coagulation , developmental signaling and fertilization [22] . Specific residues that form the protease active site are conserved in TRY-5 , and the presence of a signal sequence on the N terminus of the protein suggests that it is secreted ( Figure S2 ) . While TRY-5 has clear orthologs in other closely related nematodes , it is divergent from serine proteases in more distantly related species ( data not shown ) . In addition , its substrate-binding region is divergent from those of trypsin family members with characterized substrate specificities [23] . We initially identified try-5 using partial loss-of-function alleles of swm-1 . To determine whether mutations in try-5 are capable of suppressing a swm-1 null , we examined animals harboring both the null allele swm-1 ( me87 ) and an allele of try-5 . We found that whereas swm-1 ( me87 ) mutant males contain activated sperm [20] , swm-1 ( me87 ) try-5 ( jn2 ) and swm-1 ( me87 ) try-5 ( tm3813 ) males contained non-activated sperm like those found in the wild type or in a try-5 mutant ( [20] , Figure 1A–1D and 1F , Figure S1 ) . In summary , these results indicate that the protease TRY-5 is responsible for the premature sperm activation and associated loss of fertility that occur in swm-1 mutant males and suggest that the function of SWM-1 is to inhibit TRY-5 activity within the male . To see if try-5 is required for male sperm to activate , we assessed the ability of try-5 mutant sperm to respond to treatments that bypass normal activation signals . Wild-type sperm can be activated in vitro by treatment with any of a variety of compounds [10]–[13] . Since TRY-5 is predicted to be a protease , we first assayed the ability of try-5 mutant spermatids to activate in response to Pronase treatment . In the absence of Pronase , both wild-type and try-5 mutant sperm remained non-activated ( Figure 2A and 2B ) . Within 5 to 10 min after addition of Pronase , the majority of sperm cells developed a pseudopod , consistent with activation ( Figure 2A and 2C , Video S1 ) . These cells were capable of motility , as they were observed crawling across the microscope slide ( note altered positions of some cells in Figure 2B versus Figure 2C ) . There was no significant difference in either the level of activation ( Figure 2A; P = 0 . 89 , Student's t test ) or the rate of activation ( data not shown ) of try-5 mutant sperm as compared to the wild type . We then tested the ability of try-5 ( tm3813 ) spermatids to activate in response to treatment with a second known activator , the weak base TEA . When treated with TEA , try-5 mutant spermatids activated at levels similar to wild-type sperm ( data not shown ) . Thus , try-5 is not required for sperm activation initiated in vitro either by exogenous proteases or TEA . This result distinguishes try-5 mutants from the previously-characterized spe-8 group mutants , for which sperm activate normally when treated with TEA , but arrest at a partially-activated , “spiky” stage in response to Pronase [12] , [15] , [17] , [19] . We next determined if try-5 is required for activation induced by loss of the intracellular activation inhibitor spe-6 . SPE-6 is a sperm casein kinase 1-like protein that functions at two points during spermatogenesis: during spermatogenic cell divisions [24] and later during sperm activation [18] . Specific mutations in spe-6 allow spermatogenesis to occur but lead to premature sperm activation in males , a phenotype that is thought to be independent of extracellular signaling [18] . To determine whether try-5 function is required for the premature sperm activation phenotype of spe-6 , we assayed sperm activation in spe-6 ( hc163 ) ; try-5 ( tm3813 ) and spe-6 ( hc163 ) ; try-5 ( jn2 ) mutant males . We found that , like spe-6 ( hc163 ) mutant males , spe-6 ( hc163 ) ; try-5 males contained activated sperm ( Table 1 ) and their appearance was indistinguishable from that of the spe-6 mutant ( data not shown ) . Thus , TRY-5 activity does not function downstream of the sperm protein SPE-6 . Together , the ability of try-5 sperm to activate in response to either in vitro activators or loss of an intracellular inhibitor indicates that TRY-5 is not required for the subcellular rearrangements of sperm activation . Rather , these data suggest a regulatory role for this protease in signaling sperm to initiate the activation process . Since activation is necessary to generate mature , motile spermatozoa that are competent for fertilization , failure to activate results in infertility . If try-5 is required for sperm activation , then loss of try-5 should result in decreased fertility . To test this idea , we assayed fertility in try-5 and swm-1 ( me87 ) try-5 hermaphrodites and males , using the try-5 alleles jn2 and tm3813 . In self-fertilizing hermaphrodites , sperm is the limiting gamete for offspring production; nearly every self sperm in a hermaphrodite will fertilize an oocyte [6] , so the total self brood size is a sensitive measure of the number of functional , activated sperm produced . We found no significant difference between the number of progeny produced by try-5 or swm-1 try-5 mutant hermaphrodites as compared to wild-type and swm-1 controls ( Figure 3A , Figure S3A ) . Thus , try-5 is not required for hermaphrodite sperm activation or fertility . We next measured male fertility in crosses of individual males to spe-8 ( hc40 ) ; dpy-4 recipient hermaphrodites . While there was a great deal of variation in the number of cross progeny produced even by wild-type males , as observed previously [25] , try-5 mutant males showed a high level of fertility and no significant difference with the wild type was observed ( Figure 3B ) . In addition , swm-1 try-5 males showed high levels of fertility , in some cases equivalent to that of the wild type ( Figure S3B ) , along with suppression of the swm-1 transfer defect ( data not shown ) . While our assays detected no obvious fertility defects in try-5 animals , it is possible that they might exhibit reduced fertility in other situations , e . g . , outside the laboratory or under conditions of sperm competition . However , these results suggest that try-5 is not required for sperm activation or other aspects of fertility in either sex . Although try-5 is not required for either male or hermaphrodite fertility , there is previous evidence for distinct pathways of sperm activation in males vs . hermaphrodites [12] , [14] , raising the possibility that the effect of try-5 loss is masked by functional redundancy . Therefore , we tested genes in the hermaphrodite pathway for redundancy with try-5 . The activities of a set of five genes termed the “spe-8 group” ( spe-8 , -12 , -19 , -27 , and -29 ) are required for self-sperm activation in the hermaphrodite but not for activation of male sperm ( reviewed in [26] ) . To test whether try-5 and the spe-8 group function in independent , redundant activation pathways , we assayed sperm activation and male fertility in worms lacking both try-5 and spe-8 group activity , using the spe-8 group mutations spe-27 ( it110 ) and spe-29 ( it127 ) . While spe-27 mutant males are fertile and capable of generating cross progeny , we found that spe-27; try-5 mutant males were completely infertile ( Figure 3C ) . Similarly , while spe-29 mutant males are fertile , spe-29; try-5 fertility was greatly reduced as compared to the wild type ( Figure 3C ) . To investigate the cause of this infertility , we labeled males with MitoTracker [27] and crossed them to unlabeled recipient hermaphrodites to assay sperm transfer and migration [20] . We found that spe-27; try-5 males were able to transfer sperm to hermaphrodites , but the transferred sperm did not migrate . Similarly , for spe-29; try-5 males , we observed only rare instances of successful migration ( Table S3 and data not shown ) . To determine if the migration defect was due to improper activation or a defect in migration after sperm activation , we dissected hermaphrodites immediately after their mating to spe-27; try-5 males and examined transferred , MitoTracker-labeled sperm . We found that whereas spe-27 sperm activate within fifteen minutes after transfer to a hermaphrodite , spe-27; try-5 sperm fail to activate ( data not shown ) . Thus , spe-27; try-5 males are infertile due to failure to activate sperm upon transfer to hermaphrodites . Our findings of residual fertility and sperm migration in spe-29; try-5 males are consistent with previous observations [17] that the single known mutation in spe-29 leads to a weaker phenotype as compared to known null mutations in other spe-8 group genes . These results suggest that try-5 activity is the source of fertility in spe-8 group mutant males; i . e . , the spe-8 group and try-5 function in two separate pathways for sperm activation , and either pathway is normally sufficient for full male fertility . To determine whether try-5 indeed functions in the male-derived activation pathway , we used a specific assay to measure transfer of functional male activator . Wild-type male seminal fluid is capable of activating spe-8-group mutant hermaphrodite sperm during mating; this process is termed “transactivation” and is generally assayed using fer-1 mutant males , which are defective for producing functional sperm , to prevent cross-progeny production [12] . We crossed either fer-1 [25] or fer-1; try-5 males to spe-8 ( hc53 ) ; dpy-4 hermaphrodites and counted the number of self progeny generated . Crosses with fer-1 control males resulted in transactivation approximately 58% of the time . However , fer-1; try-5 males were rarely if ever capable of transactivating hermaphrodite sperm ( Figure 4 ) . To exclude the possibility that fer-1; try-5 males simply might harbor a behavioral defect that reduced their mating frequency , we used MitoTracker to label males and assessed their ability to transfer sperm . We observed similar frequencies of hermaphrodites containing labeled sperm after incubation with fer-1 males , fer-1; try-5 ( jn2 ) males , or fer-1; try-5 ( tm3813 ) males ( 43% , 57% , or 63% , respectively ) . These data indicate that fer-1; try-5 mutants mate and transfer sperm with similar success rates as compared to the control . Thus , try-5 mutant males are defective in transfer of the male activator responsible for transactivation of spe-8 group hermaphrodite sperm . To determine how TRY-5 functions in male sperm activation , we sought to determine where it is expressed and localized . Since we predicted that TRY-5 protein is secreted , we generated both a Ptry-5::GFP::H2B transcriptional reporter , a histone-H2B fusion that localizes to cell nuclei and facilitates identification of cells , and a Ptry-5::TRY-5::GFP translational reporter for assessing TRY-5 protein localization and function . We created stable transgenic worm strains using MosSCI ( Mos1-mediated Single Copy gene Insertion [28] , Tables S1 and S2 ) and confirmed that the Ptry-5::TRY-5::GFP transgene restored a premature sperm activation phenotype to swm-1 try-5 mutants ( Materials and Methods , Tables S1 , S2 , S4 ) . Using the Ptry-5::GFP::H2B reporter , we found that the primary site of try-5 expression was in the male somatic gonad , in particular within tissues involved in storing sperm and tissues through which sperm pass during transfer to a hermaphrodite . The C . elegans male gonad is essentially a long tube . At the distal end of this tube , germline stem cells reside and proliferate , and as they move proximally , they undergo meiosis and differentiate into spermatids . A subset of somatic gonadal cells surround spermatids to form a storage organ , the seminal vesicle; a more proximal set forms a channel , the vas deferens , through which sperm move during transfer . A valve structure regulates movement of sperm between the seminal vesicle and vas deferens . The vas deferens contains at least two distinct cell types , based on shape: cuboidal and elongated cells [29] . Beyond an obvious structural role , other functions of these different cell types are not known , although some of them appear to be involved in secretion [29] . Starting at the L4 larval stage , when sperm production initiates , we observed Ptry-5::GFP::H2B expression in several regions of the male gonad ( Figure 5A ) : the seminal vesicle ( up to seven of the twenty-three total cells in this tissue [30] ) , the valve region ( four cells ) , and the twelve cuboidal cells of the vas deferens [29] . This overall pattern persisted into adulthood until at least 72 hr post L4; highest expression levels were present consistently in the valve region . We observed no expression in the hermaphrodite gonad , so gonadal expression of try-5 is sexually dimorphic . However , we also observed low levels of expression in a few cells within the head and tail of both males and hermaphrodites ( data not shown ) . In worms carrying the Ptry-5::TRY-5::GFP reporter , the TRY-5::GFP fusion protein exhibited a localization pattern consistent with secretion from the vas deferens . Within the valve and cuboidal cells , TRY-5::GFP was localized to globular foci . In L4 larvae , most globules aligned with the apical domain that lines the developing sperm channel ( Figure 5B ) . In mature adults , very large globules were present that tended to cluster apically , and additional small globules were present throughout the cytoplasm ( Figure 5C and 5D ) . Such large globular structures are generally visible in adult males by DIC microscopy and diagnostic of vas deferens tissue , including within wild-type animals lacking a transgene . Based on their size and location , these large globules are likely to represent the “secretory globules” observed by electron microscopy [29] . We sometimes observed TRY-5::GFP within the lumen of the seminal vesicle , likely as a result of release from the adjacent , highly-expressing valve cells ( Figure 5D and 5E , Table S5 ) . The timing and extent of TRY-5::GFP expansion into the seminal vesicle was dependent on activity of the protease inhibitor SWM-1 , the level of expression , and male age . In animals wild-type for swm-1 , TRY-5::GFP was usually restricted to the valve cells or regions close by; when present near sperm cells , TRY-5::GFP was usually localized to a few discrete foci ( data not shown ) . However , in animals lacking swm-1 activity , we often observed large zones of TRY-5::GFP extending from the valve and surrounding sperm in the seminal vesicle ( compare Figure 5C and 5D; see Table S5 ) . Even in swm-1 ( + ) animals , when high levels of TRY-5::GFP were present in the seminal vesicle , we almost always observed that sperm were activated ( Figure 5E , Tables S4 and S5 ) . Together , these data suggest that TRY-5 is produced by cells of the male somatic gonad and can induce sperm activation within males if it is released into the seminal vesicle . It has been observed previously that older wild-type males sometimes contain activated sperm [20] , and the finding that TRY-5::GFP is released into the seminal vesicle in older males provides a basis for this phenotype . Thus , these results support a model in which TRY-5 acts locally on sperm , either to signal their activation or to generate such a signal , and SWM-1 acts to inhibit the accumulation and/or activity of TRY-5 in the seminal vesicle . Since TRY-5 localization is consistent with secretion from the male gonad , we sought to determine whether TRY-5 is transferred during mating . We placed individual MitoTracker-labeled Ptry-5::TRY-5::GFP; try-5 ( tm3813 ) males with unc-52 hermaphrodites , monitored the males for mating behavior [31] , and acquired fluorescence images starting at or just before spicule insertion . We observed that TRY-5::GFP was transferred to hermaphrodites during mating ( Figure 6 and Video S2 ) . Shortly after spicule insertion , TRY-5::GFP was released from the vas deferens and transferred to the hermaphrodite ( Figure 6A and 6B ) . A brief pause without obvious transfer then occurred ( Figure 6C ) . Next , TRY-5::GFP was released from the valve cells and travelled rapidly through the vas deferens into the hermaphrodite ( Figure 6D and 6E ) . Movement of this valve pool was immediately followed by transfer of sperm ( data not shown ) . After transfer , the TRY-5::GFP signal dispersed throughout the uterus ( Figure 6F ) and remained visible near the vulva for several minutes , if eggs were not laid immediately . This stereotypical series of events occurred for all cases ( n = 5 ) in which the entire process was observed from spicule insertion to sperm transfer . We also observed a partial time course of five other matings , all of which were consistent with this sequence of events . To confirm that this behavioral sequence is not unique to this specific hermaphrodite genotype , we mated Ptry-5::TRY-5::GFP; try-5 ( tm3813 ) males to either unc-31 ( n = 4 ) or him-5 unc-76 ( n = 3 ) hermaphrodites . We were unable to observe vas deferens TRY-5::GFP transfer in these cases due to excess hermaphrodite movement . However , we did observe that valve TRY-5::GFP transfer initiated approximately 15–55 sec after spicule insertion , which is similar to the time observed for mating with unc-52 hermaphrodites ( Figure 6 , Video S2 and data not shown ) and consistent with the reported timing for sperm transfer from 14 . 4 to 90 . 2 sec after spicule insertion as determined by Schindelman [32] . In summary , our data suggest that TRY-5 is a seminal fluid protein that is transferred to the hermaphrodite during copulation . Furthermore , our observations indicate that seminal fluid is released in discrete pools from specific tissues of the male gonad and that these events occur largely prior to and coincident with transfer of sperm .
We have identified a serine protease , TRY-5 , which functions in C . elegans male sperm activation , the process by which amoeboid sperm cells become motile and competent to fertilize an egg . Based on our analysis of the defects of try-5 mutants and the dynamic localization of a TRY-5 reporter , we propose that TRY-5 is a sperm activating signal ( Figure 7A and 7C ) . TRY-5 function is required for premature activation of stored sperm in males lacking the protease inhibitor SWM-1 . TRY-5::GFP is expressed by the male somatic gonad within secretory cells . When observed outside these cells , localization of TRY-5::GFP protein strongly correlates with the localization of activated sperm . We have directly observed the transfer of TRY-5::GFP to hermaphrodites during copulation , and try-5 mutant males are incapable of transferring sperm activator to hermaphrodites . Together , these data strongly support a model in which TRY-5 is a component of seminal fluid that is transferred during copulation to signal sperm activation . Coupling the exposure of sperm to TRY-5 to the timing of transfer serves to ensure that sperm motility is rapidly induced at the time of - but not before - entry into a hermaphrodite's reproductive tract , thereby promoting male fertility . Our discovery of a seminal fluid serine protease provides a mechanistic explanation for previous results linking extracellular protease activity with sperm activation in C . elegans and in other nematodes . C . elegans sperm can be activated in vitro by incubation with Pronase , a protease preparation that primarily contains trypsin-like activity at the pH used for these assays [11] . In C . elegans males , loss of the SWM-1 protease inhibitor , which should result in increased protease activity , results in increased activation [20] . Recent studies of sex determination in C . remanei , a male-female species , showed that females could be transformed into sperm-producing “pseudohermaphrodites , ” but their sperm were not motile; production of functional , activated sperm could be achieved through additional inhibition of the C . remanei orthologue of swm-1 [33] . Finally , the somatic gonad of males from the related nematode Ascaris suum contains a protease activity , which can activate sperm [34] , [35] . Thus , a role for protease activity in promoting sperm motility appears to be conserved among nematodes . Here we describe a novel role for a protease as a signaling molecule for differentiation of sperm to a motile form . Why would a protease be used in this context ? The onset of motility in C . elegans sperm , as in flagellate sperm , occurs at a stage subsequent to meiotic cell division and the compaction of the haploid genome . At this stage , C . elegans sperm cells no longer express new protein products [26] . Therefore , to alter their behavior they must either reorganize their cellular contents in response to their environment or take in external factors . In addition , the timing of activation must be tightly controlled: C . elegans sperm must become motile rapidly upon entry into the hermaphrodite to avoid being lost due to the continuous outward passage of eggs [6] , but early activation of motility precludes transfer of sperm from the male [20] . A protease activator provides a mechanism to trigger irreversible changes in the sperm cell surface that is readily coupled to mixing of sperm with seminal fluid . This type of activator also provides a simple mechanism to hold activation in check: the use of specific protease inhibitors such as SWM-1 . We propose that the balance of TRY-5 and SWM-1 activities controls the likelihood of activation in specific locations and times within the male and hermaphrodite ( Figure 7 ) . For example , within the male gonad , SWM-1 may directly inhibit TRY-5 activity to prevent activation , allowing for sperm transfer and maintaining male fertility ( Figure 7A ) . It is likely that additional proteases and/or inhibitors also function in this process . try-5 mutant hermaphrodites are fertile , suggesting that hermaphrodites have an activator that is independent of TRY-5 ( Figure 7B ) . This activator could be a protease , though its identity is not known . Male sperm sometimes activate prematurely in try-5 mutants , suggesting that males could contain a second activator . However , if it exists , such a secondary male activator must not be competent to activate male or hermaphrodite spe-8 group sperm . Genetic analysis of swm-1 had suggested that it functions to inhibit two distinct protease activities that act in parallel to promote sperm activation within males [20] . This model was based on the result that partial loss-of-function mutations affecting each of the two TIL domains of SWM-1 partially complement one another . By this model , loss of a single protease would not be expected to block sperm activation . However , we find that all SWM-1 activity works through TRY-5 in males , suggesting that both domains of SWM-1 inhibit TRY-5 . The apparently separable activities of the SWM-1 TIL domains could arise from interactions with factors other than proteases . Alternatively , these results can be reconciled by a regulatory model in which SWM-1 inhibits two distinct proteases , both of which act upstream of TRY-5 . It is also possible that SWM-1 might inhibit both TRY-5 and a second , TRY-5-activating protease . Consistent with these ideas , many well-known protease pathways consist of sequential cascades of activator and effector functions ( e . g . , [36] , [37] ) . As an extracellular protease , TRY-5 likely signals activation by cleaving sperm cell surface proteins and altering their activity . Some of the targets of TRY-5 may be SPE-8 group proteins , based on the fact that TRY-5 is required for transactivation , a process dependent on having some spe-8 group activity ( sperm from hermaphrodites harboring null alleles of these genes are essentially incapable of being transactivated [16] , [19] ) . However , spe-8 group mutant males are fertile , suggesting that SPE-8 group proteins are not essential for activation in all contexts . Thus , other targets may not be members of the SPE-8 group . The existence of such targets is further supported by our finding of additional swm-1 suppressors ( distinct from try-5 ) that show full fertility in hermaphrodites and so do not fall into the spe-8 phenotypic class ( G . M . S . , unpublished data ) . Could TRY-5 be functioning in some role other than as a direct activator ? Sperm from try-5 mutant males can be activated within hermaphrodites after mating , in spe-6 mutants , or by exogenous activators in vitro . Thus , other activators can bypass TRY-5 , and try-5 is not required for the cellular rearrangements that occur after activation is triggered . These data support the idea that TRY-5 functions in a regulatory step of the activation process . It is clear that TRY-5 is essential for transfer of sperm activator by C . elegans males and its localization correlates strongly with that of activated sperm . These data strongly suggest that if TRY-5 is not the signaling molecule per se , its activity is intimately associated with generation of the sperm activation signal . Production and transfer of seminal fluid is an important aspect of male reproduction [2] , [38] , [39] . TRY-5 is one of the first seminal fluid proteins identified in C . elegans . Indeed , it is the first directly demonstrated to be transferred at mating , and the first with a specific role in promoting gamete function . Previously , plg-1 was identified as a seminal fluid factor required for production of a copulatory plug [40] and shown to encode a mucin-like protein with a function in male mate guarding [41] . plg-1 is expressed within the male somatic gonad in a subset of cells that express try-5; interestingly , plg-1 is not expressed within the valve region [41] , from which most TRY-5 appears to be released during mating ( Video S2 ) . Thus , as in other animals [42] , [43] , different regions of the C . elegans male gonad appear to be specialized to produce specific components of seminal fluid . Furthermore , our data reveal considerable complexity in the timing of release of seminal fluid from specific tissues during the mating behavioral program . We have found that try-5 is functionally redundant for fertility in C . elegans . Although try-5 mutant males fail to transfer activator , they are fertile; however , loss of both try-5 and spe-8-group function leads to complete infertility for both hermaphrodites and males ( tested here with mutations in two of the spe-8-group genes , spe-27 and spe-29 ) . These data can be explained by the following model: spe-27; try-5 and spe-29; try-5 animals ( 1 ) make sperm that do not respond to hermaphrodite activator ( due to loss of spe-8-group function ) and ( 2 ) do not produce male activator ( due to loss of try-5 ) . In other words , try-5 males may be fertile due not to the presence of additional activators provided by the male , but rather due to rescue of male sperm activation by a signal within the hermaphrodite ( Figure 7C ) . These findings of redundancy raise the question: why does C . elegans have try-5 ? At least part of the answer might lie in the evolutionary history of this species , which evolved from a gonochoristic ( male-female ) ancestor [44] , [45] . As the male activator , try-5 may represent the ancestral mode of activating sperm . Baldi et al . [33] have shown that the transition from gonochorism to androdioecy in the related species C . remanei requires only two steps: making sperm and activating it . Acquisition of the ability to make sperm could be advantageous , even in the initial absence of a robust self-sperm activation mechanism , as long as it tended to increase fertility . Chance encounters with a male would potentially activate hermaphrodite self sperm , as long as hermaphrodite sperm remained capable of responding to male activator . In turn , the male may have developed mechanisms to ensure his sperm were used preferentially; indeed , C . elegans male sperm show strong precedence over those of the hermaphrodite [6] , [46] . Eventually , the hermaphrodite might evolve her own mechanism for activating sperm . The self-sperm activator in C . elegans is not known , but it may be a serine protease . Indirect evidence for this idea is provided by data indicating that the inhibitor SWM-1 functions in hermaphrodites: while animals mutant for the spe-8 class gene spe-29 have very low levels of self sperm activation and fertility , this phenotype is partially suppressed by mutations in swm-1 [20] . However , this protease is likely distinct from TRY-5 , since we have found that try-5 is not required for either normal hermaphrodite fertility or increased activation in spe-29; swm-1 hermaphrodites ( Figure 3A , Figure S4 ) . Alternatively , production of TRY-5 would be advantageous for males if it is a more efficient activator than that of hermaphrodites . While our fertility assays revealed no difference between fertility of wild-type and try-5 males , those assays were performed under highly permissive conditions: young adult animals were provided with many opportunities for mating to occur under conditions of unlimited food resources . TRY-5 might be important to increase reproductive fitness in less-than-ideal conditions . For example , activation by TRY-5 might occur more rapidly than that mediated by the hermaphrodite activator . If so , its transfer would decrease the chance that transferred sperm would be lost before they have the opportunity to migrate away from the vulva . In summary , our work has identified a serine protease in C . elegans male seminal fluid that regulates the timing of sperm activation to promote male fertility . TRY-5 is transferred along with sperm during mating to couple sperm motility to entry into the hermaphrodite reproductive tract . While TRY-5 appears to be necessary for males to signal activation , hermaphrodites contain their own activator . Interestingly , these redundant pathways are competent to activate sperm from either sex , providing insight into the strategies used by C . elegans to adopt a male-hermaphrodite mode of reproduction . Further dissection of these signaling pathways will require identifying targets of TRY-5 and determining the nature of the hermaphrodite activator .
C . elegans strains were grown as described by Brenner [47] at 20°C , except where otherwise noted . All strains were derived from the wild-type isolate Bristol N2 . To ensure a ready supply of males , a strain harboring the mutation him-5 ( e1490 ) [48] was used as the wild type and him-5 ( e1490 ) was present in all other strains unless explicitly noted . The try-5 alleles jn2 and jn13 were isolated as suppressors of swm-1 ( me86 ) and jn21 was isolated as a suppressor of swm-1 ( me66 ) ( G . M . S . , unpublished results ) . Ethyl methanesulfonate ( EMS ) mutagenesis was performed as in [49] . try-5 ( tm3813 ) was a gift of S . Mitani ( National Bioresource Project , Japan ) . Other alleles ( described in Wood [49] unless otherwise noted ) were: spe-8 ( hc40 , hc53 ) I , fer-1 ( hc1ts ) I , ttTi5605 II [28] , unc-52 ( e444 ) II , dpy-18 ( e364 ) III , spe-6 ( hc163 ) III [18] , unc-119 ( ed3 , ed9 ) III [50] , spe-27 ( it110 ) IV [15] , spe-29 ( it127 ) IV [17] , dpy-20 ( e1282 ) IV , mIs11[myo-2::GFP , pes-10::GFP , gut::GFP] IV , dpy-4 ( e1166 ) IV , unc-31 ( e169 ) IV , swm-1 ( me66 , me86 , me87 ) V [20] , unc-76 ( e911 ) V and nT1[unc- ? ( n754 ) let- ? qIs50 ] ( IV , V ) . Strains containing mutations in both a spe-8 group gene and try-5 were maintained as heterozygotes using the balancer nT1 . Homozygous spe-8 group; try-5 males were generated by transactivation crosses of homozygous self-sterile hermaphrodites to swm-1 mutant males , which are competent for transferring seminal fluid but rarely transfer sperm [20] . For example , for the spe-27 dpy-20/nT1; try-5 him-5/nT1 strain , homozygous spe-27 dpy-20; try-5 him-5 hermaphrodites were selected and crossed to either swm-1 ( me87 ) him-5 or mIs11; swm-1 ( me87 ) him-5 males to induce production of self progeny , which can be recognized as being phenotypically Dumpy . To screen C . elegans proteases for a function in sperm activation , RNAi against individual protease genes was performed on swm-1 him-5 worm strains by feeding on agar plates essentially as described by [51] . Bacteria containing inducible RNAi clones ( described in [52] , [53] ) were obtained from Source BioScience . Genes tested by RNAi were try-1 , try-2 , try-3 , try-5 , try-6 , try-7 , try-8 , F25E5 . 3 , F25E5 . 4 , F25E5 . 7 , and F48E3 . 4 . For each gene , swm-1 ( me66 ) him-5 and swm-1 ( me86 ) him-5 eggs were collected on RNAi plates and allowed to grow to the L4 stage; L4 males were then transferred to a fresh RNAi plate and scored either 24 hr or 48 hr later for sperm activation . Sperm activation was assayed in virgin males collected as L4 larvae and incubated at 20°C for 48 hr , unless otherwise indicated . To examine individual sperm cells , males were dissected in sperm medium ( SM ) ( 5 mM HEPES sodium salt pH 7 . 4 , 50 mM NaCl , 25 mM KCl , 5 mM CaCl2 , 1 mM MgSO4 ) supplemented with 10 mM dextrose [10] . Samples were observed using differential interference contrast ( DIC ) microscopy and sperm were scored based on cell shape as either non-activated , if spherical , or activated , based on the presence of a pseudopod . Samples were observed using an AxioImager M1 equipped with an AxioCam MRm ( Zeiss ) . Confocal imaging was performed using a TCS SP2 ( Leica ) . Images were processed using ImageJ [54] and Photoshop ( Adobe Systems ) . Hermaphrodite self fertility was measured by picking individual hermaphrodites , transferring them to fresh plates every 1–2 days until no more eggs were laid , and counting the total progeny after worms reached the L4 stage . Cases in which hermaphrodites failed to lay oocytes or died less than four days after adulthood were excluded from analysis . Male fertility was measured in 1∶1 crosses to spe-8 ( hc40 ) ; dpy-4 hermaphrodites . L4 stage animals were placed together for 48 hr; hermaphrodites were then transferred to fresh plates without males and transferred again every 1–2 days until no more eggs were laid . All cross progeny , identifiable by their non-Dumpy phenotype , were counted after worms reached the L4 stage . Use of the spe-8 mutation in recipient hermaphrodites allows for detection of mating even in cases where functional sperm are not transferred , since transfer of seminal fluid leads to production of self progeny [14] , [20] . Cases in which mating was not confirmed or the hermaphrodite died less than three days after adulthood were excluded from analysis . For all fertility assays , wild-type broods were measured in parallel to those of the strain being assayed to control for variations in temperature , media quality and other factors that can affect progeny production or mating efficiency . To assay sperm transfer and migration , males were labeled with 1 µg/mL MitoTracker CMXRos ( Invitrogen ) as described by Chen et al . [27] and observed as described previously [20] . Seminal fluid transfer ( transactivation , [12] ) was assayed using males harboring the fer-1 ( hc1ts ) mutation , which results in non-functional sperm at the restrictive temperature of 25°C [25] . L4 males were crossed in a 4∶1 ratio to L4 spe-8 ( hc53 ) ; dpy-4 hermaphrodites for 48 hr at 25°C . The number of self progeny ( Dumpy offspring ) produced during the mating period was determined after three additional days . Any crosses resulting in cross progeny ( non-Dumpy offspring ) were excluded from analysis . All other crosses with recipient worms surviving to the end of the mating period were included , because no marker for successful mating is available for this assay . To assess mating frequency in different fer-1 mutant strains , males were labeled with MitoTracker and incubated with hermaphrodites in 1∶1 crosses . Hermaphrodites were then examined after 5 hr for the presence of labeled sperm . This assay likely underestimates the total mating frequency in transactivation assays , since 1 ) fer-1 sperm can not migrate and are only retained within hermaphrodites for a short time period , and 2 ) a higher ratio of 4 males:1 hermaphrodite was used for transactivation assays . Activation assays were performed essentially as in [12] . Adult virgin males were dissected to release sperm in a drop of SM on a glass slide; a chamber was formed over the cells using a coverslip supported by a thin layer of Vaseline; additional SM either with activator ( 200 µg/mL Pronase or 60 mM TEA ) or without it ( control ) was wicked through this chamber; and the coverslip was completely sealed with Vaseline . An image was obtained immediately upon wicking through activator and subsequent images were obtained every 5 min for at least 25 min . Activation was scored at each time point based on cell shape . To obtain time-lapse videos , activation assays were performed as described except that images were obtained once per minute . For each trial , one to two 24 hr post-L4 Ptry-5::TRY-5::GFP; try-5 ( tm3813 ) him-5 males were placed at the center of a circle of ten unc-52 , unc-31 or unc-76 him-5 virgin adult hermaphrodites . Males were observed for 10 min under transmitted light using a Leica MZ16FL microscope . Prior to or shortly after spicule insertion occurred , the light source was switched to epifluorescence and images were collected at maximum speed ( an exposure time of approximately 300 msec ) using an AxioCam MRm ( Zeiss ) until spicules were removed . If copulation was not attempted within 10 min , males were removed and replaced with fresh males . Standard molecular biology protocols were used [55] . RNA was extracted from mixed-stage him-5 worms using TRIzol ( Invitrogen ) . Reactions for 5′- and 3′-RACE ( rapid amplification of cDNA ends ) were performed using GeneRacer ( Invitrogen ) . The MultiSite Gateway Three-fragment Vector Construction Kit ( Invitrogen ) with pCFJ150 as the destination vector [28] was used to generate MosSCI donor constructs ( Tables S1 and S2 ) . Plasmid pCM1 . 35 was a gift from G . Seydoux [56] . For TRY-5::GFP , fusion PCR was performed as in [57] . Details of Gateway plasmid construction are listed in Table S1 and Table S2 . To generate pJRS17 , the 279 bp KpnI-XhoI fragment from pPD95 . 85 was ligated into the 4855 bp KpnI-XhoI fragment from pJRS11 , thereby replacing the Ser65Cys variation present in GFP derived from pPD95 . 75 with the Ser65Thr variation from pPD95 . 85 . To generate transgenic strains harboring extrachromosomal arrays , constructs were injected [58] into the strain unc-119; swm-1 ( me86 ) try-5 ( jn2 ) him-5 and transgenic lines were selected based on rescue of the Unc-119 phenotype [28] , [50] . Single-copy insertion ( MosSCI ) strains were generated by the direct insertion technique into the Mos1 insertion site ttTi5605 as described by Frokjaer-Jensen [28] . Targeting constructs were coinjected with Pglh-2::transposase as the source of Mos transposase and coinjection markers labeling pharyngeal muscle ( Pmyo-2::mCherry ) , body wall muscle ( Pmyo-3::mCherry ) , and neurons ( Prab-3::mCherry ) [28] .
|
Sexual reproduction requires the generation of highly specialized gametes , eggs and sperm , that must encounter one another and fuse together to form a zygote . Males provide not only sperm but also seminal fluid , which contains a variety of factors that promote male fertility through effects on sperm and on female physiology . We have identified a C . elegans seminal fluid protease , TRY-5 , that regulates sperm activation , the process by which immature spermatids complete their differentiation to a motile form capable of fertilizing an oocyte . We observed release of TRY-5 that coincided with transfer of sperm , coupling the onset of sperm motility to transfer during mating . Although TRY-5 functions only in males , both male and hermaphrodite sperm are capable of responding to it . TRY-5 is not required for fertility , and we propose that a hermaphrodite activator compensates in its absence . Our results reveal how sperm development can be differentially modulated by males and hermaphrodites to promote fertility in each sex , and we identify a novel function for a seminal fluid protein .
|
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"and",
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2011
|
TRY-5 Is a Sperm-Activating Protease in Caenorhabditis elegans Seminal Fluid
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The dengue virus ( DV ) is an important human pathogen from the Flavivirus genus , whose genome- and antigenome RNAs start with the strictly conserved sequence pppAG . The RNA-dependent RNA polymerase ( RdRp ) , a product of the NS5 gene , initiates RNA synthesis de novo , i . e . , without the use of a pre-existing primer . Very little is known about the mechanism of this de novo initiation and how conservation of the starting adenosine is achieved . The polymerase domain NS5PolDV of NS5 , upon initiation on viral RNA templates , synthesizes mainly dinucleotide primers that are then elongated in a processive manner . We show here that NS5PolDV contains a specific priming site for adenosine 5′-triphosphate as the first transcribed nucleotide . Remarkably , in the absence of any RNA template the enzyme is able to selectively synthesize the dinucleotide pppAG when Mn2+ is present as catalytic ion . The T794 to A799 priming loop is essential for initiation and provides at least part of the ATP-specific priming site . The H798 loop residue is of central importance for the ATP-specific initiation step . In addition to ATP selection , NS5PolDV ensures the conservation of the 5′-adenosine by strongly discriminating against viral templates containing an erroneous 3′-end nucleotide in the presence of Mg2+ . In the presence of Mn2+ , NS5PolDV is remarkably able to generate and elongate the correct pppAG primer on these erroneous templates . This can be regarded as a genomic/antigenomic RNA end repair mechanism . These conservational mechanisms , mediated by the polymerase alone , may extend to other RNA virus families having RdRps initiating RNA synthesis de novo .
Most RNA viruses maintain the specific sequences present at the ends of their genomes . The 5′ genome end may carry a cap structure to ensure both genome stability and efficient translation [1] . The 3′-end may carry a poly ( A ) tail or adopt specific 3′-end sequences required for viral replication [2] , [3] . They are generally copied exactly to avoid loss of genetic information , and have supposedly evolved towards optimum replication efficiency . Terminal genome damage can be caused by errors introduced by the viral polymerase during initiation and termination , or by cellular ribonucleases [4] . In addition to special mechanisms to ensure efficient initiation of RNA synthesis , viruses have evolved mechanisms to repair or correct damaged extremities such as the use of abortive transcripts as primers , the generation and use of non-templated primers , and the addition of one or few non-templated nucleotides to the 3′-end by a terminal transferase activity [4] . However , our knowledge about these mechanisms is still very limited . Many RNA virus polymerases , which do not use a primer and thus initiate RNA synthesis de novo , generate abortive transcripts during the initiation phase of RNA synthesis [5] , [6] , [7] . Primer-mediated repair of template extremities was so far only demonstrated for the positive-strand RNA ( +RNA ) turnip crinkle virus ( TCV ) [8] . Non-templated primer synthesis by the viral polymerase might be involved in the repair mechanism of TCV [9] . Such mechanism was also proposed as the molecular basis of the reconstitution of 5′-ends of negative-strand RNA ( -RNA ) respiratory syncytial virus ( RSV ) replicons [10] . In this study we demonstrate how the dengue virus ( DV ) RNA-dependent RNA polymerase ( RdRp ) , which starts RNA synthesis de novo , plays a decisive role in the nucleotide conservation of viral RNA ends . DV belongs to the Flavivirus genus within the +RNA virus family of Flaviviridae together with viruses of the genera Hepacivirus and Pestivirus [11] . The Flavivirus genus comprises around 50 virus species [12] including major human pathogens such as DV , yellow fever virus ( YFV ) , West Nile virus ( WNV ) and Japanese encephalitis virus ( JEV ) . Flaviviruses harbour the RdRp activity in the C-terminal domain ( amino acids 272–900 ) of non-structural protein NS5 [13] , [14] , [15] , [16] , [17] . The N-terminal domain contains methyltransferase activities involved in RNA capping [18] , [19] . Evidence has been presented that the N-terminal domain of NS5 also harbours the central RNA capping guanylyltransferase activity [20] . The structure of full-length NS5 is not known but several structures of methyltransferase domains have been determined ( for review see [21] ) . Likewise , crystal structures of Flavivirus NS5 RdRp domains have been determined for DV [16] and WNV [22] . All structurally characterized viral RdRps so far adopt the basic fold of the SCOP superfamily of DNA/RNA polymerases . As the other subgroups of this superfamily , DNA-dependent DNA polymerases ( DdDp , prototype Klenow fragment of the E . coli DdDp I ) , RNA-dependent DNA polymerase ( prototype HIV reverse transcriptase ) and DNA-dependent RNA polymerases ( DdRp , prototype bacteriophage T7 DdRp ) , their apo-structure is usually likened to a right hand comprising fingers , palm and thumb subdomains . Viral RdRps contain an encircled active site having connecting elements between the fingers and thumb subdomains . Active sites of viral RdRps performing de novo RNA synthesis are additionally closed in their initiation conformation due to the existence of structural elements allowing the stable positioning of the first NTP into a priming site [23] , [24] . All Flaviviridae RdRps studied so far initiate RNA synthesis de novo . Accordingly , Flavivirus RdRp domain structures contain a “priming loop” in the thumb subdomain closing the catalytic site [16] , [22] . The putative priming loop of DV RdRp was defined as comprising residues 792 to 804 . Of particular interest are two aromatic residues near the tip of the loop , W795 and H798 , which are conserved in all Flavivirus RdRps . They might play the role of an initiation platform to which the base of the priming NTP stacks as it was shown for bacteriophage φ6 [23] and proposed for HCV and BVDV RdRps [25] , [26] . Structures of DV RdRp in complex with 3′dGTP as well as two models of de novo initiation complexes of DV and WNV RdRps favor Trp795 in the role of the initiation platform [16] , [22] . Genomes of Flaviviridae lack a poly ( A ) tail at the 3′-end . A remarkable trait of Flavivirus genomes is the strict conservation of the 5′- and 3′-end dinucleotides as 5′ AG…CU 3′ . The molecular basis for this strict conservation of the 5′- and 3′-end dinucleotides and/or the use of the same starting nucleotide for +RNA and -RNA strand synthesis by the viral polymerases is not known . Its Hepacivirus and Pestivirus counterparts have to display higher nucleotide tolerance . They are able to initiate with ( A/G ) C and G ( G/U ) , respectively , since the 5′- and 3′-ends of Hepacivirus genomes of different genotypes correspond to 5′ ( A/G ) C…GU 3′ and the genomes of pestiviruses to 5′ GU…CC 3′ . Interestingly , genomes and antigenomes of non-segmented -RNA ( ns-RNA ) paramyxoviruses , whose RdRps perform de novo RNA synthesis , start with a conserved 5′-AC [10] . Here we show that the strict sequence conservation of Flavivirus genome ends is entirely polymerase-encoded . We demonstrate ATP-specific de novo initiation using the RdRp domain of DV protein NS5 ( NS5PolDV ) and specific 10-mer oligonucleotidic RNA templates corresponding to the 3′-end of genomic +RNA and -RNA . We document the existence of a built-in ATP-specific priming site of NS5PolDV . This specific site is one of the means by which NS5PolDV ensures that the DV genome and antigenome start with an A , the others being several correction mechanisms including the generation of non-templated pppAG primers as well as the preferential formation and elongation of pppAG even on templates with non-cognate 3′-ends . Finally , we show that the ATP-specific priming site is part of the putative priming loop coming from the thumb subdomain . There , residue H798 , and not W795 , is essential for de novo initiation and may act as a priming platform stabilizing the ATP priming nucleotide . DV RdRp is actively involved in the conservation of the correct ends of the genome proving thus a direct example of how RNA viruses maintain the integrity of their genomes . The mechanisms described here may more broadly apply to other RNA viruses having viral RdRps able to initiate RNA synthesis de novo .
We set out to study primer synthesis by the RdRp domain of dengue virus protein NS5 ( NS5PolDV ) using small specific templates corresponding to the 3′-ends of the genome ( +RNA ) and the antigenome ( -RNA ) . Templates are comprised of 10 nucleotides and are predicted to be devoid of stable secondary structure ( see Materials and Methods ) . Both templates end with the dinucleotide 5′-CU-3′ . Product formation over time was followed using either ATP and GTP , or all NTPs needed to form a full-length product when synthesis is precisely started at the 3′-end of the template . Figure 1 shows reaction kinetics of RNA synthesis on DV103′+ corresponding to the 3′-end of the RNA genome 5′-AACAGGUUCU-3′ ( left ) and on DV103′- corresponding to that of the antigenome 5′-ACUAACAACU-3′ ( right ) . We used either [α-32P]-GTP ( αGTP , panel A ) or [γ-32P]-ATP ( γATP , panel B ) as the radioactive nucleotide . For the catalytic ion , either Mg2+ ( panel A ) or Mg2+ supplemented with Mn2+ ( panel B ) were used at their optimum concentrations 5 mM for Mg2+ and 2 mM for Mn2+ [14] . Reactions with ATP and GTP render time-dependent accumulation of a short product migrating below the marker G2 ( see panel B ) . Comparison with authentic unlabeled pppAG ( see Materials and Methods ) visualized using UV-shadowing indicated that it indeed corresponds to pppAG ( not shown ) , the expected product of the first step of de novo RNA synthesis . When DV103′+ is used as a template , pppAG is formed as well as pppAGA and pppAGAA . When all NTPs are used , pppAG accumulates with time as does pppAGA in the case of DV103′+ and pppAGU in the case of DV103′- . After the synthesis of trinucleotides NSPolDV adopts a processive RNA synthesis elongation mode to continue synthesis up to full-length products ( labeled by asterisks in Figure 1 ) . As we had observed before [14] , when using Mn2+ the reaction is much more efficient and allows for the use of [γ-32P]-ATP ( γATP ) as radiolabeled nucleotide in order to visualize exclusively de novo RNA synthesis products starting with ATP . The pattern observed with Mg2+ is reproduced when Mn2+ is present ( Figure 1B ) . One difference is that the use of Mn2+ results in longer full-length products , which might be caused by an alteration of the terminal nucleotide transferase activity of NS5PolDV [14] , [27] , [28] . In conclusion , using RNA templates mimicking viral sequences , dinucleotide and trinucleotide products are formed during initiation and before processive RNA elongation , the most abundant being the dinucleotide pppAG . The first nucleotide of Flavivirus genomes is an adenosine , followed by a guanosine . This 5′-pppAG sequence is strictly conserved along the Flavivirus genus . In order to answer the question whether the polymerase ( and/or the correct template ) is at the origin of the conservation of the first nucleotide , we tested a set of DV103′- variants with different 3′-ends . In addition to the correct DV103′- CU , we used DV103′- CC , DV103′- CA and DV103′- CG in the presence of the corresponding priming NTP and GTP . The expected primer products are pppAG , pppGG , pppUG and pppCG , respectively . Figure 2A compares end points of reactions performed in the presence of αGTP and Mg2+ as the catalytic ion . Remarkably , the CU template only is proficient for product synthesis ( pppAG ) . RNA primer synthesis on other templates is almost undetectable . We conclude that in the presence of Mg2+ as a catalytic ion the DV RdRp priming-site accommodates exclusively ATP . To our surprise , when Mn2+ was used instead of Mg2+ , the pppAG primer was generated even in the absence of the template , albeit to a lower extent ( Figure 2B ) . This is not the case in the presence of Mg2+ even at ten-fold higher enzyme concentration ( see below Figure 3B ) . When using Mn2+ and the DV103′- template variants , we therefore included control reactions in the absence of corresponding templates and in the presence of γGTP , which allows exclusive detection of dinucleotides starting with pppG . Figure 2C shows corresponding reaction kinetics with Mn2+ as the catalytic ion in the absence or the presence of templates using αGTP or γGTP as the radioactive nucleotide . Again , using DV103′-CU and ATP/GTP , NS5PolDV generates pppAG to a higher extent than without template . Note that no pppGA product is generated . When DV103′-CC and GTP is used , NS5PolDV synthesizes pppGG in the presence of the template only . DV103′-CA , UTP , and GTP lead to the formation of pppUG and pppGU ( see γGTP control reaction ) , the latter by initiation internal to the template . No product is formed in the absence of the template . Finally , DV103′-CG allows formation of pppCG which is not formed in the absence of the template . In conclusion , NS5PolDV keeps the strict preference for an ATP as the priming nucleotide in the presence of Mn2+ when no template is present . Nevertheless , the use of templates with an altered 3′-nucleotide can force NS5PolDV to start the de novo RNA synthesis with the corresponding base-paired priming nucleotide , and also allows internal initiation . Collectively , these observations confirm that the priming site of NS5PolDV has a marked specificity for ATP . This preference is strict in the presence of Mg2+ . It is equally strict for dinucleotide synthesis in the presence of Mn2+ and in the absence of template . The specificity for ATP as the starting nucleotide is lost when Mn2+ is used in the presence of templates with incorrect 3′-ends; only then NS5PolDV is able to form pppNG products as efficiently as pppAG . In the presence of Mg2+ and/or Mn2+ the built-in ATP-specific priming site drives NS5PolDV-mediated RNA synthesis starting with pppA . The dinucleotide pppAG is accumulated during RNA synthesis on templates with the correct 3′-end ( see Figure 1 ) . Using Mn2+ this pppAG primer is also formed in the absence of an RNA template . We asked the question whether NS5PolDV forms and/or elongates pppAG even on templates with incorrect 3′-nucleotides thus enabling to repair incorrect 3′-ends . First , pppAG formation was tested on the four DV103′- variants in the presence of only ATP and GTP . Figure 3A shows that NS5PolDV is indeed able to form pppAG in the presence of templates with any 3′-nucleotide and Mn2+ . In contrast , in the presence of Mg2+ only the natural DV103′- CU template supports pppAG formation even in the presence of an increased concentration of NS5PolDV ( Figure 3B ) . We then tested pppAG formation exclusively in the presence of Mn2+ on all DV103′- variants in the presence of all nucleotides , a scenario putatively mimicking the situation within the replication complex . Figure 3C shows that pppAG is always formed in parallel to the dinucleotide , which corresponds to the template . In the case of the template variant with a -CG 3′-end , pppAG is produced with even higher efficiency than the base-paired dinucleotide . Note that the dinucleotide pppGU is also produced on all templates by internal initiation . For the reaction in the presence of all templates and all nucleotides , we quantified all products , which were initiated de novo over the very 3′-end , and found that pppAG is formed as the prominent product ( 32 . 3±1 . 5% , three independent reactions ) . Note that all templates are present at the same concentration , which should not correspond to the situation in vivo . We conclude that in the presence of incorrect templates and Mg2+ , NS5PolDV discriminates against these templates and forms pppAG only on the correct template ( see also Figure 2A ) . In contrast , Mn2+ ions enable NS5PolDV to preferentially generate pppAG even in the presence of incorrect templates , which could represent an indirect way of 3′-end repair . We then considered the elongation of the correct pppAG primer over templates with incorrect 3′-ends . We thus tested the elongation of a chemically synthesized pppAG primer ( see Materials and Methods ) either without template or in the presence of the four DV103′- variants ( Figure 4 ) . The most prominent result is that NS5PolDV is able to productively elongate pppAG on the correct template in the presence of Mn2+ ( Figure 4A ) and Mg2+ ions ( Figure 4B ) . We also observe that NS5PolDV in the presence of Mn2+ is able to productively elongate pppAG on incorrect templates ( Figure 4A ) , thus demonstrating that the enzyme is able to indirectly correct the error in the template and conserve the 5′-end of the DV genome . Note that as expected there is no primer elongation detectable in the absence of a template . NS5PolDV harbors an ATP-specific priming site , which is essential for the formation , accumulation , and elongation of the correct primer pppAG . Which elements of NS5PolDV form this site ? The crystal structure of NS5PolDV ( Figure 5A ) allowed the prediction of a priming loop comprising residues 792 to 804 [16] , which is expected to provide the priming site during de novo RNA synthesis initiation . We generated a deletion mutant ( NS5PolDV TGGK ) by replacing residues T794-A799 between T793 and K800 by two glycines ( see close-up in Figure 5A ) . The overall correct folding of the purified , recombinant mutant protein was verified by a fluorescent thermal shift assay giving identical temperatures of denaturation ( melting temperature Tm ) for both proteins ( wild type ( wt ) NS5PolDV Tm 49 . 0°C ± 0 . 5°C , NS5PolDV TGGK Tm 48 . 4°C ± 0 . 05°C ) . The TGGK mutant is expected to have an open active site , which impedes correct ATP-specific de novo initiation over the 3′-end of a single-stranded RNA template but may favor the accommodation of double-stranded RNA . Its RNA synthesis initiation and elongation activity was first tested using a “minigenomic” RNA template consisting of 224 nucleotides of the 5′-end of the DV genome fused to 492 nucleotides of the 3′-end [14] . It has been shown before using this template and analyzing the products on a denaturing agarose-formaldehyde gel [29] that two types of product are formed ( see wt reaction kinetics in the center panel of Figure 5B ) . Firstly , the de novo RNA synthesis product is generated corresponding to the size of the template . Secondly , an elongation product is generated by back-primed RNA synthesis . There , the 3′-end ( …AACAGGUUCU-3′ ) forms a short hairpin annealing the last di-nucleotide to nucleotides -6 and -7 ( underlined in the sequence ) and is then elongated [29] . The length of the product is thus ∼twice the size of the template . Reactions were carried out using either Mg2+ or Mn2+ as catalytic ions . The left and right panels of Figure 5B show that in both cases the mutant TGGK shows an increased overall activity on this template compared to wt activity . The center panel shows that this is mainly caused by increased back-priming . Interestingly , instead of one product species of twice the template size NS5PolDV TGGK produces a range of elongated products of different lengths . This might be due to the accommodation of long hairpins , which then create longer products than the template but shorter than the elongation product of wt NS5PolDV . De novo RNA synthesis initiation by wt NS5PolDV and the TGGK mutant were then tested on DV103′- , in the absence of a template and on DV103′+ using Mn2+ as the catalytic ion , ATP and GTP containing αGTP . Figure 5C ( panel 1 ) shows that in contrast to wt NS5PolDV , NS5PolDV TGGK is not able to catalyze de novo initiation on DV103′- . Secondly , NS5PolDV TGGK does not catalyze pppAG formation without template ( panel 2 ) . In contrast , it is able to catalyze de novo initiation on DV103′+ presenting ca . 32% of wt activity ( panel 3 ) . In order to understand this apparent contradiction , we used γATP instead of αGTP as radioactive NTP . It became clear that NS5PolDV TGGK was unable to generate the pppAG primer product ( panel 4 ) . We conclude that the product observed with αGTP corresponds to pppGA formed by internal de novo initiation being only possible on DV103′+ . When using Mg2+ as catalytic ion again we did not observe formation of the de novo RNA synthesis initiation product pppAG on either template ( for DV103′- see below Figure 6B ) . We conclude that NS5PolDV TGGK is unable to pre-form the ATP-specific priming site necessary for de novo RNA synthesis initiation at the very 3′-end . The predicted priming loop plays indeed an essential role in providing the correct priming site . We explain the increased activity of NS5PolDV TGGK on minigenomic RNA templates by its increased propensity to catalyze back-priming due its more accessible catalytic site , i . e . to harbor the minigenome in different hairpin conformations allowing 3′ elongation . Two aromatic residues , W795 and H798 , within the priming loop were proposed to play a particular role in providing an initiation platform to which the base of the priming ATP could establish a stacking interaction [16] . Residue W795 was given special attention because it was found near the triphosphate moiety of a 3′-dGTP bound to NS5PolDV [16] . In addition , this tryptophan was better placed than the histidine for stacking a priming ATP in two models of de novo RNA synthesis initiation complexes of NS5PolDV and NS5PolWNV [16] , [22] . We generated two mutants of NS5PolDV , W795A and H798A . Overall correct folding of the purified recombinant mutants was equally verified by a fluorescent thermal shift assay giving Tm values corresponding to the wt protein ( wt NS5PolDV Tm 49 . 0°C ± 0 . 5°C , W795A mutant Tm 48 . 6°C ± 0 . 6°C , H798A mutant Tm 48 . 1°C ± 0 . 04°C ) . The RNA initiation and elongation activities of wt NS5PolDV and the W795A and H798A mutants were tested using the minigenomic RNA template and either Mg2+ or Mn2+ as catalytic ions ( Figure 6A ) . In both cases the H798A mutant shows an increased activity on this template whereas W795A shows a similar overall activity compared to wt NS5PolDV . Figure 6B shows the analysis of the reaction products on a denaturing agarose-formaldehyde gel . The W795A mutant behaves indeed like wt NS5PolDV , the percentage of the de novo RNA synthesis initiation product of template size is unchanged . In contrast the H798A mutant generates considerably less de novo RNA synthesis product whereas the yield of RNA elongation products is higher . We then compared the capacities of wt and all mutant NS5PolDV proteins to catalyze de novo RNA synthesis initiation on DV103′- , without template and on DV103′+ using Mn2+ as catalytic ion ( Figure 6C panels 1 , 3 and 4 ) . Indeed , the H798A mutant is considerably less capable of correct de novo RNA synthesis initiation than wt NS5PolDV whereas W795A behaves as wt NS5PolDV . Note that the product formed by NS5PolDV TGGK on DV103′+ ( panel 4 ) corresponds to pppGA generated by internal RNA synthesis initiation ( see also Figure 5C ) ; and therefore part of the product formed by the H798A mutant may correspond to pppGA . When Mg2+ is used on both templates , the same results are obtained ( Figure 6C panel 2 for template DV103′- ) . We thus conclude that residue H798 is essential for the formation of the correct ATP-specific priming site and may act as a priming platform .
In this study , we present evidence that the dengue virus NS5 polymerase domain ( NS5PolDV ) alone is responsible for maintenance of A and U as first and last nucleotides of the DV genome , respectively . NS5PolDV was used instead of full-length NS5 in the frame of this study in order to avoid any interference of the RNA-binding , NTP-binding , or enzymatic activities of the N-terminal domain of NS5 . We report that NS5PolDV is endowed with several structural and mechanistic features converging to the specific de novo synthesis and elongation of the correct ATP-initiated primer even on templates that lack the correct corresponding U at the 3′-end . The first and last nucleotides of the genome are strictly conserved in the genus Flavivirus thus the results presented here may apply to the entire genus . We demonstrate the generation of a dinucleotide primer pppAG on both genomic and antigenomic RNA templates . We have previously observed the production of such dinucleotide primer on homopolymeric templates [14] . In the following step pppAG ( A/U ) trinucleotides are formed before processive RNA elongation occurs . During the latter , NS5PolDV continues RNA synthesis to the very end of the template . We do not know if di- and tri-nucleotide primers as detected in the reaction , originate from a slow but processive RNA synthesis reaction , or are actually released from the complex and re-used by the polymerase acting in a distributive RNA synthesis mode . We also show that the pppAG primer is effectively elongated in the presence of Mg2+ or Mn2+ and the correct template . Thus , after initial phosphodiester bond synthesis , the pppAG primer is aligned at the correct position in order to be elongated . The efficient use of the short primer pppAG reported here is in apparent contrast to the inefficient use of 5′-OH-AG dinucleotide previously reported [13] , [30] . The 5′-triphosphate moiety of the chemically synthesized pppAG primer is most probably an important binding determinant allowing efficient elongation ( see discussion of the proposed de novo initiation complex Figure 7 ) . We then demonstrate that in its de novo RNA synthesis initiation state NS5PolDV contains a built-in ATP-specific priming site . Major structural elements of NS5PolDV contributing to this site reside within residues T794 to A799 . Their deletion forces NS5PolDV to initiate de novo RNA synthesis internal to the template using GTP as the first nucleotide ( Figure 5C panel 1 ) and to perform primer-dependent RNA synthesis ( Figure 5B ) . In analogy to the structure of HCV NS5B in complex with a nucleotide in its priming site [31] and because of the amino acid conservation observed within a larger group of de novo RdRps [25] , we expect that NS5PolDV residues R472 ( RdRp catalytic motif F3 , see [14] ) as well as S710 and R729 ( motif E ) are involved in triphosphate binding . This might explain why de novo RNA synthesis initiation by the loop-deleted mutant is still possible , albeit internal to the template . We conclude that indeed the T794-A799 loop plays a major role both in correct de novo initiation and in shaping the priming site . Within the priming loop , residue H798 is essential for primer synthesis ( Figure 6 ) . We propose that H798 provides the initiation platform against which the priming nucleotide ATP is stacked . Using the structure of the de novo initiation complex of the RdRp of bacteriophage φ6 [23] as a starting point , we generated a model of the initiation complex of DV serotype 2 RdRp in complex with the 3′- end of the genome UUCU and both ATP and GTP as first and second nucleotide , respectively ( Figure 7 ) . In this model , the triphosphate moiety of ATP indeed interacts with residues S710 , R729 and R737 of the thumb subdomain of NS5PolDV . The aromatic ring of H798 stacks the adenine nucleobase of ATP in a similar position to a φ6 RdRp tyrosine residue against which the guanine nucleobase of its priming GTP is stacked . In several protein complex structures histidine has been shown to bind an adenine nucleobase by stacking interactions [32] . Nevertheless , histidine does not seem to provide any specificity towards adenine versus guanine [33] . Our model does not propose any obvious specific interaction with the adenine base . This might be due to the fact that the structure of NS5PolDV has been captured in a pre-initiation state . In this state , motif F , which provides the upper part of the NTP entry tunnel in the active initiation and elongation conformation of viral RdRps , is not yet correctly positioned [34] . The fine characterization of the ATP-specific built-in priming site of NS5PolDV awaits the crystal structure of a de novo RNA synthesis initiation complex . We provide a mechanistic basis for the conservation of nucleotides A and U as the first and last nucleotides of the DV genome , respectively . Figure 8 summarizes the different levels of control that ensure ATP-specific de novo RNA synthesis initiation . Firstly , it generates and elongates the bona fide pppAG primer ( red arrows and green arrows on the right ) . Even in the absence of any template and in the presence of Mn2+ ( Figure 8 left red arrow ) NS5PolDV is able to exclusively synthesize the pppAG primer ( Figure 2B and C , Figure 3A and C ) . Note that we have also observed pppAG synthesis by full-length NS5 in the absence of a template ( not shown ) . Since a sufficiently high Mn2+ concentration is present in the cell ( 0 . 1 µM to 40 µM Mn2+ in blood , brain , and other tissues [35] ) , NS5 in the replication complex might already be loaded with pppAG and thus be ready to elongate pppAG on the viral template . The same pppAG primer is preferentially synthesized in the presence of the correct template irrespective of the metal ion present at the polymerase active site ( Figure 8 right red arrows , Figure 2A and B , Figure 3 ) . In the presence of Mg2+ , NS5PolDV supports neither formation nor elongation of pppAG on incorrect templates ( Figure 8 blue blocked arrow , Figure 4B ) . In the presence of Mn2+ , NS5PolDV is able to synthesize cognate dinucleotides on incorrect templates ( Figure 2C ) , but in the presence of all nucleotides and all templates ( a probably biased and more unfavorable set-up compared to the situation in the replication complex in vivo ) , pppAG is still a major product ( Figure 3C ) . Remarkably , the pppAG/Mn2+-loaded polymerase is able to mismatch and extend pppAG in order to restore the correct 5′-end ( Figure 8 blue arrows , Figure 4 ) . The selective extension reaction thus refrains synthesis of incorrect RNAs that could occur in the presence of incorrect templates . All these reactions converge to the formation of pppAG and the conservation of A as the starting nucleotide at the 5′-end of viral genomic and antigenomic RNAs . Note that the mechanistic basis of the conservation of the second nucleotide G is beyond the scope of this study . Preliminary results generated in our laboratory indicate that both template and polymerase are important to ensure the specific incorporation of a G as the second nucleotide ( not shown ) . Several ways of viral RNA genome maintenance and repair concerning terminal damage have been discussed [4] , among others the generation of “non-templated” primers and the use of abortive transcripts as primers . Here we demonstrate that NS5PolDV uses these two mechanisms . Non-templated primers are generated only in the presence of Mn2+ . Abortive transcripts are used as primers in the presence of either Mg2+ or Mn2+ . A third mechanism observed here is the discrimination against an incorrect template in the presence of Mg2+ . In addition , in the case that a 3′-end might be shortened , the correction upon de novo initiation should be preceded by the addition of ( a ) nucleotide ( s ) by the terminal transferase activity of NS5 . This activity has also been listed as another way of repairing terminal damage of viral RNA genomes [4] . For NS5PolDV we have observed this activity before [14] and now again in the presence of Mn2+ ( Figure 1B ) . The DV polymerase endows several of the proposed mechanisms to maintain the correct 5′ and 3′-ends of the DV genome and antigenome . The ability of DV and WNV to restore a U at the very 3′-end of genomes with 3′-end deletions has been demonstrated [2] , [36] . This observation is in accordance with the existence of an ATP-specific priming site in NS5PolDV . Tilgner et al . [2] , [36] reported the complete reversion of WNV replicon CA and CG 3′-ends to CU whereas CC was only partially reverted . Since we have not seen preferential de novo RNA synthesis initiation starting with GG in comparison to UG or CG ( all three are possible in presence of Mn2+ , Figure 2 ) , this might be due to an intrinsic difference between DV and WNV RdRp or caused by different propensities of the erroneous templates to allow pppAG elongation . Indeed CA and CG 3′-ends allow pppAG elongation more readily than the CC 3′-end ( Figure 4 , two independent reactions were performed ) . Thus the CC 3′-end might therefore take longer to revert . Furthermore , Teramoto et al . [2] , [36] observed the correction of the 5′-end from pppGAG to pppAG . Our work provides a mechanistic explanation for their observation . The observation of non-templated pppAG formation in the presence of Mn2+ by a viral RdRp has not been reported before using recombinant RdRp assays . However , previous reports convey the occurrence of non-templated dinucleotide formation . RSV , a member of the ns-RNA virus family Paramyxoviridae restores the correct 5′-pppA although minireplicons did not encode the correct 3′-U [10] . The authors propose that RSV RdRp contains a built-in ATP-specific priming site and cite the observation that the RdRp of the related ns-RNA vesicular stomatitis virus ( VSV , Rhabdoviridae ) contains a specific ATP-binding site [37] as an argument in favor of their proposition . When VSV RdRp assays were carried out using recombinant RdRp in the presence of Mg2+ , non-templated 5′-initiation was not observed [6] . There is either the possibility that RSV and VSV belong to two different ns -RNA viral families and thus developed different strategies or , in analogy to our results that their RdRps use Mn2+ to correctly initiate RNA synthesis on erroneous templates as observed for NS5PolDV here . It is generally believed that Mg2+ is the activating cofactor of polymerases in vivo because viral RdRp properties observed with Mg2+ in vitro are more consistent with properties observed biologically . A second reason for giving the preference to Mg2+ is its cellular abundance in comparison to Mn2+ ( i . e . , 0 . 5 mM free Mg2+ versus 0 . 7 µM free Mn2+ in rat hepatocytes [38] , [39] and 0 . 1 µM–40 µM Mn2+ in blood , brain and other tissues [35] versus 0 . 2 to 0 . 7 mM Mg2+ in human blood [40] . Nevertheless , some events especially involved in correct and efficient de novo RNA synthesis initiation may require the specific use of Mn2+ by viral RdRps under physiological conditions ( our study and [10] , [36] , [41] , [42] ) . The pppAG primer synthesis by the DV RdRp can be considered as the first line of control of the conservation of Flavivirus genome and antigenome ends . However , there might be other mechanisms to tighten the selection . The first one could be the base pairing of the genome ends maintaining specific RNA secondary structures , which are necessary to recruit the replication machinery . Computer simulations of such structures [43] indicate that the last U of the 3′-end of the genome may be unpaired or paired ( structure I or II , respectively in [43] ) . Thus , requested base pairing may exert selective pressure to keep a U at the end of the Flavivirus genome . Another selection level concerns only the 5′-end of the genome and is due to the counterselection of incorrect 5′-ends through the NS5 RNA-cap methyltransferase . Indeed , several crystal structures of the cap-dependent bi-functional methyltransferase domain of NS5 show that specific binding of the 5′-cap involves specific recognition of the first transcribed 5′-adenosine through its N1 position and residue Asn18 [44] , [45] . Therefore , for the genomic strand , methylation at the cap N7-guanine and the subsequent 2′-O position of the first transcribed adenosine should be efficiently achieved only when ATP is the starting 5′-nucleotide . Finally , cap addition seems to involve 5′-ATP selectivity as well [20] . Collectively , we propose that the RdRp of flaviviruses is the first actor responsible for the conservation of the correct ends of their genome , and that other mechanisms such as genome cyclization and the specificity of guanylyltransferase and methyltransferase activites add to the selective pressure . These mechanisms of maintenance might also apply to other RNA virus genera with conserved genome ends and viral RdRps initiating RNA synthesis de novo .
DV103′+ ( 5′-AACAGGUUCU-3′ ) and DV103′- ( 5′-ACUAACAACU-3′ ) were synthesized at the RIBOXX GmbH Dresden and by Dharmacon . The templates are devoid of stable secondary structure when submitted to the Mfold server [46] ( ΔG = 3 . 60 kcal/mol for DV103′+ and no folding for DV103′- ) . The gene coding for N-terminal His6-tagged NS5PolDV ( serotype 2 , New Guinea C ) as defined in [14] cloned in a pQE30 plasmid was expressed in E . coli ( Tuner ( Novagen ) or NEB Express ( New England Biolabs ) ) cells carrying helper plasmid pRare2LacI ( Novagen ) . Expression was carried out in Luria broth overnight at 17°C after induction with 50 µM IPTG , addition of 2% EtOH and a cold shock ( 2 h at 4°C ) . Sonication was done in 50 mM sodium phosphate lysis buffer , pH 7 . 5 , 500 mM NaCl , 20% glycerol , 0 . 8% Igepal ( 10 ml of this lysis buffer for around 2 g cell pellet from 1l culture ) in the presence of DNase I ( 22 µg/ml ) , 0 . 2 mM benzamidine , protease inhibitor cocktail ( SIGMA ) , 5 mM β-mercaptoethanol and 1 mg/ml lysozyme after 30 min incubation at 4°C . After centrifugation the soluble fraction was incubated in batch with 2 ml TALON metal-affinity resin slurry ( Clontech ) for 40 min at 4°C . Protein bound to the beads was washed once with 10 volumes of sonication buffer containing 1 M NaCl and 10 mM imidazole and once with the former buffer without Igepal . Protein fractions were then eluted with sonication buffer containing 250 mM imidazole , no Igepal and 250 mM glycine . After dialysis into 10 mM Tris buffer , pH 7 . 5 containing 300 mM NaCl , 20% glycerol , 250 mM glycine and 1 mM DTT the protein was diluted with the same volume of this buffer without NaCl and loaded onto a HiTrap heparin column ( GE Healthcare ) . Pure NS5PolDV was then eluted in a single peak applying a gradient from 150 mM to 1 M NaCl . Alternatively , gel filtration was used as a second purification step using a Superdex 75 HR 16/60 column ( GE Healthcare ) and the dialysis buffer . NS5PolDV was stored at −20°C at a concentration of 40 to 60 µM after a final extensive dialysis into 10 mM Tris buffer , pH 7 . 5 containing 300 mM NaCl , 40% glycerol and 1 mM DTT . Purity was higher than 98% as judged by SDS-PAGE . Mutant TGGK , W795A and H798A NS5PolDV expression plasmids were generated using the kit QuikChange ( Stratagene ) . Protein expression and purification was done as for the wt protein . Analysis by gel filtration showed a single peak eluting at the same volume as wt NS5PolDV . Melting temperature ( Tm ) values of wt and mutant NS5PolDV were determined using a thermofluor-based assay [49] . In 96-well thin-wall PCR plates 3 . 5 µl of a fluorescent dye ( Sypro Orange , Molecular Probes , 714-fold diluted in H2O ) was added to 21 . 5 µl protein solutions at a concentration of 0 . 5 or 1 mg/ml ( 6 . 7 or 13 . 4 µM ) in storage buffer . Thermal denaturation of the proteins was followed by measuring fluorescence emission at 575 nm ( excitation 490 nm ) . Tm values were calculated using GraphPad Prism software and the Boltzmann equation as in [49] . Reactions were done in 50 mM HEPES buffer , pH 8 . 0 containing 10 mM KCl , 10 mM DTT and template , NS5PolDV , non-labeled NTPs , and catalytic ions at final concentration as given in the figure legends . Radiolabeled [γ-32P]-ATP , [α-32P]-GTP , or [α-32P]-UTP was used at 0 . 4 µCi per µl reaction volume ( 3000 Ci/mmol , Perkin-Elmer ) . Reactions were started by addition of a mixture of HEPES buffer , KCl , catalytic ions and UTP and CTP when used ( given in Figures ) . After given time points samples were taken and reactions stopped by adding an equal volume of formamide/EDTA gel-loading buffer . Reaction products were separated using sequencing gels of 20% acrylamide-bisacrylamide ( 19∶1 ) , 7 M Urea with TTE buffer ( 89 mM Tris pH 8 . 0 , 28 mM taurine ( 2-aminoethanesulfonic acid ) , 0 . 5 mM EDTA ) . RNA product bands were visualized using photo-stimulated plates and the Fluorescent Image Analyzer FLA3000 ( Fuji ) and quantified using Image Gauge ( Fuji ) . The oligoG marker was produced as explained in [14] . The minigenomic template was produced by in vitro transcription and tests carried out as described in [14] . Reactions analyzed by filter-binding and liquid scintillation counting contained 50 mM HEPES buffer , pH 8 . 0 , 10 mM KCl , 10 mM DTT , 100 nM RNA template , 200 nM NS5PolDV , 500 µM NTP except for UTP ( 4 µM ) , [3H]-UTP at 0 . 2 µCi/µl and either 5 mM MgCl2 or 2 mM MnCl2 . Reactions were started by the addition of a mixture of HEPES , KCl , catalytic ions , CTP , and UTP . After 30 , 60 , 90 , and 120 min 10-µl samples were taken and diluted into 50 µl of 100 mM EDTA , pH 8 . 0 to quench the reaction . Samples were then transferred onto a DEAE filter mat . Non-incorporated [3H]-UTP was removed by washing with 300 mM ammonium formate and the radioactively labeled product quantified in counts per minute ( cpm ) using liquid scintillation counting . Product formation was then plotted against time and initial velocities calculated in cpm/min . Reactions analyzed on formaldehyde-agarose gels contained 50 mM HEPES buffer , pH 8 . 0 , 10 mM KCl , 10 mM DTT , 100 nM RNA template , 200 nM NS5PolDV , 500 µM NTP except for UTP ( 4 µM ) , [α-32P]-UTP at 0 . 4 µCi/µl , and 5 mM MgCl2 . Reactions were started by a mixture of HEPES , KCl , MgCl2 , CTP and UTP and stopped after 60 and 120 min by adding an equal volume of sample buffer ( 40 mM MOPS pH 7 . 0 , 83 . 3% formamide , 2 M formaldehyde , 10 mM sodium acetate , 85 mM EDTA ) . Samples were denatured for 10 min at 70°C and 1/10 of loading buffer ( 50% glycerol , 10 mM EDTA , xylene cyanol and bromphenol ) added . Samples were then analyzed on a 1 . 2% agarose-formaldehyde gel in 20 mM MOPS buffer pH 7 . 0 , 5 mM sodium acetate , 1 mM EDTA . Gels were dried and RNA product bands visualized using photo-stimulated plates and the Fluorescent Image Analyzer FLA3000 ( Fuji ) and quantified using Image Gauge ( Fuji ) . A homology model of NS5PolDV serotype 2 strain New Guinea C was generated using the Swiss-model server [50] and the X-ray structure of NS5PolDV serotype 3 ( PDB code 2J7W [16] ) . NS5PolDV and the RdRp of bacteriophage φ6 in complex with a template RNA strand and initiating NTPs ( PDB code 1HI0 ) were then superimposed using the three catalytic aspartate residues of both proteins . The structural model of the initiation complex of NS5PolDV serotype 2 was then generated by changing the RNA template to UUCU ( 3′-end of the DV genome ) and the initiating NTP to ATP , and by manually adapting the conformation of the priming loop using the UCSF Chimera software [51] . Subsequently using the same program the computed free energy of the model was minimized .
|
The 5′- and 3′-ends of RNA virus genomes have evolved towards efficient replication , translation , and escape from defense mechanisms of the host cell . Little is known about how RNA viruses conserve or restore the correct ends of their genomes . The Flavivirus genus of positive-strand RNA viruses contains important human pathogens such as yellow fever virus , West Nile virus , Japanese encephalitis virus and dengue virus ( DV ) . The Flavivirus genome ends are strictly conserved as 5′-AG…CU-3′ . We demonstrate here the primary role of the DV polymerase in the conservation of the first and last genomic residue . We show that DV polymerase contains an ATP-specific priming site , which imposes a strong preference for the de novo synthesis of a dinucleotide primer starting with an ATP . Furthermore , the polymerase is able to indirectly correct erroneous sequences by producing the correct primer in the absence of template and on templates containing incorrect nucleotides at the 3′-end . The correct primer is productively elongated on either correct or incorrect templates . Our findings provide a direct demonstration of the implication of a viral RNA polymerase in the conservation and repair of genome ends . Other polymerases from other RNA virus families are likely to employ similar mechanisms .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"virology",
"viral",
"enzymes",
"biology",
"microbiology"
] |
2012
|
Molecular Basis for Nucleotide Conservation at the Ends of the Dengue Virus Genome
|
The ATP binding cassette ( ABC ) proteins are a family of membrane transporters and regulatory proteins responsible for diverse and critical cellular process in all organisms . To date , there has been no attempt to investigate this class of proteins in the infectious parasite Trichomonas vaginalis . We have utilized a combination of bioinformatics , gene sequence analysis , gene expression and confocal microscopy to investigate the ABC proteins of T . vaginalis . We demonstrate that , uniquely among eukaryotes , T . vaginalis possesses no intact full-length ABC transporters and has undergone a dramatic expansion of some ABC protein sub-families . Furthermore , we provide preliminary evidence that T . vaginalis is able to read through in-frame stop codons to express ABC transporter components from gene pairs in a head-to-tail orientation . Finally , with confocal microscopy we demonstrate the expression and endoplasmic reticulum localization of a number of T . vaginalis ABC transporters .
Trichomonas vaginalis is a human-infective parasitic protozoan that is the most prevalent causative agent for non-viral sexually transmitted infections , with an estimated annual incidence of at least 170 million new cases of trichomoniasis worldwide [1] . Characteristic of other parabasalids in the order Trichomonadida , T . vaginalis is a flagellated , microaerophilic eukaryote which displays many features typical of eukaryotic cells including a membrane-bound nucleus , endoplasmic reticulum , ribosomes and the nine plus two arrangement of microtubules [2] . The unicellular organism possesses five microtubule-based flagella – four located anteriorly and one posteriorly associated with an undulating membrane , the structure responsible for the parasite's distinctive quivering movement [3] . An additional key morphological feature of T . vaginalis is its axostyle , a cylindrical structure spanning the length of the cell from an anteriorly positioned nucleus . Also composed of microtubules , it is believed to play a role in anchoring the parasite to the vaginal epithelium [3] . In common with many unicellular eukaryotes with apparently early evolutionary divergence , T . vaginalis has a reduced complement of organelles in comparison with higher eukaryotes , lacking both mitochondria and peroxisomes , but containing instead a hydrogen producing organelle ( the hydrogenosome ) which may be a relic of an endosymbiotically acquired mitochondrion-like organelle , or which may be the result of a 2nd symbiotic event ( see [4] for a review ) . Given the unusual cell biology of T . vaginalis we decided to investigate a class of membrane proteins ( ATP binding cassette ( ABC ) transporters ) localized to the plasma membrane and organellar membranes of all eukaryotic cells , and examine differences in the complement of these proteins in T . vaginalis compared to other eukaryotes , with a longer term view to determining the contribution of ABC transporters to the parasite's biology . ATP binding cassette ( ABC ) systems encompass a family of proteins found in all 3 domains of life , which are responsible for an abundance of transport roles as well as a variety of intracellular non-transport processes including gene regulation and DNA repair [5] . The vast majority of ABC transporters in eukaryotes are exporters , whilst prokaryotes encode functioning importers too . All proteins in this family are defined by the presence of a highly conserved nucleotide binding domain ( NBD; the eponymous ATP binding cassette ( ABC ) ) , an ATPase domain with characteristic motifs including a Walker A , Walker B and Signature sequences , which contribute to the hydrolysis of ATP , the energy of which drives the various cellular processes mentioned . The NBDs are highly conserved among different ABC proteins , sharing typically greater than 25% sequence identity [6] , and a common 3-dimensional fold [6] . In addition to the cytoplasmic NBDs , ABC transporters contain transmembrane domains ( TMDs ) , which span the membrane numerous times via α helices ( typically 4–11 helices per domain , [7] ) , and which contain binding sites for transported substrates . The typical configuration for a functional ABC transporter is believed to comprise 2 NBDs and 2 TMDs , although these need not necessarily be present within the same polypeptide . For example , “full-transporters” are defined as containing all four domains within the same polypeptide , whilst “half-transporters” typically comprise a single NBD and TMD within one polypeptide and are believed to either homo- or hetero-dimerize in order to function [5] [8] . In contrast to the NBDs , the TMD components show considerable sequence variation between unrelated transporters reflecting their role in recognition and transport of substrate . Based on the organisation of their NBDs and TMDs as well as features such as membrane topology , sequence homology and gene structure , eukaryotic ABC proteins have been grouped into 7 subfamilies - ABCA to ABCG [7] , [8] , although occasionally sequences have been identified as not fitting with this classification and these have been annotated as ABCH and ABCI sequences ( e . g . see [9] ) . The availability of the recently completed T . vaginalis genome sequence [10] enabled a database search for ABC proteins within T . vaginalis to be conducted , as presented here , using the sequences of known ABC proteins from other species for comparison . A phylogenetic classification of the hypothetical ABC proteins into subfamilies has been conducted , accompanied by discussion regarding the putative location and function of different transporters within the parasite . In addition we have performed sub-cellular localization studies on selected ABC proteins to validate our analysis . Finally , comparisons are drawn between the ABC families of T . vaginalis and three other sequenced protists P . falciparum , E . histolytica and G . lamblia [11] , [12] , [13] .
The sequenced T . vaginalis strain G3 ( ATCC PRA-98; [10] ) , kindly provided by G . H . Coombs , was used for genomic DNA preparation throughout this study and was grown in modified TYM medium [14] . Strain C1 ( ATCC 30001 ) , which is less pathogenic , was employed for transfection and localization studies . Late stage cultures of T . vaginalis C1 were centrifuged ( 1500 g , 10 minutes , 4°C ) and resuspended in supplemented Diamond's medium to a density of 108 cells/ml . Cells ( 3×107 ) were incubated with plasmid DNA ( 50 µg ) for 15 minutes on ice and then electroporated 350 V , 960 µF ( BioRad GenePulser ) in chilled 0 . 4 cm spacing electroporation cuvettes ( GeneFlow ) . Electroporated cells were immediately diluted into 50 ml of complete media , pre-warmed to 37°C and then incubated for a further 4 hours , prior to the addition of G418 to 50 µg/ml , and left overnight , before surviving cells ( those in motile suspension ) were transferred into fresh complete , selective media and incubated for 3–21 days until a density of ca . 2×106 cells/ml was obtained , whereupon they were passaged as described above . Genomic DNA ( gDNA ) was isolated from 3×108 T . vaginalis cells as described previously [15] and resuspended in 500 µl TE containing 10 µg/ml RNase A . Total RNA was extracted from 2×109 T . vaginalis cells by the single-step acid guanidinium thiocyanate-phenol-chloroform method [16] . 1 mg of total RNA was processed using the PolyATtract mRNA isolation system ( Promega Corporation , Madison , WI , USA ) according to manufacturers' instructions to yield ca . 10 µg of enriched polyA+ RNA that was stored at −80°C until required . Reverse transcription and PCR of specific regions of the genes TVAG_275410 , TVAG_275420 , TVAG_072410 , TVAG_072420 and TVAG_470720 were carried out according to manufacturer's instructions using the Access RT-PCR System ( Promega Corporation , Madison , WI , USA ) with appropriate primers listed in Table S1 . Bands of interest were separated by agarose gel electrophoresis , and DNA was extracted using the Qiaquick Gel Extraction Kit ( Qiagen Inc . ) prior to treatment with T4 DNA polymerase ( New England Biolabs ) to remove 3′ A overhangs introduced by Tfl DNA polymerase in the Access RT-PCR System . To allow for sequencing , the blunt-ended fragments were then ligated into the pSC-B vector using the StrataClone Blunt PCR Cloning Kit ( Stratagene ) . Where indicated , specific gene fragments were amplified by the polymerase chain reaction ( PCR ) from T . vaginalis gDNA using Phusion DNA polymerase ( New England Biolabs , Inc . ) according to manufacturer's recommendations . cDNA regions comprising the open reading frames for genes TVAG_470720 , TVAG_605460 and fused TVAG_415980/TVAG_415990 were amplified by PCR using the primer pairs 470720NdeI and 470720BamHI , 605460NdeI and 605460BamHI , 415980NdeI and 415990BamHI respectively ( Table S1 ) . The introduced NdeI and BamHI restriction recognition sites facilitated ligation into corresponding sites on the pTagVag2 vector , thereby resulting in C-terminal tagging with a double haemagluttinin epitope [17] , ( a kind gift of Professor Jan Tachezy ) . The resulting plasmid constructs , pTagVag2-470720 , pTagVag2-605460 and pTagVag2-415980/90 were maintained in E . coli XL1-Blue and plasmid DNA was purified using Qiagen maxiprep kits . The purified plasmid DNA was further subjected to ethanol precipitation and resuspended in water under sterile conditions at a concentration of 10 µg/µl for transfection into T . vaginalis . Late stage cells were centrifuged ( 1500 g , 10 minutes , 4°C ) and resuspended in phosphate buffered saline ( PBS ) at 1×107/ml . Aliquots ( 0 . 5 ml ) of this suspension were layered onto silane covered microscope slides ( Sigma ) and left to adhere for 30 minutes at room temperature . Non-adherent cells were removed by washing once with PBS , and remaining cells were fixed and permeabilized with 0 . 5 ml 4% w/v paraformaldehyde , 0 . 1% v/v Triton X-100 for 20 minutes at room temperature . Slides were washed twice with PBS , and then incubated for 1 hour at room temperature in blocking solution ( PBS supplemented with 0 . 25% w/v each BSA and fish gelatin ) . Slides were then incubated with primary antibody ( mouse anti-HA , 1∶2500 or rabbit anti-BiP , 1∶1000 in blocking solution ) for 1 hour at room temperature , washed twice with PBS and then incubated with secondary antibody ( species specific AlexaFluor-488 , 1∶1000 in blocking solution ) for 1 hour at room temperature in the dark . After further washing in PBS , slides were treated with RNAase ( 100 µg/mL in PBS , 37°C , 20 minutes ) , washed , and nuclei stained by addition of propidium iodide ( 3 . 3 µg/mL in PBS , 5 minutes ) and then mounted with 50% v/v glycerol in PBS . Slides were kept at 4°C in the dark until analyzed , and could be stored for at least 2 months without loss of signal quality . Slides were analysed on a Zeiss LSM 710 confocal laser scanning microscope , using a 63× oil-immersion objective . The fluorescent tags were excited using a laser sources at 488 nm ( Alexa488 ) and 561 nm ( propidium iodide ) , and emitted light collected . Image files were subsequently processed using the Zeiss LSM Image Browser software . The protein sequence of human ABCB1/P-glycoprotein ( AAA59575 ) was used as a query for a homology search of TrichDB ( http://trichdb . org/trichdb ) , the complete T . vaginalis genome database , using BLASTp . Pairs of sequences considered redundant due to greater than 95% amino acid identity were removed prior to further analysis . All remaining sequences were screened manually for Walker A ( GxxGxGK ( S/T ) , where x = any amino acid ) , Walker B ( hhhhDE , where h = hydrophobic amino acid ) and ABC signature ( LSGGQ ) motifs . Putative TM spanning regions of hypothetical T . vaginalis ABC transporters was predicted using the programs TMHMM [18] , TopPred [19] , and HMMTOP [20] . Multiple BLASTp searches were performed on the NCBI ( National Centre for Biotechnology Information ) and UNIPROT websites to identify characterised and curated homologues of the hypothetical ABC proteins of T . vaginalis in other species and thus facilitate classification of the T . vaginalis proteins . Each protein sequence was also used as a query to search the Expressed Sequence Tag ( EST ) database using tBLASTn . The EST database consists of 26 , 491 single pass cDNA sequences obtained from the C1 and T1 strains of T . vaginalis ( TrichDB ) . Protein sequences showing greater than 97% identity to translated ESTs were categorised as being expressed in T . vaginalis . Consensus phylogenetic trees were constructed via a multistep process to examine relationships between different protein sequences . Multiple sequence alignment of the hypothetical ABC proteins was performed on Bioedit with ClustalW_2 using the BLOSUM-62 matrix . Alignments were manually edited to remove internal gaps and N and C-terminal extensions where necessary to prevent differences in sequence length affecting protein clustering . The amended alignment was bootstrapped with 500 replications using Seqboot , whilst Protpars generated trees from the resulting alignments to be used by Consense in producing a consensus . Seqboot , Protpars and Consense all form part of the Phylip Package Version 4 . 0 , which was accessed via the Mobyle website ( http://mobyle . pasteur . fr ) . Trees were visualised using Treeview .
A BLASTp search of TrichDB [21] using human P-glycoprotein [22] as a query sequence identified 102 predicted T . vaginalis ABC proteins , four of which showed >95% identity to another sequence and so were removed to avoid redundancy ( TVAG_059100 , TVAG_132360 , TVAG_431960 and TVAG_510260 ) . The 98 hypothetical ABC proteins identified here exceeds the 88 originally estimated based on the draft genome sequence [10] and , compared with the number of ABC proteins identified in other species , constitutes a significant total . Table 1 compares the ABC family of T . vaginalis with multi- and unicellular non-parasitic species as well as four other disease-causing parasites: P . falciparum , E . histolytica , L . major and G . lamblia . The number of ABC genes in T . vaginalis exceeds all but the two plant species [9] , [23] , and indicates that the ABC family of genes has undergone considerable expansion in T . vaginalis . Genome analysis indicates many other gene families ( including several involved in membrane trafficking and transport ) have expanded similarly [10] . As with these , expression proteomics under diverse growth conditions is required before the tags “putative” or “hypothetical” can be dispensed with . The large number of ABC proteins in plants is believed to be partly due to genome expansion and also due to functional diversification [9] . The putative functional diversity of T . vaginalis ABC proteins will be discussed below . The lengths of hypothetical proteins varied from 116 to 919 amino acids , although for three sequences this is difficult to verify as three are located at the ends of unassembled sequence scaffolds ( TVAG_241640 , TVAG_542450 and TVAG_542470 ) . All 98 sequences were analysed manually for the presence of Walker A ( GxxGxGKS/T ) , Walker B ( hhhhDE ) and Signature motifs ( LSGGQ ) . The majority were found to contain all three although it was common in putative ABC proteins for one of these , usually the Signature , to be very distinct from the canonical sequences ( Table S2 ) . This is not atypical as examination of multiple ABC transporter families has previously shown ( e . g . [24] ) . Analysis of the hydrophobicity of the ABC proteins using TOPPRED , TMHMM and HMMTOP resulted in predictions of between 0 to 9 transmembrane ( TM ) helices . Indeed 26 sequences were identified as containing no transmembrane regions , a number far in excess of any other non-plant , eukaryotic genome . No single sequence appeared to encode two blocks of multiple TMHs , separated by an NBD sequence , indicating that the T . vaginalis genome does not encode any full length transporters , an observation discussed further below . Based on the number and order of NBDs and TMDs , the topology of each protein was defined and table S2 presents an inventory of all 98 predicted proteins with respect to length , membrane topology , motifs and subfamily classification ( explained below ) . Initial phylogenetic analyses ( data not shown ) of the T . vaginalis ABC protein sequences resulted in a tentative assignment of the majority to one of the major sub-families of ABC proteins documented in eukaryotes . However , bootstrap analyses indicated low certainty in many of the branch-points and thus three further measures were taken to reinforce our assignment of proteins to the ABC sub-families as listed in Table S2 . Firstly , we considered the topology of each predicted protein . For example , sequences with a large ( >250 amino acids long ) predicted extracellular loop ( ECL ) sandwiched between the first two predicted TM helices were candidates for the ABCA family ( e . g . TVAG_173120 ) as this insertion is exclusively found in ABCA sequences [25] . Secondly , sequences with two consecutive NBDs and no TMDs were likely to represent members of the ABCE or ABCF sub-families ( e . g . TVAG_385840 ) as again in eukaryotes this domain organization is only found in non-transporting ABC proteins [26] . Thirdly , for each sequence we performed BLASTp analyses against the GenBank non-redundant protein sequence database and used the highest ranked sequences as a guide for sub-family allocation . For example , in the case of the TVAG_542450 sequence , which had been previously categorised as being the parasite's homologue of P-glycoprotein/ABCB1 [27] , we found that all of the highest-ranking 100 sequences for TVAG_542450 were classified as being predicted or characterised members of the ABCB family . Finally , to improve the accuracy of bootstrap analyses , we removed the confounding factor of highly variable sequence lengths and aligned the NBD sequences only . This analysis demonstrated clear clustering of the ABC proteins into several sub-families , and the removal of putative ABCH and ABCI sequences ( Table S2 ) from the analysis further improved the clarity of the sub-classification ( Figure 1 ) . The final predicted numbers of sequences in each sub-family are given in Table 1 , with numbers from other eukaryotes given for comparison . Among the findings we discuss below are the absence of two families – namely ABCG and ABCC , the expansion of the ABCA sub-family , and the preponderance ( 31 in total ) of proteins that are unclassifiable with the ABCA-ABCG proteins . Upon examination of the chromosomal localisation of the genes listed in Table S2 , we noted in excess of 20 examples of ORFs linked on the same loci . Several of these ORFs apparently encode half-transporters with a complete NBD and several transmembrane segments , but a large proportion ( see Table S2 , “Others” ) encoded only part of the NBD on one ORF , and the rest on adjacent genes with a linked head-to-tail orientation ( e . g . see Figure 2 and 3 ) . The intergenic regions in the latter cases were found to be relatively small , ranging from 0 to a few hundred bases . BLASTx searches with these intervening sequences revealed that they are themselves highly similar to coding abc gene sequences , but were either out of frame with the flanking partial abc ORFs , or were in frame but interrupted by stop codons . To audit whether these abc ORFs are genuinely partial genes or the result of incomplete sequence data , we sought to analyse the transcription and the genomic arrangement of a representative subset ( Figure 2 ) . TVAG_275420 is an ORF that encodes a predicted 478 aa protein of the ABCA subfamily that includes a TMD , a Walker A motif and a Signature sequence shortly followed by an in-frame TGA stop codon ( Table S2 , Figure 2A ) . This predicted protein is 70% identical to TVAG_440500 , also an ABCA subfamily member ( Table S2 ) of 830 aa that includes a full NBD . Downstream of TVAG_275420 there is a linked ORF , TVAG_275410 ( Figure 2A , top panel ) which is 82% identical to the last 70 amino acids of TVAG_440500 . A Blastx search of the 232 bp intergenic region between TVAG_275420 and TVAG_275410 revealed that this is 88% identical to a similar region in the predicted TVAG_440500 protein sequence . Collectively , these data suggest that TVAG_275420 , the intergenic region and TVAG_275410 are all part of a single gene/pseudogene ( encoding a half ABC transporter ) that is highly related to TVAG_440500 . Moreover , an EST was found that matched the region from the 3′ end of TVAG_275420 , the intergenic region and the complete sequence of TVAG_275410 ( Figure 2A , open arrow ) . This finding suggests that there is either an error in the genomic sequence or that bicistronic transcription occurs at this locus . To assess this locus , we used RT-PCR to amplify putative transcripts running from TVAG_275420 through to TVAG_275410 ( Figure 2D ) . Primers were designed to amplify any transcript or gDNA fragment from position 862 on TVAG_275420 to position 186 on TVAG_275410 ( Table S1 ) . We successfully amplified a band of the expected size at 990 bp by RT-PCR of polyA+ RNA ( Figure 2D , lane 3 ) . This band migrated at a similar size to one amplified from gDNA using the same primer set ( Figure 2D , lane 2 ) but no such species was amplified from the negative control ( Figure 2D , lane 4 ) , confirming that the band in lane 2 ( Figure 2B ) originates from mRNA and not from gDNA contamination of the poly A+ template . As a positive control , we ran a parallel set of reactions on the locus of TVAG_470720 ( Figure 2C ) , an abc gene which is known to be expressed , based upon both EST and protein detection ( our unpublished data ) . This gene encodes a complete TMD-NBD and both its transcript and a corresponding gDNA fragment were amplified using primers listed in Table S1 to yield bands of 1 kb ( Figure 2D , lanes 8–10 ) . The sequences of both the transcript and the gDNA fragment were found to be identical to the TVAG_470720 sequence from TrichDB . However , upon sequencing of both the gDNA and the amplified cDNA from the TVAG_275420/410 locus , we noted that a cytosine ( C ) was absent from a predicted C triplet at position 1414 to 1416 on the TrichDB TVAG_275420 sequence . The absence of this C residue results in an ORF of 1878 bp that runs from TVAG_275420 through to the 3′ end of TVAG_275410 . Thus , it appears that a sequencing error , and not bicistronic transcription , is responsible for the original arrangement of genes shown in Figure 2A , top panel , and raises the possibility that the same may apply to other loci containing split abc genes . We therefore investigated an additional locus ( Figure 2B ) that consists of TVAG_072420 , an 875 bp ORF encoding a single predicted TM helix and TVAG_072410 , a 1703 bp encoding five further TM helices followed by a complete NBD ( Table S2 ) . The two ORFs are separated by only 5 bp and no ESTs have been matched to either sequence . We designed primers to run from position 658 on TVAG_072420 to position 391 on TVAG_072410 ( Table S1 ) , thus expecting an amplicon of 613 bp . We were unable to amplify a band of the correct size by RT-PCR of poly A+ RNA but the same primers yielded a 0 . 6 kb band from gDNA ( Figure 2D , lanes 5–7 ) which exactly matched the TrichDB sequence , confirming that the two ORFs are indeed separated by a stop codon and a 5 bp intergenic region . These data collectively show that not all split abc genes can be explained by sequencing discrepancies . Two further examples of this were analysed to reinforce the fact that ABC transporter homologous DNA sequences are located in the intergenic regions between closely separated partial ABC transporter reading frames . The two additional examples ( Two TVAG_049010 & TVAG_049020 , and TVAG_254080 , TVAG_254070 & TVAG_254060 ) are shown in Figure 2E and F respectively . In the second locus , it appears that an abc gene has been split into four parts that each contain at least an ABC motif ( Figure 2F ) . No ESTs were found in the current databases to match any of these genes or intergenic regions . TVAG_049010 is predicted to encode three TM helices and is followed by a 93 bp intergenic region and then a downstream ORF TVAG_049020 encodes two further TM helices and a complete NBD ( Figure 2E ) . As observed above , the intergenic region has high sequence identity , as detected by BLASTx , to another putative ABCB gene TVAG_127410 . Thus , in the absence of a stop codon at the end of TVAG_049010 , this locus could potentially comprise a complete half-transporter . We successfully amplified a fragment of 1 . 3 kb from genomic DNA ( data not shown ) using primers from position 191 on TVAG_049010 to position 710 on TVAG_0490120 ( Table S1 ) , and found the sequence of the amplicon to exactly match that on TrichDB . The TVAG_254080/70/60 locus ( Figure 2F ) contains ORF TVAG_254080 with two predicted TMS followed by an open intergenic region that could potentially encode 3 TMS that are 44% identical to those of ORF TVAG_299600 , a member of the ABCA subfamily . Downstream , TVAG_254070 contains a Walker A motif , with TVAG_254060 containing the Signature sequence and Walker B motif , and these two ORFs are separated by a 27 bp intergenic region . We amplified and sequenced a 2 kb region of the genomic locus ( data not shown ) from position 125 on TVAG_254080 through to position 466 on TVAG_254060 and again found the sequence to be identical to the TrichDB sequence , confirming that the arrangement of the partial genes depicted in Figure 2F is correct . We further noted another category of loci where ORFs are arranged tail to tail , separated by short intergenic regions as shown by two examples with three open reading frames each in Figure 3 . In each case , the size of the assembled ORFs could constitute a full length ABC transporter . The locus in the top panel ( Figure 3A ) consists of two ORFs , TVAG_245200 and TVAG_245210 , separated by only 27 bp and arranged head to tail . TVAG_245220 is arranged tail to tail with 245210 with an intergenic space of 258 bp . We set out to investigate whether the contigs at this locus had been correctly assembled and sequenced to ascertain that the T . vaginalis genome does not bear any genes for full-length ABC transporters as suggested by the data presented in Table S2 . To do so , we used primers designed to give a positive result if in the first case , TVAG_245220 were reversed and in the second case , if both TVAG_245200 and TVAG_245210 were reversed ( Figure 3A ) . It was found that a PCR product for the locus was generated with a single primer , 245220R2 , which bound to the corresponding complementary strand on both TVAG_245220 and TVAG_245200 ( data not shown ) . The sequence for this fragment was identical to that in the TrichDB database , demonstrating that the original ORF arrangement in Figure 3A , top panel , was correct . TVAG_245200 is transcribed as evidenced by a matching EST from the TrichDB database , indicating that the ORFs in this locus are unlikely to represent pseudogenes . The locus comprising TVAG_415970 , TVAG_415980 and TVAG_415990 represents a similar situation as the previously described locus except that there is no intergenic region between TVAG_415980 and TVAG_415990 that are just separated by a TGA stop codon ( i . e . TVAG_415980 and TVAG_415990 could comprise an intact half-transporter , linked head-to-head with another half-transporter TVAG_415970; Figure 3B ) . Using a similar strategy as with the TVAG_245200-220 locus , we set out to verify the possibility that either TVAG_415970 or TVAG_415980 and TVAG_415990 may be reversed or whether the original arrangement is correct . A 2 . 8 kb fragment was generated by PCR with a single primer ( data not shown ) , TVAG_415990R1 , which similarly to the previous case bound to opposite strands on two tail to tail ORFs . Sequencing of this product revealed that the TrichDB arrangement was correct and that the sequence was identical to that in the database . Given that ORFs TVAG_415980 and TVAG_415990 are separated just by one TGA stop codon , we pursued study of this locus to investigate the possibility of stop codon read-through . T . vaginalis was transfected with a plasmid construct , pTagVag2-415980/90 that contained a fragment comprising the TVAG_415980 ORF and the TVAG_415990 ORF , including the intervening TGA stop codon . This fragment had been cloned upstream of a double haemagluttinin ( HA ) tag , such that detection of a recombinant protein by an anti-HA antibody would only occur if read-through happened or if the TGA stop codon were processed post-transcriptionally . PolyA+-enriched RNA isolated from the transfectants enabled us to verify by RT-PCR that the gene cassette from pTagVag2-415980/90 was transcribed . We detected amongst other smaller bands , a 0 . 9 kb band of the expected size that was amplified from the reverse-transcribed polyA+ template ( Figure 3C , lane 5 ) . This band was of the same size as that obtained from pTagVag2-415980/90 plasmid DNA that was used as a positive control ( Figure 3C , lane 4 ) , but not from negative controls ( Figure 3C , lane 3 ) . To investigate translation , we analysed transfected cells by immunofluorescence microscopy with anti-HA antibody . A diffuse distribution of anti-HA signal was seen in fixed transfected cells ( Figure 3E ) as opposed to wild-type C1 cells ( Figure 3D ) . These data provides tentative evidence for stop codon read-through , although further experimental work would be required to substantiate this . The ABCG sub-family sequences are distinguished from other ABC proteins by their “reversed topology” [28] , i . e . the NBD is found at the N-terminus of the protein , whereas the C-terminus contains the TMD . The family also contains only half-transporters in organisms whose genomes have been sequenced to date ( e . g . see [9] ) . Examination of the T . vaginalis genome's complement of half transporters reveals none with this altered topology . Furthermore , even though many of the 31 unclassified proteins contain either a single NBD or a single TMD , no 2 of these are genomically arranged in a manner compatible with them forming a complete ABCG transporter following stop codon read through or sequencing inconsistencies explored above . Similarly , we were unable to detect any members of the ABCC sub-family , which are commonly identified by the presence of a large additional N-terminal TMD containing ( usually ) 5 TM α-helices . This N-terminal extension was not found in any of the Trichomonas ABC transporter sequences , and none of these sequences more similar to the ABCC transporters than to the ABCB transporters of other parasite genomes ( E . histolytica , P . falciparum , G . lamblia ) . This absence of ABCC and ABCG transporters must reflect biological perspectives of the organism . The absence of ABCG proteins may correlate with the expansion of ABCA proteins . Although both families have members that are involved in the export of lipids and their derivatives , only the ABCA family has members that are believed to be importers [8] , [29] , and the proposal is that in T . vaginalis a requirement for lipid import ( see next section ) has driven the ABCA expansion . For the ABCC family , the absence of members may be a correlate of the absence of a glutathione system in T . vaginalis [30] as many characterised eukaryotic ABCC members are either co-transporters of glutathione , or even transport directly GSH-conjugated substrates . The other members of the ABCC family function as ion channels or ion channel regulators ( CFTR/ABCC6 and SUR/ABCC8 , C9 respectively ) which are absence from other early diverging eukaryotes [31] . The ABCA subfamily was the largest identified in T . vaginalis , with 34 putative transporters , several of which are transcribed as evidenced by expressed sequence tags . With the exception of two partial sequences , all have a ( TMD-NBD ) topology , range in length from 478–919aa and all bar 7 members of this subfamily have the characteristic extracellular loop ( ECL ) of the ABCA subfamily between their first and second predicted TM helices ( Table S2 ) . Trichomonas vaginalis has a severely compromised ability to synthesis lipids [32] , and is therefore reliant on their import - a trait shared by G . lamblia , another species in which the ABCA proteins form a significant proportion ( 68% ) of the ABC family ( Table 2 ) . Given that ABCA transporters in humans have been implicated in the export and import of a variety of lipids and lipid conjugates [29] , [33] it is plausible that some ABCA transporters have evolved in T . vaginalis as importers of lipids rather than exporters . To date , ABCA transporters that have been characterised in other eukaryotes are full-length ( i . e . 2 NBDs and 2 TMDs in the same polypeptide ) , unlike the transporters described here . In order to reconstruct the phylogenetic history for the T . vaginalis ABCA subfamily , we used sequences for TVAG_064700 and TVAG 064710 respectively as queries to search for homologues in UniProt . These two sequences were chosen as the genes are linked as inverted tandem repeats , and they both contain an ECL . Moreover , TVAG_064700 has a degenerate signature motif ( LSDGD ) whereas TVAG_064710 has a canonical one ( LSGGQ ) . To be able to properly align the half-transporters from T . vaginalis to other eukaryotic full-length transporters , we selectively extracted NBD sequences from the latter to comprise an N-terminal and a C-terminal NBD region each . As shown in Figure 4 , these ABCA NBD sequences from other eukaryotes form two distinct clusters , suggesting an early duplication event followed by divergence . It is of note that the C-terminal NBD almost always contains a degenerate signature motif in other eukaryotes whereas the N-terminal NBD invariably contains the canonical LSGGQ . The T . vaginalis ABCA single NBD sequences mimic the clustering of NBD sequences from other eukaryotes in that they too fall into two distinct groups termed Group I and Group II ( Figure 4 ) , with Group I sequences invariantly bearing a canonical signature motif and Group II sequences invariantly bearing a degenerate signature motif . Twenty of the abca genes are linked as pairs that appear to be inverted repeats ( TrichDB and Table S2 ) . Any one member of each pair ( except for TVAG_225860 and TVAG_225880 ) has higher sequence similarity to other ORFs in the same group than to its linked repeat . For instance , TVAG_072430 and TVAG_064710 cluster together in a group that is distinct from another that includes their respective linked partners TVAG_072410 and TVAG_064700 ( Figure 4 ) . A possible scenario consistent with this data that accounts for the history of abca genes in eukaryotes is that ancestral abca genes existed both as half-transporters and full-transporters , prior to the evolution of the progenitor of T . vaginalis [34] . This lineage lost the full-length transporter gene , but the evolutionary forces that maintain the paired canonical and degenerate NBDs in full-length eukaryotic ABCA transporters are clearly still acting on the Trichomonas abca half-transporter genes . In the case of the 2258 locus , the duplicate locus has apparently duplicated again to give rise to two sets of inverted genes . This appears to be a recent duplication as the sequences of corresponding gene fragments are almost identical to each other . T . vaginalis is the only parasite in Table 2 encoding ABCD type transporters , with 2 half transporters , TVAG_470720 ( 556aa ) and TVAG_605460 ( 546aa ) , similar to the total number found in other eukaryotic species [35] , indicative of a lack of duplication ( conversely to the above ) . The hypothetical T . vaginalis ABCD transporters are half transporters with the topology TMD-NBD , in common with all other non-plant eukaryotic ABCD proteins [9] . The majority of ABCD transporters have been localised to peroxisomes , where they are implicated in the transport of VLCFAs and other co-enzyme A conjugates into the peroxisome for β-oxidation . To investigate their sub-cellular localization , ABCD transporters TVAG_470720 and TVAG_605460 were expressed in T . vaginalis C1 cells with a C-terminal double haemagluttinin epitope , and were visualised using immunofluorescence microscopy ( Figure 5 ) . Control C1 cells were incubated with PI to detect the nucleus , and with anti-BiP antibody to detect the diffuse ER ( Figure 5A ) . This contrasts directly with an alternative organelle membrane marker - a hydrogenosomal TOM40 homologue [36] , which showed dozens of internal , discrete , vesicular structures with dimensions consistent with those of the hydrogenosome ( Figure 5B; [2] ) . Both the ABCD transformants showed a highly diffuse distribution similar to that observed with BiP ( Figure 5C , D ) with TVAG_470720 also showing some additional perinuclear signal . Attempts to demonstrate co-localization with anti-BiP were confounded by this extreme diffuseness . Our argument for the ABCD transporters being localized to the ER is further supported by both the absence of peroxisomes from T . vaginalis , and by recent localization of a subset of ABCD proteins to the ER rather than to the peroxisome in humans and mice [37] . Notably , both TVAG_470720 and TVAG_605460 belong to this latter subset , rather than to the “classical” peroxisomal ABCD proteins ( Figure 5E ) . Twenty-seven proteins constitute the hypothetical ABCB subfamily in T . vaginalis , the second largest subfamily , comprising a significant proportion ( 28% ) of all ABC proteins - a characteristic shared by P . falciparum ( 44% ) and E . histolytica ( 27% ) . Such findings reflect the importance of ABCB transporters in these parasites and raise the possibility of ABCB-specific gene amplification having occurred . All hypothetical ABCB proteins have the same TMD-NBD topology as ABCA transporters , but lack the defining extracellular loop of the latter family , and range in length from 477–733aa . Additionally , and distinct from ABCA transporters , the ABCB members all contain consensus signature sequences , with a single exception . In humans , ABCB proteins , both full transporters at the plasma membrane and half transporters dimerising intracellularly , have been implicated in various roles ranging from drug resistance ( ABCB1 or MDR1 ) to peptide transport into the ER ( ABCB2 and B3 ) and iron homeostasis in mitochondria ( ABCB6 ) [7] . In T . vaginalis , the closest sequence to mammalian ABCB1/P-glycoprotein is TVAG_542450 ( Tvpgp1; [27] ) , however research has not supported an involvement of Tvpgp1 in resistance to metronidazole [27] . For other eukaryotic ABCB transporters , including Atm1 , convincing homologues in T . vaginalis could not be identified by sequence analysis alone and further localization and functional studies will be required . The evolution of the ABCB family in T . vaginalis was examined by constructing phylogenetic trees employing the same criteria as applied to the ABCA sequences above , i . e . eukaryotic full length transporters had their NBD sequence extracted and these were then aligned and neighbour-joining trees generated using bootstrap analysis ( Figure S1 ) . A similar conclusion to that regarding the T . vaginalis ABCA proteins is reached , i . e . that despite the absence of full length ABCB transporters , the proteins form two distinct clusters which mimics the N- and C-terminal NBDs of full length eukaryotic ABCB proteins , suggesting that evolutionary pressure has acted on the T . vaginalis half transporters as it has on the full transporters . ABCE sequences are absent from the eubacteria but present in all Archaea and eukaryotes for which genomic sequencing information is relatively complete . As expected , T . vaginalis contains a single homologue of human ABCE , and of all the ABC proteins the certainty that can be ascribed to TVAG_249850 as being ABCE is highest . T . vaginalis ABCE is 54–57% identical at the amino acid sequence level to other eukaryotic ABCEs , and 39–46% identical to those from Archaea ( Figure 6 ) . This degree of conservation is remarkable , to date only Hsp70 has been shown to have a similar level of conservation to homologues in both eukaryotes and Archaea [26] , [38] . In spite of a structural description of ABCE [39] a complete understanding of the function of ABCE remains unresolved , although roles in translational control , ribosome assembly , and ribosome recycling [40] , have been proposed . Clearly , its sequence conservation across the eukarya and Archaea argues for a function critical to the evolution of cell biology in these kingdoms [26] . T . vaginalis contains a larger number of predicted ABCF proteins than other parasites listed in Table 2 . Consistent with other species' ABCF proteins , the hypothetical T . vaginalis ABCF subfamily is another group of non-transporters composed of two fused NBD domains ( NBD2 ) and lacking membrane-spanning regions . Taxonomic BLAST searches with TVAG_427530 highlight the high level of conservation shown by the predicted T . vaginalis ABCF proteins , with E values as low as 9e-112 and identity as high as 42% with sequences from other species . Similar analysis with TVAG_385840 indicate that this is the Trichomonas homologue of yeast GCN20 , an activator of eukaryotic translational initiation factor 2α-kinase ( eIF2α-kinase ) [41] , showing 35% sequence identity ( p-value of 3 . 9e-88 ) . Confirmation of the function of Trichomonas ABCF proteins in translational control awaits further experimentation . Our analysis of the ABC transporters in T . vaginalis demonstrates three key findings with broader impact for our understanding of the parasite's biology . The first is the absence of full length ABC transporters . This is unique in eukaryotes for which we have sufficient sequence data . All other species described as early branching ( e . g . mosses ) , and others classified as Excavata contain full-length ABC transporter genes ( see footnote to Table 1 ) . The absence of these from T . vaginalis , taken together with our data here on the maintained sequence separation of the half-transporters in the ABCA and ABCB sub-families suggests that either the full-transporter gene was an early loss in the evolution of T . vaginalis from a common ancestor with other eukaryotes , or that gene fusion events that produced full length ABC transporters in other extant eukaryotes have not been selected for in T . vaginalis . Another finding is the putative suppression of stop codons by Trichomonas . The expression of the ABCA half-transporter TVAG_415980 and TVAG_415990 as a single protein warrants further investigation of the mechanism for this suppression and its frequency . Finally , our confocal microscopy work shows that sub-cellular localization studies in T . vaginalis are accessible enabling further proteomic classification of this organism .
|
The parasite Trichomonas vaginalis infects in excess of 100 million people per year , and is a contributory factor to enhanced transmission rates of HIV , the causative virus in AIDS . As such , T . vaginalis infection is an important public health concern . Understanding the biology of the organism is important to determine aspects of the response to drug treatment , host:parasite interactions and so on . We have investigated an important family of proteins – the ATP binding cassette transporters – which are present in the membranes of all cells , and which contribute to a diverse spectrum of important cellular processes . The ABC transporters of T . vaginalis were identified by analysis of primary amino acid sequence data , and examined by subsequent protein and gene expression studies . Our most important conclusion is that – uniquely amongst eukaryotes - T . vaginalis has no ABC transporters capable of acting as monomers . In other words , its ABC transporters must all act by forming functional complexes with other ABC proteins . This has implications for our understanding not just of the parasite's biology , but also its evolution . In summary our analysis opens up the path for future research of individual members of the ABC protein family in T . vaginalis .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"and",
"Discussion"
] |
[
"medicine",
"parasitic",
"diseases",
"biological",
"transport",
"sequence",
"analysis",
"infectious",
"diseases",
"proteins",
"membranes",
"and",
"sorting",
"biology",
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"cell",
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"transmembrane",
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"proteins",
"computational",
"biology",
"molecular",
"cell",
"biology",
"metabolism"
] |
2012
|
The ATP-Binding Cassette Proteins of the Deep-Branching Protozoan Parasite Trichomonas vaginalis
|
The emerging disease Buruli ulcer is treated with streptomycin and rifampicin and surgery if necessary . Frequently other antibiotics are used during treatment . Information on prescribing behavior of antibiotics for suspected secondary infections and for prophylactic use was collected retrospectively . Of 185 patients that started treatment for Buruli ulcer in different centers in Ghana and Bénin 51 were admitted . Forty of these 51 admitted patients ( 78% ) received at least one course of antibiotics other than streptomycin and rifampicin during their hospital stay . The median number ( IQR ) of antibiotic courses for admitted patients was 2 ( 1 , 5 ) . Only twelve patients received antibiotics for a suspected secondary infection , all other courses were prescribed as prophylaxis of secondary infections extended till 10 days on average after excision , debridement or skin grafting . Antibiotic regimens varied considerably per indication . In another group of BU patients in two centers in Bénin , superficial wound cultures were performed . These cultures from superficial swabs represented bacteria to be expected from a chronic wound , but 13 of the 34 ( 38% ) S . aureus were MRSA . A guide for rational antibiotic treatment for suspected secondary infections or prophylaxis is needed . Adherence to the guideline proposed in this article may reduce and tailor antibiotic use other than streptomycin and rifampicin in Buruli ulcer patients . It may save costs , reduce toxicity and limit development of further antimicrobial resistance . This topic should be included in general protocols on the management of Buruli ulcer .
Buruli ulcer ( BU ) is a neglected , emerging disease caused by Mycobacterium ulcerans . BU usually starts as a nodule , papule , plaque , or oedema . When left alone , the lesion breaks open and a typical painless ulcer with undermined edges appears which can progress to a large necrotic lesion . Sometimes the bone can be affected and amputation may be necessary . Until 2004 , the only available treatment was surgical removal of affected tissue . Since 2004 , streptomycin and rifampicin have been used to treat BU [1]–[3] . Secondary infection is often thought to be responsible of severe complications in BU [4]–[6] . In chronic diabetic foot ulcers and thermal burn wounds , secondary infections increase time to healing and prolong hospital stay [7]–[10] . The Infectious Diseases Society of America ( IDSA ) guidelines on diabetic foot infections states that infection should be diagnosed clinically on the basis of the presence of purulent secretions ( pus ) or at least 2 of the cardinal manifestations of inflammation ( redness , warmth , swelling or induration , and pain or tenderness ) [11] , [12] . Although the incidence of secondary infections in BU is unknown , antibiotics may be frequently prescribed for this indication . It is equally unknown which bacteria these antibiotics should target and what the susceptibility of these bacteria is . Furthermore , the prescribing behaviour of antibiotics used as prophylaxis after surgery or skin grafting is unknown . Only limited data are available on resistance of microbes in Bénin and Ghana; a study in Ghana found that 18% of the S . aureus were MRSA [12] . The few studies published on the prevalence of MRSA in Bénin , showed percentages varying from 17–36% depending on the type of samples studied [13] , [14] . Resistance patterns of E . coli were described in faeces from healthy volunteers and patients with diarrhea . No Extended-Spectrum beta-lactamase ( ESBL ) producing enterobacteriaceae were found in these two studies , but there was a high resistance to locally used antibiotics [15] , [16] . In a 500 bed hospital in Abomey 22% of the E . coli were ESBL positive [17] . An antibiotic policy that is adjusted to the expected microbes and resistance of these microbes can make antibiotic use more effective , with lower daily defined doses of antibiotics per person , leading to less side-effects , and with less resistance . Such policy will also save resources in an environment where resources are scarce . The prescribing behaviour of antibiotics for secondary infections or as prophylaxis in BU interventions was studied , and cultures of ulcers were taken to provide data for the development of future guidelines for the use of antibiotics for this indication .
Data were retrieved from files of patients that started treatment with streptomycin and rifampicin in the period August–October 2009 in the ‘centre de dépistage et de traitement de l'ulcère de Buruli’ , Lalo , Bénin . The same data were retrieved from hospital files on patients that started treatment in the period August–December 2009 in the ‘centre dépistage et de traitement de l'ulcère de Buruli’ , Allada , Bénin , and in the period March 2008–March 2009 in Agogo Presbyterian Hospital , Agogo , Ghana . Records were studied in August and September 2010 , so that follow-up of these patients was already completed . Patient characteristics and the type of lesion were recorded . Data on antibiotic use different from rifampicin/streptomycin , as well as the indication for these prescriptions , the dosage and duration as well as the clinical presentation at the start of treatment were retrieved . Between October and December 2010 , 20 consecutive patients with an ulcerative lesion reporting for BU treatment were enrolled after consent and followed longitudinally . Before the start of treatment , and before washing or the application of antiseptics , a swab was taken both from the undermined edge and from the center of the ulcer . Swabs were also taken at 6 weeks after start of treatment and at 12 weeks ( this is 4 weeks after finishing the 8 weeks rifampicin/streptomycin ) . Apart from these 20 patients followed in time , we intended to enrol 25 patients in the villages and 25 patients admitted in the hospitals after consent with only one culture taken at a random moment during or after treatment . The clinical presentation ( including local and/or systemic signs of a secondary infection ) and previous use of antibiotics was recorded for all patients . Results of the cultures were not reported to the treating physicians in order to avoid interference with antibiotic prescribing that would be based on superficial cultures . Samples were cultured on a medium of Trypcase Soy Agar+5% Sheep Blood and Chapman Agar ( both Biomerieux EMB agar ) . The different microbial isolates were differentiated and antimicrobial susceptibility was performed on Müller Hinton agar with antibiotic discs ( Rosco Diagnostica ) . Detection of methicillin resistance was done on Műller Hinton agar with the addition of 5% NaCl . The results of the antibiograms were reported as either susceptible , intermediate resistance , or resistant . Apart from the antibiotics tested in the routine setting , rifampicin and clarithromycin discs were added to the antibiograms of gram positive microbes , and streptomycin discs were added to antibiograms of both gram positive and negative microbes . Cultures and antibiotic susceptibility testing were performed at the National Public health Laboratory , Cotonou . The method used for susceptibility testing was the agar medium diffusion method ( Kirby Bauer method ) . The internal quality control is done with the following reference strains:E . coli ATCC25922 and S . aureus ATCC25923 . The laboratory participates in international quality control with the following organizations: Institute Pasteur in Paris , the WHO Collaborating Centers Faro in Marseilles ( France ) , and MDSC in Ouagadougou , Burkina Faso . From the 20 patients with cultures taken at the same time from the center and the border of the ulcer , only the results of the border of the ulcer were used for the descriptive analysis . The protocol and consent forms of the study were approved by the ethical review committee of the Ministry of Health ( Direction de la Formation et de la Récherche en Santé , nr IRB6860 ) . For participants in the part of the study obtaining swabs from the Buruli ulcer lesions , written and verbal informed consent or assent was obtained from all participants aged 12 years or older , and consent from parents , care takers , or legal representatives of participants aged between 12 and 18 years of age .
In total , 185 patients started treatment with streptomycin and rifampicin in the study periods . 147 Patients had an ulcer , 38 had a plaque as the only lesion . Four patients ( 2 . 7% ) had both an ulcer and a nodule . Median age was 12 years old . Two patients were known to be HIV positive; the other patients did not have a relevant medical history . Of these 185 patients , 51 were admitted because of the severity of the disease or because of distance to health care center . Of the 51 admitted patients , 40 ( 78% ) received at least one course of antibiotics other than streptomycin and rifampicin during their admission . The median number ( IQR ) of antibiotic courses for admitted patients was 2 ( 1 , 5 ) , with a maximum number of courses of 13 . In Table 1 , the different antibiotic strategies for suspected secondary infections and prophylaxis extended after three different surgical interventions are presented . Apart from the antibiotics prescribed in Table 1 , two patients received treatment for suspected sepsis with a secondarily infected BU lesion as focus . One of them received ampicillin , gentamicin and metronidazole and the other received ceftriaxone and metronidazole . Another patient received ampicillin and metronidazole as prophylaxis extended after bone surgery for disseminated BU . Different antibiotic combinations were started during the treatment with streptomycin and rifampicin sixteen times . Median number of days passed between start of streptomycin and rifampicin and the first time other antibiotics were prescribed was 63 days . The clinical signs reported when starting a course of antibiotics to treat a Buruli ulcer related infection were diverse ( Table 2 ) . This prescribing behaviour resulted in a high number of antibiotics prescribed per 100 patient days of hospitalization ( Table 3 ) .
Currently there is no specific guideline for the prescription of additional antibiotics to BU patients suspected to have secondary infection . However , our findings indicate that prescribing additional antibiotics is widespread among BU patients that are admitted to the hospital because of the severity of their BU or distance to health care . Moreover , the type and duration of these antibiotic courses is highly variable , even within the same indication . Antibiotics were most frequently prescribed as prophylaxis of secondary infections extended after surgical procedures rather than for the treatment of suspected secondary infections . Surprisingly , duration of this prophylaxis was even longer than for actual treatment of secondary infections . This frequent use of antibiotics leads to unnecessary costs , antibiotic resistance , and side-effects for the patients with possible long term consequences . For example , prescribing gentamicin during the therapy with rifampicin and streptomycin imposes a serious risk of aminoglycoside toxicity . In general , skin surgery is not an indication for antimicrobial prophylaxis , therefore prophylaxis after excision and after debridement [18] is not considered as indicated . However , there is some inconclusive evidence that in skin grafts systemic perioperative antibiotic prophylaxis contributes to the autograft survival [19] . Ramos et al . found a rate of autograft survival for the group of patients with burns using two days of perioperative antibiotic prophylaxis of 97% versus 87% in the group without prophylaxis [20] . In patients with arterial and venous ulcers no differences in graft survival was observed with perioperative use of antibiotics [21] . As stated by the IDSA guidelines it is unlikely that benefit is conferred by the administration of additional doses after the patient has left the operating room [18] , [22] , [23] . Antimicrobial prophylaxis should certainly be discontinued within 24 hours of the operative procedure since prolonged antibiotic prophylaxis contributes to antimicrobial resistance [18] , [24] , [25] . In case clinicians decide to give perioperative antibiotics in skin grafting , advice for use of appropriate antibiotics is given in Figure 1 . Although MRSA is frequent and community acquired , suggested antibiotic therapy as prophylaxis in skin grafting in Figure 1 does not cover MRSA . The alternatives vancomycin or ofloxacin/ciprofloxacin are not appropriate as prophylaxis due to resistance development to these antibiotics and/or costs . Studies on the use of vancomyin as prophylaxis in medical centers with high MRSA prevalence are controversial in preventing surgical site infections [26] , [27] . Report of clinical symptoms of secondary infections may not always have been complete in the files , but different clinical signs were reported in patients receiving antibiotics for suspected secondary infection . These clinical signs seem nonspecific; in patients not clinically suspected to have a secondary infection , the same local and systemic signs were frequent as well . The diagnosis of a secondary infection of the BU therefore remains difficult to ascertain . A paradoxical response may be an alternative diagnosis in patients suspected of a secondary infection [28] . A superficial culture of the wound as done in the study is certainly not helpful in the diagnosis of a secondary infection and can not guide individual care [29] , [30] , [31] . Superficial cultures as done in this study , can not guide individual care . In this study , we performed cultures to make an inventory of isolates and local resistance patterns to enable the first steps to an antibiotic guideline . Superficial swabs yield a greater range of organisms than do deeper tissue material due to contaminants not involved in the secondary infection and yet may fail to identify some of the deep-seated organisms . If a culture is needed to guide antibiotic therapy , tissue specimens obtained by biopsy , ulcer curettage or aspiration are preferred [11] . Such cultures would have been preferable for this study but were not performed for ethical reasons . Such procedures are more invasive , and moreover , most included patients were not suspected to have a secondary infection and they would therefore not benefit from a deep tissue biopsy . Another limitation of the study is the limited number of positive cultures , yet we think this number of isolates reflects the micro-organisms in the superficial swabs in general and this information guides antibiotic treatment suggestion . Further studies are needed to gather more information on the susceptibility of the highly prevalent MRSA . Patients treated in the out-patient setting did not have reported use of antibiotics apart from streptomycin and rifampicin , but we are not informed on antibiotics that may have been used by these patients prescribed by other doctors than the doctors treating their BU or that patients bought without prescription . The high percentage of MRSA cultured is worrisome . The prevalence of MRSA was equally high among patients before start of treatment , suggesting this MRSA may be community acquired . In Benin , antibiotics are freely available , and are often used without prescription , and a recent study showed that BU patients often use left-over antibiotics to reduce pain and inflammation before reporting to the hospital [32] . Improved wound care and antibiotic therapies in case of clinical suspicion of a secondary infection of the BU are suggested in Figure 1 . This figure is based on availability , WHO Essential Drug Lists 2011 , costs and the high prevalence of community acquired MRSA . If treatment contains gentamicin , treatment with streptomycin and rifampicin should be stopped temporarily , to limit toxicity . Dosage of different antibiotics are given in Text S1 . Even though secondary infections were not frequent , the high prevalence of MRSA complicates treatment . Further studies are needed to have a more precise susceptibility pattern of S . aureus in this patient population . To deal with the currently found high prevalence of MRSA , facilities at the BU treating centers will have to be improved . Laboratory facilities that enable cultures and testing for MRSA are highly necessary along with knowledge about rational prescribing and the possibility to consult clinical microbiologists or infectious diseases specialist to help interpreting the results . In case patients are suspected to have a secondary infection not responsive to appropriate wound care , a deep tissue biopsy should be sent for culture before the start of antibiotic treatment . In case of systemic signs , blood cultures should also be performed if possible . Vancomycin is on the complementary list of the WHO Essential drug list 2011but it is not available in most centers . However , with the MRSA prevalence as found , it seems an essential drug . Its use is complicated by high costs and need for plasma drug concentration monitoring to limit toxicity . Treatment of osteomyelitis is not included in the figure; in the year 2000 a study showed that only in 16% of the osteomyelitis patients another germ than M . ulcerans was involved [33] Whether this percentage is still accurate is unknown and this should be studied during ongoing drug studies . Especially because of long treatment duration , in case of osteomyelitis , cultures should be sent from bone debridement/biopsy to guide antibiotic therapy for potential non-MU organisms , if possible . Strategies to optimize wound care in Buruli ulcer patients should be studied , as no information is currently available . However , all measures to prevent healthcare associated infections should be actively implemented in all facilities implementing wound care . Periodic routine culturing of wounds should be performed ( e . g . once every 2 years ) to remain updated on the prevalence of MRSA and possible development of resistance . Rational antibiotic prescribing behaviour should be stimulated in centers treating BU [34] , [35] Adherence to Figure 1 in prescribing antibiotics different from the streptomycin and rifampicin will have a major impact on antibiotic use in the BU treatment centers , saving money and toxicity and limiting of further antimicrobial resistance development . This topic should be included in general protocols on the management of BU .
|
Buruli ulcer ( BU ) is a neglected , emerging disease caused by Mycobacterium ulcerans . BU usually starts as a nodule , papule , plaque , or oedema . When left alone , the lesion breaks open and a typical painless ulcer with undermined edges appears which can progress to a large necrotic lesion . BU is treated with antibiotics ( streptomycin and rifampicin ) and surgery if necessary . Apart from these two antibiotics , patients frequently receive other antibiotics during treatment . In files from patients treated in Benin and Ghana we found that in admitted patients a median of two antibiotic courses were prescribed . Only twelve patients received antibiotics for a suspected secondary infection , all other courses were prescribed as prophylaxis of secondary infection extended till 10 days on average after excision , debridement or skin grafting . In another patient group in Benin , superficial wound swabs from Buruli ulcers were performed and showed a high rate of MRSA . We propose a guideline for rational antibiotic treatment for suspected secondary infections or prophylaxis . Adherence to the proposed guideline will have a major impact on antibiotic use other than streptomycin and rifampicin in Buruli ulcer patients , saving costs , toxicity and development of antimicrobial resistance .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"buruli",
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] |
2013
|
Towards Rational Use of Antibiotics for Suspected Secondary Infections in Buruli Ulcer Patients
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Flow cytometry is the prototypical assay for multi-parameter single cell analysis , and is essential in vaccine and biomarker research for the enumeration of antigen-specific lymphocytes that are often found in extremely low frequencies ( 0 . 1% or less ) . Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts , a process that is subjective and often difficult to reproduce . An alternative and more objective approach is the use of statistical models to identify cell subsets of interest in an automated fashion . Two specific challenges for automated analysis are to detect extremely low frequency event subsets without biasing the estimate by pre-processing enrichment , and the ability to align cell subsets across multiple data samples for comparative analysis . In this manuscript , we develop hierarchical modeling extensions to the Dirichlet Process Gaussian Mixture Model ( DPGMM ) approach we have previously described for cell subset identification , and show that the hierarchical DPGMM ( HDPGMM ) naturally generates an aligned data model that captures both commonalities and variations across multiple samples . HDPGMM also increases the sensitivity to extremely low frequency events by sharing information across multiple samples analyzed simultaneously . We validate the accuracy and reproducibility of HDPGMM estimates of antigen-specific T cells on clinically relevant reference peripheral blood mononuclear cell ( PBMC ) samples with known frequencies of antigen-specific T cells . These cell samples take advantage of retrovirally TCR-transduced T cells spiked into autologous PBMC samples to give a defined number of antigen-specific T cells detectable by HLA-peptide multimer binding . We provide open source software that can take advantage of both multiple processors and GPU-acceleration to perform the numerically-demanding computations . We show that hierarchical modeling is a useful probabilistic approach that can provide a consistent labeling of cell subsets and increase the sensitivity of rare event detection in the context of quantifying antigen-specific immune responses .
Flow cytometry is the prototypical assay for multi-parameter single cell analysis , and is essential in vaccine development , monitoring of T cell-based immune therapies and the search for immune biomarkers . In many clinical research applications , the cell subsets of interest are antigen specific T lymphocytes that are often found in extremely low frequencies ( 0 . 1% or less ) . These antigen-specific T cells can be detected using HLA-peptide multimers or by their expression of effector proteins upon specific antigen stimulation in intracellular staining ( ICS ) assays . Current methods of flow cytometry analysis rely on visual gating of cell events to identify and quantify cell subsets of interest . However , the choice of sequence for the dot plots ( gating strategy ) and where to draw the gating boundaries is highly dependent on assay protocols and operator experience and may not be easily harmonized , as illustrated in recent international proficiency panels [1] , [2] . There has therefore been increasing interest in the use of objective , automated methods for cell subset identification [3] . One approach that we and others have promoted is the use of statistical models to estimate the data distribution [4]–[6] , followed by a mapping of summaries of the statistical distribution to cell subsets of biological interest . This model-based approach tends to be more numerically intensive than other ad hoc approaches to data clustering , but as we have previously demonstrated , this can be overcome by exploiting the cheap massively parallel capabilities of modern graphical processing units ( GPUs ) . Importantly , the model-based approach has the advantage of using a declarative probabilistic framework that can be extended using well-established and understood mechanisms to improve discriminative power . In particular , hierarchical models that incorporate information from both the individual and group levels when fitted to flow cytometry data samples can increase both interpretability and sensitivity . These hierarchical models increase interpretability by aligning clusters in a way that enables direct comparison of cell subsets across data samples , and increase sensitivity for detecting very low frequency cell subsets by sharing information across multiple samples . Hierarchical models thus improve the ability of model-based approaches to detect low frequency event subsets , and enable the comparative analysis that is essential to any downstream analysis of multiple data samples . We briefly describe three alternative software packages for automated analysis to contrast the approach of HDPGMM . FLOCK 2 . 0 ( FLOw cytometry Clustering without K ) [7] is widely used because it is a resource provided by IMMPORT ( Immunology Database and Analysis Portal ) , a repository of data generated by investigators funded through the NIAID/DAIT . Similar to DPGMM and HDPGMM , FLOCK is able to estimate the optimal number of data partitions from the data . However , FLOCK uses an adaptive multi-dimensional mesh to estimate local density followed by hierarchical merging of adjacent regions based on density differentials rather than a mixture model , and does not appear to either provide a statistical model ( e . g . for goodness-of-fit calculations ) or methods for alignment of cell subsets across different samples . In contrast , flowClust [6] and FLAME ( FLow analysis with Automated Multivariate Estimation ) [5] both use a statistical mixture model approach for density estimation and clustering . Both packages are likely to be widely used , since flowClust is provided as a library in R/BioConductor , and FLAME is part of GenePattern . Apart from the choice of base distribution ( T distribution for flowClust and skewed distributions for FLAME ) , the main differences with DPGMM are the use of optimization ( Expectation-Maximization ) rather than simulation ( MCMC ) to estimate the density , the need for the user to specify the number of partitions and differences in the type of transform applied in data pre-processing . FlowClust does not provide any method to align cell subsets across samples , while FLAME provides a heuristic algorithm to do so as described in their original publication [5] . Unlike HDPGMM , none of the three algorithms use a hierarchical approach to model group and individual specific effects . With this in mind , the developments reported here concern the implementation of a hierarchical Gaussian mixture model based on a Dirichlet process prior , and extensions of the basic model to identify and quantify rare cell subsets in flow cytometry data . Simulated data is first used to demonstrate the advantages of hierarchical models over conventional clustering approaches . This is followed by validation of the model on experimental samples , using retrovirally TCR-transduced T cells that are spiked into autologous peripheral blood mononuclear cell ( PBMC ) samples to give a defined number of antigen-specific T cells [8] . Finally , the reproducibility and accuracy of this approach for rare cell quantification is compared to that of standard DPGMM and manual analysis performed by a group of ten flow cytometry users , and compared with the results from FLOCK , FLAME and flowClust . The basic concept in model-based approaches is to consider events in a flow cytometry data set as being random samples drawn from a multi-dimensional probability distribution . The objective of analysis is then to define the probability distribution model and evaluate inferences over the model parameters based on fit to the specific data set . Statistical mixture models are a standard approach for the construction of the underlying distribution , using the sum of many simpler probability distributions ( e . g . multivariate Gaussian , Student-t or skewed distributions ) to approximate arbitrary multi-dimensional distributions . For biological interpretation , fitted models are then used for clustering , i . e . using statistical properties of individual events to assign them to biological cell subsets . For example , with statistical mixture models , this can be done by grouping events with the highest probability of coming from a specific mixture component together , or merging of multiple components using specified criteria such as having a common mode in the estimated distribution over markers [9] , [10] . Of course , the number of distinguishable cell subsets and Gaussian components necessary to fit the model satisfactorily is not known in advance . To avoid having to specify the number of mixture components needed in the model , we use a Dirichlet process prior in which the number of components necessary is directly estimated from the data [11] . Computationally , the use of Dirichlet process priors is more efficient than fitting multiple models with different numbers of components and testing with some penalized likelihood ( e . g . Akaike or Bayesian information criteria ) to choose the best model , as only a single model fit is performed . Since we use multivariate Gaussian distributions as components , the overall approach is described as a Dirichlet process Gaussian mixture model ( DPGMM ) . DPGMM are extremely flexible models that can fit flow data from flow cytometry experiments using different antibody-fluorochrome labels ( e . g . 4-color HLA-peptide multimer and 11-color intracellular staining ( ICS ) panels ) , and a natural evolution of the fixed Gaussian mixture models we originally proposed [4] . Finally , while the model uses Gaussian components , cell subsets are identified with merged components using the consensus modal clustering strategy described in Methods . As a result , cell subsets can have arbitrarily complex distributions and are not restricted to symmetric Gaussian clusters . Clustering methods applied to data samples independently face two major limitations . The first is that cluster labels are not aligned across data samples , posing a problem for comparing subsets across multiple samples which is usually the purpose of the original experiment . The second is that there are limits to the ability of clustering models to identify very rare event clusters due to masking by abundant event clusters [12] . In particular , this makes it difficult to identify clusters matching antigen-specific HLA-peptide multimer labeled or polyfunctional T cells in ICS assays that may be biologically meaningful at frequencies of 0 . 1% or lower . We show in this paper that both issues are successfully addressed by the use of hierarchical Dirichlet process Gaussian mixture models ( HDPGMM ) . Hierarchical , or multi-level models , represent individual events in flow cytometry data as being organized into successively higher units . For example , individual events belong to a sample , and a sample may belong to a collection of similar samples . The critical idea is that cell subset phenotypes that are common across data samples can be used to inform and hence better characterize events in individual samples . For example , one hierarchical Dirichlet process model formulation partitions components into those common across data samples and those unique to a specific sample [13] , [14] – this provides a different notion of sharing that is useful for identifying fixed and variable components across heterogeneous data samples but lacks a straightforward alignment of all clusters necessary for multi-sample comparison . Instead , we model information sharing by placing all data samples under a common prior , such that the mean and covariance in any of the individual sample Gaussian components are shared across all samples , but the weight ( proportion ) of the component in each sample is unique . As described by Teh et al ( 2006 ) [15] , this can be achieved by using a set of random measures , one for each data sample , where is distributed according to a sample-specific Dirichlet process . The sample-specific DPs are then linked by a common discrete prior defined by another . This hierarchical model leaves the cluster locations and shapes constant across datasets , and hence aligns the clusters in that the location of the normal components is common to all data samples . As depicted in the summary schematic of the HDPGMM model shown in Figure 1 , there are basically 6 parameters that control the sensitivity . The parameter controls the spread of the ( standardized ) cluster means and controls how informative our prior is about the shape of the covariances . The default for these parameters is vague and it is our opinion that and should not be tuned since it is unlikely that a user is knowledgeable about these constraints . The next set of parameters and are hyper-parameters for the Gamma distribution on which controls the overall number of clusters . Small values of will encourage fewer clusters and large values of will encourage more clusters . The mean and variance of the Gamma distribution are and respectively , and the default is set such that both mean and variance are 1 . As an example of how we can tune this , if we set , the variance will be fixed , and the mean will vary as – in that case we can encourage larger values of and more clusters by choosing small values of . The final set of parameters and are hyper-parameters for the Gamma distribution on which specifies how similar the weights for each sample are to the other samples' distribution – when is small , the amount of information shared is small ( weights for each batch can be very different from the overall distribution ) ; when is large , the weights for each batch are likely to be similar to the base distribution . Tuning of via and is analogous to tuning via and . In the context of flow cytometry , a data sample typically consists of an by data matrix from a single FCS file , where there are events and features reporting scatter and fluorescent intensities . The HDPGMM is a model that fits a collection of such data samples , and makes the assumption that the same cell subsets are present in every sample with frequencies that vary from sample to sample . The model does not make any further assumptions about whether the samples in a collection come from the same or different subjects , experimental conditions , treatment groups etc . Different flow cytometry technologies generate data sets that mainly vary in the maximum number of features that can be observed rather than in the standardized locations of cell subsets or their covariances , and hence and do not need tuning . With more features , it is likely that more cell subsets can be distinguished , and it would be reasonable to tune and to encourage larger values of . The values of and do not depend on the flow cytometry technology , but rather on how similar or different samples are from each other , and can be tuned accordingly . The number of mixture components that are needed for a good model fit is also likely to increase , and we present a diagnostic for model goodness-of-fit that can be used to guide choice of the lower bound for the number of components used in the results and discussion . The hierarchical DP mixture model allows information sharing over data sets . In the hierarchical model , each flow cytometry data sample can be thought of as a representative of the collection of data samples being simultaneously analyzed . The individual data samples then provide information on the properties of the collection , and this information , in turn , provides information on any particular data sample . In this way , an HDPGMM fitted to a single data sample “borrows strength” from all other samples in the collection being analyzed . In other words , if a rare cell subtype is found in more than one of the samples , we share this information across the samples in the collection to detect the subtype even though the frequency in a particular data sample may be vanishingly small . HDPGMM thus increases sensitivity for clustering cell subsets that are of extremely low frequency in one sample but common to many samples or present in high frequency in one or more samples . In principle , there is no lower limit to the size of a cluster that can be detected in a particular sample . In practice , vanishingly small clusters ( e . g . 3–5 events out of 100 , 000 ) require expert interpretation to distinguish background from signal , but it is not uncommon for biologically significant antigen-specific cells to be present at such frequencies .
We illustrate the ability of hierarchical modeling to simultaneously overcome the problem of masking of rare event clusters and provide an alignment of cell subsets over multiple data samples . Four simulated data sets were created , each with up to 4 bivariate normal clusters in 4 quadrants . Clusters in each quadrant may have different means or covariance matrices , or be absent entirely; see Figure 2 . We compared four different approaches to clustering the data – independent fitting of DPGMM to each data sample , using a reference data set , using pooled data , and using hierarchical modeling . To evaluate the utility of HDPGMM for identifying rare event clusters in real data , we used reference cell samples containing a predefined number of T cells with known TCR specificity for the NY-ESO-1 cancer-testis antigen . TCR-transduced cells were added to autologous PBMC samples at final concentrations of 0% , 0 . 013125% , 0 . 02625% , 0 . 0525% , 0 . 105% and 0 . 21% [8] . There is also a small background contribution by antigen-specific T cells that are already present in the unspiked sample , which is estimated to be 0 . 0154% using the mean frequency from manual gating by 10 flow practitioners . A total of 50 , 000 events was then collected from each sample for analysis . At the highest spike frequency , we would therefore expect to detect a maximum of 0 . 2254% , or 113 antigen-specific T cell events out of 50 , 000 total events . This is a challenging clustering problem as the frequency of expected multimer-positive events is extremely low , but ideal for validation since the expected number of T cells that bind with high-affinity to the HLA-peptide multimer is known . DPGMM and HDPGMM models were separately fitted to these six data samples using the FSC , SSC , CD45 , CD3 and HLA-multimer channels ( 5 dimensional ) , using a truncated Dirichlet process with 128 mixture components , 20 , 000 burn-in steps and 2 , 000 identified iterations to calculate the posterior distribution as described in Methods . The trace plots of log-likelihood shown in Figure 4 provides evidence for model convergence , and the distribution of mixture component proportions in Figure 5 provides evidence for model goodness of fit . After consensus modal clustering , the multimer positive clusters were defined using the gating scheme shown in the left panel of Figure 3 , but applied to event clusters found by HDPGMM rather than individual events . Since the clustering is done in the full set of markers rather than in two-dimensional slices , events that look close together in a particular projection but are further apart when all dimensions are considered will not belong to the same cluster . The frequency of multimer-positive events as a percentage of all 50 , 000 events was then calculated . We also ran trials of HDPGMM to evaluate the lower bound needed to find the antigen specific clusters in all samples; 3 out of 4 runs were successful with 32 components , and all runs were successful when 40 or more components were used . A side-by-side comparison of manually gated , DPGMM and HDPGMM classifications is shown in Figure 3 . All 3 approaches are comparable in terms of being able to identify and quantify the antigen-specific cluster of events . Across all runs , DPGMM consistently finds occasional outlier events that are likely to be false positives ( e . g . the CD3 negative to low events in the DPGMM fits shown in rows 1 and 4 ) . HDPGMM does not appear to suffer from the same false positive detection , and is also more sensitive for the samples with the lower spiked-in frequencies than DPGMM . However , the most striking advantage of HDPGMM over DPGMM is the interpretability of the hierarchical modeling – cell subsets are consistently labeled across data samples , allowing direct comparison of any cell subset of interest , not just of the multimer positive events . Figure 6 shows the results from the application of FLOCK , FLAME and flowClust on the same data set . FLOCK only detects the antigen-specific cell subset at the highest spiked-in concentration with a moderate number of probable false positive events that are CD3-negative . As indicated by the color coding of events , FLOCK does not provide any alignment of cell subsets across samples . Using the default settings , FLAME failed to identify any antigen-specific cell subsets . Cell subsets found were aligned but there were alignment artifacts when the event partitioning was different across samples ( arrowed example ) . Using a 64 component mixture , flowClust only detects antigen-specific clusters at the highest spiked-in concentration , and does not provide any alignment of cell subsets . Unlike FLOCK and Dirichlet process based models , the number of components for FLAME and flowClust is not estimated from the data . Hence , in practice , one would have to fit a variety of models with different numbers of components and subsequently perform model selection when using FLAME or flowClust . In Figure S1 , we compare HDPGMM , FLAME and flowClust models with 48 components fitted to the same data set . HDPGMM completed in 3 hours and 30 minutes ( 20 , 000 burn-in and 2 , 000 identified iterations ) , FLAME took 4 days 12 hours and 28 min , and flowClust completed in 25 minutes ( 1 , 000 iterations ) . With 48 components , HDPGMM found antigen-specific clusters in all samples . FLAME found the clusters when the spiked in concentration was greater or equal to 0 . 02625% , but cluster alignment failed with the error “missing value where TRUE/FALSE needed” . In contrast , flowClust did not detect any antigen-specific clusters . Both HDPGMM and FLAME clusters included a fair number of CD3-negative events , in agreement with the goodness-of-fit analysis shown in Figure 5 that 48 components is inadequate for modeling rare event clusters in this data set . We tried to run FLAME with 128 components but this was not practical since the program did not terminate after more than 10 days . It took 26 hours for flowClust to run 1 , 000 iterations with 128 components , and 4 out of 6 samples gave “NA” indicating missing data for all cluster centroids . The wide variation in run-times seen with flowClust ( 25 minutes to 26 hours ) probably reflects early termination with fewer than 1 , 000 iterations due to tolerance thresholds being met in the 48 component case . We suspect that the missing data might be caused by the Expectation-Maximization algorithm failing when there are zero-event components , but cannot confirm this since the program terminated with no error messages . Finally , to evaluate the robustness of the DPGMM and HDPGMM frequency estimates , the fitting was repeated 10 times for each algorithm using different random number seeds , and compared to manual gating results from 10 users . Manual gating was performed by operators who were instructed to gate using the same sequence of 2D plots ( common gating strategy ) , but were free to set gate boundaries within any given plot . The results are shown in Figure 7 . With respect to linear regression , all three methods perform comparably well with respect to correlation coefficient , but manual gating has slightly less deviation from a straight line fit than HDPGMM which in turn is better than DPGMM . From the figure , it can also be seen that HDPGMM is more accurate than manual gating in that the absolute deviation of the median of the estimates from the “true” concentration is lower than that for manual gating at every concentration . Since the “true” value is taken to be background estimated from 10 manual estimates in the autologous PBMC only sample added to the known spiked-in frequency , accuracy is not evaluated for autologous sample alone . In Figure 8 , we show that the algorithm is robust to changes in the hyper-parameters across a 9-fold range .
We have shown that HDPGMM improves on fitting individual samples with DPGMM in two ways - 1 ) it aligns clusters , making direct comparison of cluster counts across samples possible , and 2 ) by sharing information across samples , it can identify biologically relevant cell subsets present at frequencies in the 0 . 01–0 . 1% range , since “real” cell subsets would naturally be expected to be present in multiple data samples . The hierarchical model is also preferable to using a reference data sample or pooling the data from all samples , since individual sample characteristics are lost with these alternative strategies . Unlike HDPGMM , other approaches for automated flow cytometry analysis treat data in the same way as DPGMM , that is , fitting a model to independent samples separately , then using a heuristic or algorithm to match up clusters in one data set with another . However , since the model fitting is performed independently , the way that events are partitioned in individual data sets into clusters may be different even across samples that are otherwise very similar , resulting in poor alignment as seen in the FLAME analysis . We are not aware of any other automated flow cytometry analysis software that directly models contributions from individual and grouped samples to align cell subsets , and believe that the HDPGMM approach fills a useful niche in multi-sample comparisons , especially for the quantification of rare event clusters . One limitation of the HDPGMM model is that all the data to be fitted need to be simultaneously available . This is not an issue for most studies , but may be limiting for longitudinal studies that collect samples serially over an extended period where interim analyses need to be performed . Even in these cases , it may be useful to batch process cell samples in stages using a hierarchical model , then perform post-processing to align cell subsets over different stages . Because of information sharing , cell subsets that are consistent across data samples will be extremely robust features in the posterior distribution . Hence , it is likely that features across batches will be more consistent and easier to align for HDPGMM-fitted batch samples than if every sample was independently fitted . As described in the text , HDPGMM achieves alignment by assuming that the cluster locations and shapes are constant across datasets , and only their proportions vary from sample to sample . This is similar to the standard practice of using a gating template common to all samples for manual analysis . However , the HDPGMM approach has several advantages over the use of a common gating template . Because the locations and shapes of the clusters are inferred from the data and not imposed top-down by an expert , there is less risk of a subjective bias and failure to detect novel cell subsets . Since classification of events is done by assignment to the maximum probability cluster , cell subsets are not demarcated by arbitrary ( typically polygonal ) boundaries . In addition , it is simple to tune for higher sensitivity or specificity depending on experimental context by setting the probability necessary for an event to be included in a cluster; events that fall below this threshold are considered to be indeterminate . However , clusters that are doubly rare in the sense of being found in only a small proportion of the samples , and which also constitute a tiny fraction of the total events in any given sample , risk being masked by other more common and high abundance clusters . In many cases , this limitation can be addressed by the inclusion of appropriate positive controls in the samples . Where such positive controls are not available , a post-processing step to scan for “anomalous” events that are found in extremely low probability regions of the posterior distribution at higher frequencies than predicted , may be effective for identifying these doubly rare events . Technically , our implementation of the HDPGMM integrates several innovations necessary to make such hierarchical models a practical tool for flow cytometry analysis , including the use of a Metropolis-within-Gibbs step for sampling , an identification strategy to maintain consistent component labels across iterations that allows us to calculate the posterior distribution from multiple MCMC iterations , and a consensus modal map to merge components in such a way that non-Gaussian cell subsets are aligned across multiple data sets . To ensure scalability , we have implemented Message Passing Interface ( MPI ) and Compute Unified Device Architecture ( CUDA ) optimized code that can take advantage of multiple CPUs and GPUs from a cluster of machines to fit a single HDPGMM model to multiple data sets . We provide software for HDPGMM fitting to flow cytometry data sets , together with pre-specified robust default parameters and hyper-parameters that make practical usage simple . In our experience , we have never needed to adjust these parameters for data sets ranging from 3-color to 11-color flow cytometry data sets . The only parameters we individually set are the number of burn-ins , the number of iterations to collect for the posterior distribution , and the maximal number of components for the truncated DP algorithm . These parameters are tuned mainly for computational efficiency since conservative defaults that would be expected to be effective in all common use cases can be given , with the trade-off being longer run times . In addition , the use of prior information to set the starting values for component means and covariances ( e . g . from fits to previously collected similar data ) would reduce the number of iterations necessary to achieve convergence . The fitting of HDPGMM is computationally demanding but can be accelerated with cheap commodity graphics cards as previously described [16] . For example , running an MCMC sampler for 20 , 000 burn-in and 2 , 000 identified iterations to fit a 128-component HDPGMM to the six multimer data sets shown in Figure 3 took less than 6 hours on a Linux workstation using a single NVidia GTX 580 card costing under USD 500 . The algorithm has runtime complexity of , and benchmark experiments shown in Figure 9 confirm that the performance is linear in the number of events and samples and quadratic in the number of markers . Open source code for fitting DPGMM and HDPGMM models to flow cytometry data is available from http://code . google . com/p/py-fcm/ . The code is written in the Python programming language , and will run on regular CPUs , but is optimized for massively parallel computing using the CUDA interface ( a suitable Nvidia GPU is required for CUDA ) . Flow cytometry data samples , source code and a sample script to fit a HDPGMM model to the data are provided in Supplementary Materials . In summary , we describe and provide code for a hierarchical modeling extension to statistical mixture models that improves on the robustness , sensitivity and interpretability of model-based approaches for automated flow cytometry analysis . We demonstrate the consistency of frequency of HDPGMM estimates on reference data samples spiked with extremely low frequencies of antigen-specific cells , a scenario directly relevant to many clinical research studies in vaccine development , immune monitoring and immune biomarker discovery where the frequency of rare antigen-specific T cells is of interest .
We give posterior computational details only for HDPGMM since details for our implementation of DPGMM have been previously published [16] . First , let and so that . Furthermore , let and . These along with equations ( 3 ) and ( 4 ) give a complete specification of the model . Metropolis within Gibbs is performed by updating each parameter with a draw from its conditional distribution in turn and when the conditional distribution is intractable , use a Metropolise Hastings update instead . We give the specifics of the sampling in the remainder of this section . The generation of the standard samples with a defined number of antigen-specific CD8 T cells spiked into autologous PBMC for use in HLA-peptide multimer has been described [8] . Briefly , Phytohemagglutinin ( PHA; ) and IL-2 ( 20 U/ml ) stimulated HLA-A*0201 positive PBMC were retrovirally transduced with an HLA-A*0201 restricted specific TCR construct after the CD4 T cells were depleted using Dynabeads ( Invitrogen ) . After 5 days , the transduced cells were harvested and purified using APC-conjugated NY-ESO-1 specific HLA multimer and magnetic cell sorting . Purified cells were clonally expanded , harvested and spiked at the desired percentage of NY-ESO-1 specific TCR expressing CD8 T cells into autologous PBMC . These samples were stained with monoclonal antibodies specific for CD45 ( pan leukocyte ) CD3 ( T-lymphocytes ) and HLA-A*0201 NY-ESO-1 157–165 multimers to identify spiked T cells . For details , please refer to reference [8] ) . Sample preparation conditions were set so that results ( i . e . generated FCS files ) would be as comparable as possible: Cell staining was performed simultaneously by the same operator , using the same batches of staining reagents , and data acquisition was subsequently done in a single experiment using the same cytometer settings ( voltages , compensations ) for all samples . The data were generated using a FACSCalibur and CellQuest Pro 6 . 0 , with values ranging from 0 to 1023 . No further transformations were performed on the data but standardization to have zero mean and unit standard deviation was performed before fitting the mixture model so all markers would have equal contributions . The standardization was reversed before plotting - i . e . all plots are based on the original 0 to 1023 scale . For gating estimates , frequency estimates from 10 flow cytometry operators using the same gating strategy were collected .
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The use of flow cytometry to count antigen-specific T cells is essential for vaccine development , monitoring of immune-based therapies and immune biomarker discovery . Analysis of such data is challenging because antigen-specific cells are often present in frequencies of less than 1 in 1 , 000 peripheral blood mononuclear cells ( PBMC ) . Standard analysis of flow cytometry data relies on visual identification of cell subsets by experts , a process that is subjective and often difficult to reproduce . Consequently , there is intense interest in automated approaches for cell subset identification . One popular class of such automated approaches is the use of statistical mixture models . We propose a hierarchical extension of statistical mixture models that has two advantages over standard mixture models . First , it increases the ability to detect extremely rare event clusters that are present in multiple samples . Second , it enables direct comparison of cell subsets by aligning clusters across multiple samples in a natural way arising from the hierarchical formulation . We demonstrate the algorithm on clinically relevant reference PBMC samples with known frequencies of CD8 T cells engineered to express T cell receptors specific for the cancer-testis antigen ( NY-ESO-1 ) and compare its performance with other popular automated analysis approaches .
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"Discussion",
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"medicine",
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"immunity",
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2013
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Hierarchical Modeling for Rare Event Detection and Cell Subset Alignment across Flow Cytometry Samples
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The cyclic AMP-dependent protein kinase A signaling pathway plays a major role in regulating plant infection by the rice blast fungus Magnaporthe oryzae . Here , we report the identification of two novel genes , MoSOM1 and MoCDTF1 , which were discovered in an insertional mutagenesis screen for non-pathogenic mutants of M . oryzae . MoSOM1 or MoCDTF1 are both necessary for development of spores and appressoria by M . oryzae and play roles in cell wall differentiation , regulating melanin pigmentation and cell surface hydrophobicity during spore formation . MoSom1 strongly interacts with MoStu1 ( Mstu1 ) , an APSES transcription factor protein , and with MoCdtf1 , while also interacting more weakly with the catalytic subunit of protein kinase A ( CpkA ) in yeast two hybrid assays . Furthermore , the expression levels of MoSOM1 and MoCDTF1 were significantly reduced in both Δmac1 and ΔcpkA mutants , consistent with regulation by the cAMP/PKA signaling pathway . MoSom1-GFP and MoCdtf1-GFP fusion proteins localized to the nucleus of fungal cells . Site-directed mutagenesis confirmed that nuclear localization signal sequences in MoSom1 and MoCdtf1 are essential for their sub-cellular localization and biological functions . Transcriptional profiling revealed major changes in gene expression associated with loss of MoSOM1 during infection-related development . We conclude that MoSom1 and MoCdtf1 functions downstream of the cAMP/PKA signaling pathway and are novel transcriptional regulators associated with cellular differentiation during plant infection by the rice blast fungus .
Eukaryotic organisms , including fungi , can sense and respond to extracellular cues via various signaling pathways for regulating a variety of developmental and differential cellular processes . Among these pathways , the conserved cyclic AMP-dependent protein kinase A ( cAMP/PKA ) signaling pathway has been well studied . The secondary messenger cAMP is universally produced through cyclization of ATP catalyzed by adenylate cyclases ( ACs ) , and the level of cellular cAMP is regulated by cAMP phosphodiesterases [1] , [2] . PKA consists of two catalytic subunits and two regulatory subunits . Binding of four cAMP molecules at two sites on each regulatory subunit causes conformational changes in PKA regulatory subunits , releasing activated PKA catalytic subunits which subsequently phosphorylate target proteins , including transcription factors , to control various physiological processes [3]–[6] . The cAMP/PKA response pathway plays an important role in fungal morphogenesis and virulence in plant pathogenic fungi [7] . During the last two decades , the function of several components of the cAMP/PKA pathway , in particular , AC and PKA , has been determined in a number of plant pathogenic fungi , including Colletotrichum trifolii [8] , C . lagenarium [9] , [10] , Fusarium verticillioides [11] , Magnaporthe oryzae [12]–[14] , Sclerotinia sclerotiorum [15] and U . maydis [16] , [17] . In yeasts , several downstream target proteins of PKA have also been identified and functionally characterized . In Saccharomyces cerevisiae for instance , the Flo8 transcription factor is critical for pseudohyphal growth in diploids , haploid invasive growth and flocculation and functions downstream of the cAMP/PKA pathway [18] , [19] . A family of FLO genes , including FLO11 ( also referred as MUC1 ) which encodes a cell surface flocculin critical for both pseudohyphal growth and invasive growth , are regulated or activated by Flo8 [19]–[22] . It has been shown that the binding of Flo8 to the promoter of FLO11 is regulated by Tpk2 ( a catalytic subunit of PKA ) in S . cerevisiae [23] . In both S . cerevisiae and Candida albicans , APSES ( Asm1 , Phd1 , Sok2 , Efg1 , and StuA ) transcription factors are targets for the cAMP/PKA pathway [24]–[27] . C . albicans Flo8 interacts with Efg1 , a homolog of the Phd1/Sok2 and StuA proteins that regulate morphogenesis of S . cerevisiae and Aspergillus nidulans , respectively , and is essential for hyphal development and virulence [28] . In phytopathogenic fungi , several APSES transcription factors , including F . oxysporum FoStuA , Glomerella cingulata GcStuA and M . oryzae MoStu1 ( Mstu1 ) , have recently been identified [29]–[31] . Both GcStuA and MoStu1 are required for appressorium mediated plant infection [30] , [31] , while FoStuA is dispensable for pathogenicity by F . oxysporum [29] . In U . maydis , three transcription factors , Prf1 , Hgl1 and Sql1 , regulated by cAMP pathway have also been identified [32]–[34] . However , the downstream targets of the cAMP/PKA pathway still remain largely unknown in phytopathogenic fungi . Magnaporthe oryzae is the causal agent of rice blast , the most destructive disease of rice worldwide [35] , [36] . In the last two decades , M . oryzae has arisen as a model fungal pathogen for understanding the molecular basis of plant-fungus interactions [36]–[39] . It is now clear that infection-related morphogenesis is controlled by the cAMP response pathway and activation of the mitogen-activated protein kinase ( MAPK ) cascade in M . oryzae [12] , [40]–[42] . Appressorium formation of M . oryzae requires the cAMP-response pathway , which responds to inductive signals from the rice leaf , including surface hydrophobicity and wax monomers from the plant [12]–[14] , [43]–[45] . Deletion of the M . oryzae MAC1 gene encoding adenylate cyclase resulted in mutants that cannot form appressoria and were defective in the growth of aerial hyphae and conidiation [12] . However , these defects in Δmac1 mutants could be complemented by adding exogenous cAMP or by spontaneous mutations in the regulatory subunit of PKA gene SUM1 [44] . Consistent with this , M . oryzae CPKA , which encodes the catalytic subunits of PKA , is dispensable for appressorium formation , but is required for appressorial penetration [13] , [14] . Additionally , the role of the M . oryzae Pmk1 MAPK pathway in regulating appressorium development has been clearly established [40] , [42] , [46]–[49] . Therefore , the cAMP/PKA pathway and Pmk1 MAPK cascade are essential for regulation of appressorium development and pathogenicity in the rice blast fungus . In M . oryzae , the upstream activation of adenylate cyclase appears to be mediated by G-proteins in response to physical and chemical properties of the rice leaf surface . The M . oryzae genome contains three Gα ( MagA , MagB , and MagC ) , one Gβ ( Mgb1 ) , and one Gγ ( Mgg1 ) subunits . For the three Gα subunits , only disruption of MAGB can significantly reduce vegetative growth , conidiation , appressorium formation , and pathogenicity , although the ΔmagC mutants are also reduced in conidiation [50] . MagB may respond to surface cues to stimulate Mac1 activity and cAMP synthesis , because expression of a dominant active allele of MAGB causes appressoria to form on hydophilic hard surfaces [51] . Rgs1 , a regulator of G-protein signaling , interacts with all the three Gα subunits and functions as a negative regulator of G-proteins in M . oryzae [52] . Additionally , both MGB1 and MGG1 are essential for appressorium formation and plant infection [53] , [54] . M . oryzae PTH11 which encodes a putative G-protein-coupled receptor may be involved in regulating Mac1 activities , because PTH11 is required for surface recognition and virulence and exogenous cAMP restores appressorium formation and pathogenicity in PTH11 deletion mutants [55] . Recently , we reported that MoRic8 interacts with MagB and acts upstream of the cAMP/PKA pathway to regulate multiple stages of infection-related morphogenesis in M . oryzae [56] . However , downstream targets of the cAMP/PKA pathway are not well studied in M . oryzae . Here , we present the identification and functional characterization of two novel pathogenicity-related genes identified by insertional mutagenesis , MoSOM1 and MoCDTF1 , which are required for morphogenesis and virulence . Our results have provided evidence that MoSOM1 and MoCDTF1 are regulated by the cAMP/PKA pathway . Deletion of either MoSOM1 or MoCDTF1 resulted in defects in hyphal growth , sporulation , appressorium formation and virulence . MoSom1 strongly interacted with the transcription factors , MoCdtf1 and MoStu1 , and also weakly interacted with CpkA in yeast two hybrid assays performed in the presence of cAMP . Moreover , MoSOM1 can complement the defects of S . cerevisiae flo8 in haploid invasive growth and diploid pseudohyphal development . When considered together , these data suggest that MoSom1 is an important regulator of infection-related development in M . oryzae which interacts with the transcription factors , MoCdtf1 and MoStu1 , and acts downstream of the cAMP/PKA signaling pathway .
To investigate the molecular basis of plant infection by M . oryzae , a large T-DNA insertional mutagenesis library ( ∼20 , 000 transformants ) was constructed . All of the transformants were first screened for impairment in pathogenesis by inoculating barley leaves ( cv . Golden Promise ) with conidia or hyphae ( if conidia were not available ) using a barley cut-leaf assay . The mutants obtained from the first round screening were subsequently verified by inoculating rice leaves . Among them , YX-145 , YX-1303 and YX-864 ( Figure 1A; Table S1 ) were identified as mutants , which were incapable of causing disease on barley or rice leaves ( CO-39 ) following inoculation with hyphae ( Figure 1B ) . To identify the T-DNA integration sites in the mutants , genomic DNA flanking the integrated T-DNAs was obtained from the third round PCR products ( Figure S1 ) and sequenced , respectively . By amplifying the genomic DNAs flanking the left border of the integrated T-DNA , the patterns of T-DNA integrated into these mutants were determined ( Figure 1C ) . The T-DNA insertion in YX-145 was found at position 593835+ , which is 2457 bp downstream of the translational start site , in the seventh exon of a hypothetical gene MGG_04708 ( GenBank XP_362263 ) located on supercontig 16 of chromosome IV . We named the T-DNA tagged gene MoSOM1 , because it putatively encodes a predicted protein which is homologous with Som1 proteins , which may be involved in the cAMP-dependent protein kinase pathway controlling growth polarity in related fungal species . MoSom1 showed 47 . 54 , 36 . 66 , 36 . 84 , 37 . 29 , 51 . 47 and 47 . 96% amino acid identity with Neurospora crassa Som1 ( AAF75278 ) , Aspergillus nidulans OefA ( AAW55626 ) , A . niger Som1 ( XP_001395127 ) , A . fumigatus Som1 ( XP_746706 ) , Metarhizium acridum Som1 ( EFY91592 ) and Verticillium albo-atrum Som1 ( XP_003006356 ) , respectively . However , MoSom1 showed only 14 . 76% and 14 . 93% amino acid identity with Saccharomyces cerevisiae Flo8 ( DAA07769 ) and Candida albicans Flo8 ( AAQ03244 ) , respectively . Phylogenetic analysis of the putative homologs of MoSom1 was shown in Figure S2A . The T-DNA integration site in YX-1303 was at position 1126131- , which is 544 bp downstream of the translational start site , in the first exon of a hypothetical gene MGG_11346 ( GenBank XP_001413674 ) located on supercontig 27 of chromosome I . The T-DNA tagged gene putatively encodes a protein with no known function . We named the gene MoCDTF1 ( for Magnaporthe oryzae cAMP-dependent transcription factor gene ) . MoCdtf1 showed 21 . 44 , 24 . 23 , 18 . 73 and 27 . 37% amino acid identity with N . crassa NCU00124 ( XP_957248 ) , Sclerotinia sclerotiorum SS1G_07310 ( XP_001591864 ) , A . nidulans AN4210 ( XP_661814 ) and Gibberella zeae FG06653 ( XP_386829 ) . However , no homolog of MoCdtf1 exists in the genomes of the yeasts Saccharomyces cerevisiae and C . albicans . Phylogenetic analysis of the putative homologs of MoCdtf1 was shown in Figure S2B . In the YX-864 mutant , MoMSB2 ( MGG_06033 ) was disrupted by T-DNA integration ( Figure 1C ) . To verify the non-pathogenic phenotype of YX-864 , we performed a targeted gene deletion of MoMSB2 ( Figure S3A ) . The resulting Δmomsb2 null mutants , MK9 and MK12 ( Table S1 ) , were selected by Southern blot analysis ( Figure S3B ) ; and were also confirmed by the lack of MoMSB2 transcript using RT-PCR amplification with 864Q-F and 864Q-R ( Table S2 ) . Deletion of MoMSB2 had no obvious effect on vegetative growth , conidial germination and sexual development , but caused defects in conidiation , appressorium formation and virulence ( Figure S4 ) . The defect in appressorium formation could not be restored by adding exogenous 1 , 16-hexadecanediol ( Diol ) , cyclic adenosine 3′ , 5′-cyclophosphate ( cAMP ) , and 3-iso-butyl-1-methylxanthine ( IBMX ) . In S . cerevisae , it has been shown that Msb2 interacts with Sho1 and Cdc42 to promote their function in the filamentous growth pathway [57] . However , no direct interactions between MoMsb2 and MoSho1 ( MGG_09125 ) and MoCdc42 ( MGG_00466 ) were detected in yeast two hybrid assays ( data not shown ) . Taken together , our data provide evidence that MoMSB2 is required for plant infection-related morphogenesis and virulence in M . oryzae , which is consistent with a very recent study in which the gene was independently identified [49] . To determine the role of MoSOM1 in plant infection and confirm the predicted role based on phenotypic analysis of YX-145 , we performed targeted gene deletion of MoSOM1 using the gene replacement vectors pMoSOM1-KO ( Figure S3C ) . The gene replacement was analyzed by PCR amplification with primers 145-F and 145-R ( Table S2 ) from transformants . The resulting Δmosom1 null mutants , SK5 , SK21 and SK27 ( Table S1 ) , were selected based on Southern blot analysis ( Figure S3D ) and also confirmed by RT-PCR amplification using primers 145Q-F and 145Q-R . One of the transformants resulting from ectopically integrated pMoSOM1-KO , ES16 , was used as a control strain . To complement the mutant , the 2 . 8 kb MoSOM1 gene-coding sequence and a 1 . 5 kb promoter region was re-introduced into SK27 ( Δmosom1 ) to obtain two complemented strains , SC1 and SC3 ( Table S1 ) . Similarly , the Δmocdtf1 null mutants , CTK2 and CTK15 , were generated by a targeted gene deletion of MoCDTF1 ( Figure S3E and F ) . The complemented strains , CTC1 and CTC5 , were obtained by transforming the genomic DNA including 4 . 1 kb MoCDTF1 gene-coding sequence and a 1 . 6 kb promoter region back to Δmocdtf1 ( CTK15 ) . We then harvested the mycelium of Δmosom1 and Δmocdtf1 mutants from liquid CM cultures to inoculate susceptible barley and rice using the cut leaf assay . Our results showed that the wild-type strain Guy11 , ectopic ( ES16 ) or complementation ( SC1 and CTC1 ) transformants caused typical rice blast lesions on both intact and abraded barley or rice leaves ( Figure 2A ) . However , consistent with the original analysis of YX-145 , the Δmosom1 ( SK27 ) mutant was non-pathogenic on both susceptible barley and rice leaves , even when they were abraded to remove the surface cuticle ( Figure 2A ) . The Δmocdtf1 ( CTK15 ) mutant was non-pathogenic on both barley and rice leaves , but was still able to cause some disease symptoms when leaf surfaces were abraded ( Figure 2A ) . We were unable to carry out a pathogenicity assay using spray inoculation , because these mutants were completely defective in sporulation in culture ( see below ) . Furthermore , the Δmosom1 ( SK27 ) mutant was non-pathogenic when inoculated onto rice roots , but the Δmocdtf1 ( CTK15 ) mutant was still able to cause some disease symptom ( Figure 2B ) . These results therefore demonstrated that the non-pathogenic phenotype of YX-145 and YX-1303 mutants was caused by T-DNA integration and that both MoSOM1 and MoCDTF1 are crucial for plant infection in M . oryzae . Deletion of MoSOM1 caused significant defects in hyphal growth and colony pigmentation ( Figure 3A ) . The Δmosom1 mutant formed colonies that were less pigmented and which formed less aerial hyphae ( Figure 3A ) . All Δmosom1 mutants ( SK5 , SK21 and SK27 ) showed the same phenotypes and only data for mutant SK27 are therefore presented here . When the Δmosom1 mutant ( SK27 ) was grown in CM liquid culture , it formed very small compact mycelium masses , in contrast to the bigger but less compact mycelium formed by the wild-type strain ( Figure 3A ) . The growth rate of mycelium from each strain was determined ( Figure 3B ) . The Δmosom1 mutant and YX-145 were significantly reduced in vegetative growth , forming colonies with diameters of 3 . 6±0 . 09 cm and 3 . 7±0 . 08 cm after 10-day incubation on CM at 25°C , respectively , compared with 6 . 8±0 . 1 cm colony diameter of wild-type strain Guy11 ( P<0 . 01 ) ( Figure 3B ) . We also carried out mycelial dry weight assays . The results showed that the Δmosom1 mutant was significantly reduced in mycelial dry weight with 0 . 151±0 . 007 g compared with 0 . 330±0 . 015 g of the wild-type strain Guy11 ( P<0 . 01 ) after 2-day incubation in liquid CM at 25°C . Deletion of MoCDTF1 also caused defects in vegetative growth and colony pigmentation on CM plate cultures compared with the wild-type strain , although the affected degree was not as severe as in Δmosom1 mutants ( Figure 3A ) . The Δmocdtf1 mutant ( CTK15 ) formed mycelium that was not well pigmented compared with the wild-type strain and formed smaller mycelium masses in liquid culture ( Figure 3A ) . The Δmocdtf1 mutant and YX-1303 were reduced in vegetative growth , forming colonies with diameters of 5 . 0±0 . 08 cm and 5 . 1±0 . 1 cm after 10-day incubation on CM at 25°C , respectively , compared with 6 . 8±0 . 1 cm colony diameter of wild-type strain Guy11 ( P<0 . 01 ) ( Figure 3B ) . The other Δmocdtf1 mutant ( CTK2 ) had the same phenotypes as CTK15 ( data not shown ) . To further investigate the roles of MoSOM1 and MoCDTF1 , two Δmosom1Δmocdtf1 mutants D-3 and D-9 were created by transformation of pMoSOM1-DK ( Figure S3G ) into the strain CTK15 ( Δmocdtf1 ) and selected by PCR and confirmed by RT-PCR with the primers 145-F and 145-R ( Figure S3H and I ) , respectively . The Δmosom1Δmocdtf1 mutant D-3 grew more slowly than both the Δmosom1 ( SK27 ) and Δmocdtf1 mutants ( CTK15 ) in culture ( Figure 3A and B ) . Additionally , when the Δmosom1 mutant ( SK27 ) and Δmocdtf1 mutant ( CTK15 ) were inoculated on various media , including MM , PDA and OMA , their vegetative growth and colony pigmented were also impaired ( Figure S5 ) . We conclude that MoSOM1 and MoCDTF1 are required for vegetative growth and mycelium pigmentation . The ability to form spores was evaluated by carefully washing the surface of 10-day-old cultures on CM plates . YX-145 , SK27 , YX-1303 and CTK15 were unable to form conidia , while the wild-type strain Guy11 produced numerous conidia with 21 . 0±2 . 0×106 spores per plate ( Figure 4A ) . When these mutants were grown on different growth media , including MM , PDA , OMA , sporulation was also not observed . These results showed that asexual sporulation was completely blocked by the deletion/disruption of either MoSOM1 or MoCDTF1 , indicating that each of the two genes is essential for conidiation in M . oryzae . Furthermore , no conidiophores were observed from the cultures of the mutants , while Guy11 formed normal conidiophores and conidia ( Figure 4B ) . The phenotypes were also observed from other targeted gene replacement mutants , such as SK5 , SK21 and CTK2 . These results suggest that the defect in conidiation of the Δmosom1 and Δmocdtf1 mutants may be caused by the lack of aerial conidiophore development . To determine the role of MoSOM1 and MoCDTF1 in sexual reproduction , the wild type Guy11 ( MAT1-2 ) , SK27 and CTK15 were crossed with a standard tester strain TH3 ( MAT1-1 ) of M . oryzae to allow perithecium production . After three weeks , the junctions between mated individuals were examined for the presence of perithecia . We observed numerous perithecia at the junctions of the wild type strains Guy11 and TH3 , but no perithecia were formed after crossing SK27 with TH3 or CTK15 with TH3 ( Figure 4C ) , even when the incubation time was extended to six weeks . Similarly , crossing of TH3 with the T-DNA insertional mutants ( YX-145 and YX-1303 ) , SK5 , SK21 and CTK2 did not produce any perithecia , indicating that MoSOM1 or MoCDTF1 are essential for fertility and development of fruiting bodies by M . oryzae . The Δmosom1Δmocdtf1 mutant D-3 was also unable to produce conidiophores , conidia and was completely impaired in sexually development ( Figure 4A–C ) . We conclude that MoSOM1 and MoCDTF1 are both essential for production of asexual and sexual spores by M . oryzae . Since the Δmosom1 and Δmocdtf1 mutants were unable to produce spores , we harvested mycelium of the mutants from liquid CM culture and appressorium formation was investigated by placing hyphae on hydrophobic surfaces . Numerous appressoria were formed from mycelium of the isogenic wild type strain Guy11 , but no appressoria were observed at 24 h or even 48 h post inoculation with the Δmosom1 ( SK27 ) and Δmocdtf1 ( CTK15 ) mutants ( Figure 4D ) . When mycelium of these mutants was placed on barley or rice leaf surfaces , no appressorium formation was induced and no penetration events were observed at 24 h post inoculation ( data not shown ) , indicating the non-pathogenic phenotypes of Δmosom1 and Δmocdtf1 mutants on host leaves may be caused by the defect in appressorium formation . The Δmosom1Δmocdtf1 was also unable to form appressoria from mycelium ( Figure 4D ) . These results suggest that MoSOM1 and MoCDTF1 are both required for appressorium formation and plant infection by M . oryzae . An easily wettable phenotype can be observed when a fungal culture becomes easily water-logged , due to a loss of surface hydrophobicity , brought about by the absence of the rodlet layer associated with aerial hyphae and conidiospores [58] . We observed that colonies of YX-145 and Δmosom1 mutants were distinct from the wild-type strain Guy11 and formed less aerial hyphae . YX-1303 and Δmocdtf1 mutants were also reduced in aerial hypha formation . We therefore tested the surface hydrophobicity of these strains ( Figure 5A ) . Drops of water and 0 . 2% gelatin remained on the surface of mycelium of Guy11 and older mycelium of the Δmocdtf1 mutant ( CTK15 ) after 24–48 h incubation , and drops of detergent solution remained suspended on the surface of colonies of Guy11 for about 10–30 min before soaking into the mycelium . By contrast , drops of water and detergent solution immediately soaked into the cultures of the Δmosom1 mutant ( SK27 ) and young mycelium of CTK15 ( Figure 5A ) . Similar results were observed for the other Δmosom1 and Δmocdtf1 mutants . The surface hydrophobicity of the double knockout mutants D-3 and D-9 was similar to the Δmosom1 mutants . The results indicate that deletion of either MoSOM1 or MoCDTF1 affects cell surface hydrophobicity in M . oryzae . As a consequence of the wettable phenotype of the mutants , we reasoned that M . oryzae hydrophobin genes might be down-regulated in the mutants . To test this idea , we investigated the expression of M . oryzae hydrophobin-encoding genes , including MPG1 and MHP1 and two MHP1 homologs ( MGG_09134 and MGG_10105 ) , by quantitative RT-PCR ( qRT-PCR ) . We found that expression of hydrophobin encoding genes was significantly ( P<0 . 01 ) down-regulated in both Δmosom1 and Δmocdtf1 mutants , particularly in the Δmosom1 mutant ( Figure 5B ) . To investigate the expression pattern of MoSOM1 during infection-related development , a 1 . 52 kb promoter fragment upstream of the gene and the entire MoSom1 protein-coding sequence were fused in-frame to the green fluorescent protein ( GFP ) -encoding gene , GFP ( sGFP ) , and introduced into the Δmosom1 mutant SK27 . Transformants carrying a single integration of the pMoSOM1-GFP were selected by DNA gel blot analysis . An independent single plasmid insertion event occurred in the transformants , SC1 and SC3 ( Table S1 ) . Punctate green fluorescence was observed in the two transformants . SC3 was used to investigate the spatial localization of the MoSom1 protein in detail . In this analysis , GFP fluorescence was observed both in mycelium and in conidia of SC3 , and each cell contained one fluorescence punctum ( Figure 6A ) , suggesting that MoSom1 may localize to the nucleus of each cell . To test this idea , mycelium and conidia of SC3 were stained with 4′-6-Diamidino-2-phenylinodle ( DAPI ) to stain nuclei specifically . The merged image of GFP and DAPI staining showed that MoSom1-GFP localizes to the nucleus and that each cell contains a single nucleus ( Figure 6A ) . To observe MoSOM1 expression and nuclear division patterns during appressorium development in M . oryzae , conidia of the strain SC3 were allowed to germinate on hydrophobic GelBond film surfaces . During conidium germination , the nucleus in the germinating cell entered mitosis and then one of the daughter nuclei migrated to the incipient appressorium ( Figure 6B ) . Three nuclei that remained in the conidium degenerated and could no longer be seen after approximately 18 hours post inoculation , consistent with previous observations of nuclear division in M . oryzae [59] . Bright green fluorescence of the strain SC3 during penetration on onion epidermis was also observed , as shown in Figure 6C . However , qRT-PCR analysis showed that the expression levels of MoSOM1 were similar at different developmental stages ( data not shown ) , indicating expression throughout the life cycle of the fungus . The expression and localization of MoSom1-GFP was identical in the other transformant SC1 . A similar strategy was used to investigate the expression pattern of MoCDTF1 and localization of the encoded protein during infection-related development . Green fluorescence was also observed in nuclei , both in mycelium and in conidia of the transformants CTC1 ( Figure S6 ) and CTC5 . However , weak GFP fluorescence was observed in mycelium and in conidia of CTC1 and CTC5 compared strong GFP fluorescence observed in SC1 and SC3 . These results provide evidence that both MoSom1 and MoCdtf1 proteins are localized to the nucleus in M . oryzae . To ensure that all phenotypes observed in the Δmosom1 and Δmocdtf1 mutants were associated with the gene replacement event , we carried out phenotypic analysis of complemented transformants SC1 , SC3 , CTC1 and CTC5 . The GFP-expressing transformants SC1 and CTC1 exhibited full virulence to barley and rice by cut-leaf assay using mycelium inoculations ( Figure 2A ) or by seedling assays with conidial spray-inoculation . The other phenotypes of Δmosom1 and Δmocdtf1 mutants , including vegetative growth , conidiation and appressorium formation , were all fully complemented by re-introduction of the genes of MoSOM1 or MoCDTF1 ( Figure 3B; Figure 4A , B and D; Figure S7 ) . However , the mutants were not responsive to 10 mM exogenous cAMP ( data not shown ) , indicating MoSom1 and MoCdtf1 may act downstream of the cAMP/PKA pathway . We conclude that MoSOM1 or MoCDTF1 are both essential for multiple steps of plant infection-related morphogenesis development and pathogenicity in M . oryzae . To confirm the position and size of the introns of MoSOM1 and MoCDTF1 , cDNA clones of the coding sequence were obtained by reverse transcription-PCR with primer pairs of SOM-E-F/SOM-Xh-R and P1303-F/1303H-Kpn-R ( Table S2 ) and the resulting PCR products cloned into pGEM-T easy vectors and sequenced , respectively . Comparison of the cDNA and sequenced genomic DNA confirmed that MoCDTF1 has an open reading frame of 4 , 121 bp interrupted by one intron ( 62 bp ) and putatively encodes a 1352 aa protein , which is identical to the protein sequence predicted by automated annotation of the M . oryzae genome sequence ( ID: MGG_11346 . 6; Broad Institute ) . MoSOM1 has an open reading frame of 2 , 789 bp interrupted by seven introns ( 56 bp , 85 bp , 62 bp , 24 bp , 66 bp , 81 bp and 68 bp , respectively ) and putatively encodes a 781 aa protein ( ID: MGG_04708 . 6; Broad Institute ) . However , five splice variants of MoSom1 were also found , as shown in Figure S8 . Furthermore , all of the alternatively spliced isoforms of MoSom1 could be detected in RNA extracted from mycelium cultured in liquid CM ( 1 d , 3 d and 5 d ) or conidia from 10-day-old CM plates ( data not shown ) . Three missed amino-acid fragments occurred in exons 4 , 6 and 7 , respectively , while the extra amino-acid fragment was in exon 4 . These data suggested that there may be various forms of post-transcriptional modification of MoSOM1 in M . oryzae . Both S . cerevisiae Flo8 and C . albicans Flo8 contain a LUFS ( LUG/LUH , Flo8 , Single-stranded DNA binding protein ) domain and there is a LisH ( Lissencephaly type 1-like homology ) motif within the domain . Similarly , a LUFS domain harbored a LisH motif was also found at the N-terminal portion of the M . oryzae MoSom1 protein ( Figure S9A ) . The amino acid alignment of LisH domains of MoSom1 homologs from related fungal species were shown in Figure 7A , indicating that the fungal LisH domain in fungi is conserved . In addition , MoCdtf1 has a C-terminal ZnF_C2H2 domain . The amino acid alignment of the putative zinc finger , ZnF_C2H2 domain in MoCdtf1 was shown in Figure S9B . The position of the LisH domain of MoSom1 was shown in Figure 7B . To explore the role of the LisH domain of the MoSom1 protein , we generated a mutant allele of MoSOM1-GFP by deletion of the LisH domain . The resulting transformants ( SL1 and SL7 ) expressing MoSOM1ΔLISH-GFP produced more aerial hyphae and formed more melanized colonies than the original Δmosom1 mutant , but they were still defective in conidiation , asexual/sexual development and pathogenicity ( Figure 7C ) . In these strains , the GFP fluorescence was observed both in nucleus and cytoplasm of hypha ( Figure 7C ) , indicating that protein localization was somewhat affected by the deletion of LisH domain of MoSOM1 . Additionally , mutants carrying deletions in the ZnF_C2H2 domain of MoCdtf1 had the same phenotypes as the original strain CTK15 ( data not shown ) , indicating that the domain is essential for the function of MoCdtf1 in M . oryzae . These results indicated that both the LisH domain of MoSom1 and the ZnF_C2H2 domain of MoCdtf1 are essential for infection related morphorgenesis and virulence in M . oryzae . Consistent with their observed localization patterns ( Figure 6; Figure S6 ) , both M . oryzae MoSom1 and MoCdtf1 were predicted to be nuclear localized proteins . The positions of two predicted nuclear localization signals ( NLSs ) were shown in Figure 7B . To determine the role of the predicted NLSs of MoSom1 , we generated mutant alleles of MoSOM1-GFP deleted of each individual putative NLS ( PKKK or PSKRVRL ) and transformed them into the Δmosom1 mutant ( SK27 ) . We found that transformants ( SN1-2 and SN1-5 ) expressing the MoSOM1ΔPKKK -GFP grew normally on CM medium , produced numerous conidia and were fully pathogenic . Moreover , green fluorescence was still observed in the nucleus of these transformants ( Figure 7C ) . However , like the original Δmosom1 mutant , strains ( SN2-3 and SN2-4 ) expressing the MoSOM1ΔPSKRVRL-GFP were unable to produce asexual/sexual spores and were non-pathogenic . Interestingly , we observed green fluorescence of these strains in the cytoplasm of hypha ( Figure 7C ) . These results suggest that PSKRVRL but not PKKK sequence is essential for the function and transportation of MoSom1 protein from cytoplasm to the nucleus . Using a similar strategy , we also demonstrated that the predicted NLS ( PPKRKKP ) of MoCdtf1 was crucial for the protein localized to the nucleus and its functions during differentiation and plant infection ( Figure 7C ) . In Saccharomyces cerevisae , Flo8 is critical for invasive growth and flocculation in haploids and pseudohyphal growth in diploids [18] . To determine if MoSOM1 can functionally complement the S . cerevisae flo8 defects , we carried out yeast complementation assays . Our results showed that a yeast strain expressing MoSOM1 in the haploid flo8 mutant HLY850 was restored in its ability to carry out invasive growth on YPD medium ( Figure 8A ) . Consistently , the strain expressing MoSOM1 in the dipoliod flo8 mutant HLY852 recovered the ability to carry out pseudohyphal development on SLAD ( synthetic low ammonium dextrose medium ) ( Figure 8B ) . These data suggest that MoSOM1 can functionally complement yeast flo8 defects in both haploid invasive growth and diploid pseudohyphal development . To understand the regulation of MoSOM1 and MoCDTF1 by the cAMP/PKA pathway , the expression of both MoSOM1 and MoCDTF1 was determined by qRT-PCR in Δmac1 , ΔcpkA , ΔmagA , ΔmagB and Δrgs1 mutants ( Table S1 ) . For comparison , other signaling mutants impaired in infection-related morphogenesis , such as Δpmk1 and Δmps1 , were also used . Interestingly , we found that expression levels of MoSOM1 and MoCDTF1 were significantly reduced in Δmac1 , ΔcpkA and ΔmagA mutants ( P<0 . 01 ) , but not in other mutants ( Figure S10 ) . However , qRT-PCR analysis showed that expression of MoCDTF1 was not significantly regulated by the deletion of MoSOM1 or vice versa ( data not shown ) . These results indicate that expression of MoSOM1 and MoCDTF1 are down-regulated by impairment of the cAMP/PKA signaling pathway . To understand whether over-expression of the MoSOM1 can restore the phenotypes of the Δmac1 or ΔcpkA mutants , we developed two strains ( OM1 and OM4 ) expressing MoSOM1-GFP driven by the TrpC promoter from A . nidulans in the Δmac1 mutant , and similarly constructed two strains ( OC2 and OC7 ) in the ΔcpkA mutant . Strong fluorescence was observed at the nucleus of these strains ( Figure S11A ) . However , the phenotypes of the Δmac1 or ΔcpkA mutants , including appressorium formation and pathogenicity ( Figure S11B and C ) , were not restored by over-expression of MoSOM1 . Interestingly , treatment of SC3 with the adenylate cyclase inhibitor MDL-12 , 330A hydrochloride at high concentrations , led to some accumulation of MoSom1-GFP in the cytoplasm ( Figure S12 ) . These results provide evidence that phosphorylation of MoSom1 by activated CpkA may be important for its nuclear localization . Our results showed that the phenotypes of Δmosom1 and Δmocdtf1 mutants are somewhat similar and that expression of both MoSOM1 and MoCDTF1 are regulated by the cAMP/PKA signaling pathway . In a previous report , MoStu1 ( MGG_00692 ) , an APSES protein of M . oryzae , was shown to be required for pathogenicity and sporulation [31] . To determine whether MoSom1 interacts with the two transcription factors , MoCdtf1 and MoStu1 , we carried out yeast two hybrid ( Y2H ) experiments . The results provided evidence that MoSom1 physically interacts with MoCdtf1 and MoStu1 ( Figure 9A ) , suggesting both MoCdtf1 and MoStu1 were regulated by a direct interaction with MoSom1 . However , we did not observe interactions between MoSom1 and other tested proteins , including CpkA and MoLdb1 , under these experimental conditions . Additionally , interactions between MoCdtf1 and MoStu1 or MoLdb1 were also not observed in Y2H . Previously , an interaction between C . albicans Flo8 and Tpk2 was observed in a modified Y2H system [23] . To examine the interaction between MoSom1 and CpkA , we added 5 mM exogenous cAMP into yeast growing medium . Interestingly , a weak interaction between MoSom1 and CpkA was detected by addition of 5 mM exogenous cAMP which may potentially reduce the binding of PKA catalytic subunits with regulatory subunits , while no interaction was detected between the two proteins without adding exogenous cAMP ( Figure 9B ) , presumably because the CpkA is inactive and tightly bound to the endogenous PKA regulatory subunit . These results further demonstrate that MoSom1 may act downstream of the cAMP/PKA pathway in M . oryzae . To identify genes that are putatively regulated by MoSOM1 , we generated serial analysis of gene expression ( SAGE ) libraries for the wild-type strain ( Guy11 , 3728956 tags ) and the Δmosom1 mutant SK27 ( 3449284 tags ) using mycelium grown in liquid CM medium . To confirm gene expression patterns derived from the SAGE libraries , 10 down-regulated genes in the Δmosom1 mutant were randomly selected and validated by qRT-PCR . The results showed that each gene expression pattern was consistent with that in the SAGE data ( Figure 10A ) . To identify genes that were subjected to regulation by MoSom1 , we compared the gene expression profiles between the wild-type strain and the MoSom1 mutant . In total , 719 genes were up-regulated with log2 ratio ( Δmosom1/Guy11 ) >2 and 439 genes were down-regulated with log2 ratio ( Δmosom1/Guy11 ) <−2 ( Figure 10B ) . Genes regulated by deletion of MoSOM1 with log2 Ratio ( Δmosom1/Guy11 ) >1 . 5 or <−1 . 5 were shown in Table S3 . By analysis of the SAGE data , we found that several pathogenicity-related genes ( MPG1 , MoVPR1 , MoAAT1 , MSP1 , MoSSADH , MoACT , and COS1 ) were significantly down-regulated , whereas some ( MoRIC8 , MAC1 , CPKA , MgRAC1 , BUF1 and TPS1 ) were up-regulated ( Table 1 ) . The expression patterns of these genes by SAGE were consistent with those by qRT-PCR analysis ( Table 1 ) . Interestingly , most genes involved in the cAMP/PKA pathway , including MAC1 and CPKA , were significantly up-regulated by deleting MoSOM1 ( Table 1 ) , suggesting that MoSom1 is a negative regulator of their transcription . Recently , we have described two pathogenicity-related genes , MoRIC8 and MoLDB1 [56] , [60] . MoRic8 interacts with Gα subunit MagB and acts upstream of the cAMP/PKA pathway to regulate infection-related morphogenesis . MoLdb1 is a morphogenetic regulator and the Δmoldb1 mutants are similar phenotypes to the Δmosom1 mutants . Therefore , we also generated SAGE libraries from the Δmoric8 mutant Q-10 ( 3636867 tags ) and the Δmoldb1 mutant AK58 ( 3615472 tags ) . Sixty most up- or down-regulated genes in the SAGE library of Δmosom1 , which were also detected in the SAGE libraries of the Δmoric8 and Δmoldb1 mutants , were presented in Table S4 . As expected , the profile of gene expression in the Δmoric8 mutant was very consistent with that in the Δmosom1 mutant SK27 , because both MoRic8 and MoSom1 proteins appear to be involved in the cAMP/PKA signaling pathway . Interestingly , the gene expression profiling of the Δmoldb1 mutant was also consistent with that in Δmoric8 or Δmosom1 mutants , although there were interesting differences such as the expression of CPKA , TPS1 and MoACT , as shown in Table 1 . These data suggest that there may be a potential link between MoSom1 and MoLdb1 in regulating infection-associated gene expression in M . oryzae .
In this study we identified three T-DNA insertional mutants , YX-145 , YX-1303 and YX-864 , which were defective in multiple steps of plant infection and morphogenesis by the rice blast fungus Magnaporthe oryzae . HiTAIL-PCR analysis revealed the integrated T-DNA in the mutants disrupted genomic regions corresponding to genes of MoSOM1 , MoCDTF1 and MoMSB2 , respectively . Targeted deletion of MoSOM1 or MoCDTF1 caused severe defects in both fungal morphogenesis and virulence , which were consistent with the corresponding T-DNA insertional mutants ( Figure 2–4 ) . To our knowledge , both MoSOM1 and MoCDTF1 genes have not been functionally characterized previously in phytopathogenic fungi . In addition , our results also showed that MoMSB2 was required for plant infection-related morphogenesis and virulence in M . oryzae , which is consistent with a very recent study in which the gene was independently identified [49] . However , we also observed that deletion of MoMSB2 resulted in a significant reduction in conidiation ( Figure S4A ) , which was distinct from the previous report . MoSom1 and MoCdtf1 are key morphogenetic regulators . Like most fungal pathogens , asexual reproduction and infection-related development play key roles in the disease cycle in M . oryzae [39] . Molecular genetic analysis of conidiation reveals several conidiation-associated genes that have distinct effects on control of conidiation and conidial morphology . The con7 mutant , for instance , produces a mixture of normal and aberrantly shaped conidia unable to form appressorium , and is non-pathogenic [61] . However , very few mutants have been identified that have completely lost the ability to form conidia in M . oryzae . The MoHOX2 gene encodes a putative homeobox transcription factor . Deletion mutants of MoHOX2 completely abolished asexual sporulation , but the mutants were still pathogenic through hypha-driven appressoria [62] , [63] . Recently , we have reported that MoLDB1 gene encoding a protein with a putative LIM binding domain is necessary for fungal morphogenesis [60] . Deletion mutants of MoLDB1 completely lost the ability to differentiate spores , including meiotically generated ascospores , and were non-pathogenic . The mutants were also unable to differentiate conidiophores or appressoria from mycelium [60] . One of the most interesting findings we report here is that deletion either MoSOM1 or MoCDTF1 completely blocked asexual/sexual sporulation and appressorium development from mycelium and the mutants were non-pathogenic . Interestingly , similar to MoLDB1 , both MoSOM1 and MoCDTF1 are also required for efficient hyphal growth , melanization and hydrophobicity . Furthermore , we did not observe conidiophores in the mutants , indicating that the defect in conidiation of the mutants is associated with lack of conidiophore formation rather than subsequent conidiogenesis . M . oryzae MoSom1 is homologous with Aspergillus nidulans OefA and the hypothetical proteins from other related fungal species . Among these proteins in filamentous fungi , only A . nidulans OefA has been investigated [64] . OEFA has been identified by gene silencing and over-expression approaches and targeted deletion of OEFA causes a “fluffy” growth phenotype due to its development of undifferentiated aerial hyphae [64] . However , the detailed role of OefA in signaling pathways has not been characterized . In yeasts , previous studies have shown that Saccharomyces cerevisiae Flo8 is critical for filamentous growth and functions downstream of the cAMP-PKA pathway [18] , [19] , [23] . Similarly , Candida albicans Flo8 is also essential for hyphal development and virulence and functions downstream of the cAMP-PKA pathway [28] . Since MoSom1 showed only 14 . 76% and 14 . 93% amino acid identity with S . cerevisiae Flo8 and C . albicans Flo8 , respectively , this makes it difficult to find orthologs of Flo8 from the genomes of filamentous fungi by BLAST search . As a consequence of this , a recent report mentioned that the M . oryzae genome , including many other filamentous ascomycetes , may lack distinct orthologs of Flo8 [65] . However , we have shown in this report that MoSom1 functions downstream of the cAMP/PKA pathway , in a similar manner to yeast Flo8 . Several lines of evidence support such a view . First , MoSOM1 can complement a S . cerevisiae flo8 mutant in its ability to carry out haploid invasive and diploid pseudohyphal growth . Second , a strong interaction between MoSom1 and MoStu1 and a weak interaction between MoSom1 and CpkA was detected by yeast two-hybrid analysis . Thirdly , MoSOM1 expression was significantly down-regulated by deletion of MAC1 or CPKA , the two key components of the cAMP/PKA pathway , and finally , the defects of Δmosom1 mutants could not be restored by supplementation with exogenous cAMP . MoSom1 directly interacted with MoStu1 in a yeast two-hybrid assay , and might therefore act as a regulator of MoStu1 to regulate fungal morphogenesis in M . oryzae . In C . albicans , Efg1 , an APSES transcription factor , is essential for regulating morphogenesis [66] . A previous report has demonstrated that C . albicans Flo8 interacts with Efg1 to regulate expression of hypha-specific genes and genes important for virulence [28] . In M . oryzae , MoStu1 is also an APSES transcription factor [31] . Deletion of MoSTU1 results in a reduction of mycelial growth and conidiation and a delay in appressorium formation , and deletion mutants are non-pathogenic [31] . Consistently , we also found that a strong interaction between M . oryzae MoSom1 and MoStu1 in a yeast two-hybrid assay , indicating that MoSom1 may act as a regulator of MoStu1 to regulate fungal morphogenesis . However , because of the different phenotypes of Δmosom1 and Δmostu1 mutants , it seems reasonable to predict that MoSom1 also interacts with other transcription factors in addition to MoStu1 . In a previous study , a direct interaction between S . cerevisae Flo8 and Tpk2 proteins was observed using a modified yeast two-hybrid system carried out in the presence of exogenous cAMP [23] . We found a weak interaction between M . oryzae MoSom1 and CpkA but only when the selection medium was supplemented with 5 mM exogenous cAMP ( Figure 9B ) . This analysis makes a prediction possible that directly places MoSom1 downstream of the cAMP/PKA signaling . In S . cerevisiae , phosphorylation of Flo8 by Tpk2 is required for Flo8 interaction with the FLO11 promoter both in vivo and in vitro [23] . Since multiple PKA phosphorylation sites were also predicted in the MoSom1 protein ( see Figure S8B ) , therefore , in addition to transcriptional regulation , it is possible that MoSom1 is activated by serine/threonine phosphorylation by CpkA to regulate genes required for fungal morphogenesis and pathogenicity . Additionally , we noted that there were obvious different phenotypes between Δmosom1 and ΔcpkA . It is therefore also possible that MoSom1 may be activated by additional regulators from different signaling pathways . LisH domains exist in various eukaryotic proteins and are required for regulating microtubule dynamics , either by mediating dimerization , or by binding cytoplasmic dynein heavy chain or microtubules directly [67] . Like yeast Flo8 , M . oryzae MoSom1 has a LUFS domain with a conserved LisH motif at its N-terminus ( Figure S9A ) . Multiple alignment analyses indicated that the LisH domain is highly conserved in fungi ( Figure 7A ) . We found that the LisH domain is required for the function of MoSom1 in M . oryzae , because deletion of the LisH domain in MoSOM1 partially impaired protein localization to the nucleus and resulted in similar phenotypes to the Δmosom1 mutant ( Figure 7C ) . It is possible that the LisH domain may therefore mediate cytoskeletal interactions necessary for transport of MoSom1 to the nucleus . In S . cerevisiae Flo8 has been localized to the nucleus [18] . Consistent with this , our results also showed that MoSom1 localized to the nucleus and that the predicted NLS of PSKRVRL is important for the function and transportation of MoSom1 protein from the cytoplasm to the nucleus . In this study , we also found the expression of M . oryzae MoSOM1 was significantly down-regulated by deletion of either MAC1 or CPKA ( Figure S10 ) , which encode the key components of the cAMP/PKA pathway and , interestingly , several genes involved in the cAMP/PKA pathway were significantly up-regulated after deletion of MoSOM1 ( Table 1 ) . These data are also consistent with MoSom1 acting downstream of the cAMP/PKA pathway . When considering these results together , we conclude that MoSom1 is likely to act as a transcriptional regulator that functions downstream of the cAMP/PKA pathway to regulate fungal morphogenesis and pathogenicity . M . oryzae appears to possess over 400 transcription factor genes , but only a minority of them have so far been characterized , including MST12 [68] , CON7 [61] , MIG1 [69] , MoHOX8 [62] , COM1 [70] , MoAP1 [71] and MoMCM1 [72] , which are required for fungal morphogenesis or plant infection by M . oryzae . In this study , we identified a novel transcription factor , MoCdtf1 , which is essential for sporulation , apressorium formation and virulence . However , ΔmoCdtf1 mutants were able to cause some disease on wounded leaves or roots , although the disease severity was significantly reduced compared with the isogenic wild-type strain or complemented strains ( Figure 2 ) . These results were consistent with a recent report , in which an insertional mutant M558 was presented in which the T-DNA was integrated into the promoter of MoCDTF1 and also showed impairment in conidiation and pathogenicty , but still infected rice roots [73] . MoCdtf1 has a putative NLS sequence and a conserved zinc finger structure , which are important for MoCdtf1 protein localized to nucleus and for regulating plant infection-related mophorgenesis . Like MoSOM1 , expression of M . oryzae MoCDTF1 was significantly down-regulated by deletion of either MAC1 or CPKA ( Figure S10 ) . More interestingly , we found that MoCdtf1 physically interacts with MoSom1 in a yeast two hybrid assay ( Figure 9A ) . These data suggest that M . oryzae MoCdtf1 may function as a transcription factor that acts downstream of the cAMP/PKA pathway . The importance of MoSom1 to infection-related development was underlined by transcriptional profile analysis using SAGE , which demonstrated that a large set of genes are differentially regulated in a Δmosom1 mutant compared to a wild type M . oryzae strain . Significantly , morphogenetic genes , such as the MPG1 hydrophobin gene and the BUF1 melanin biosynthesis gene , as well as physiological regulators such as the TPS1 trehalose-6-phosphate synthase gene were among those differentially regulated . This is consistent with MoSom1 affecting processes pivotal to the formation and function of appressoria and acting downstream of the cyclic AMP signaling pathway , which is necessary for infection-related development in rice blast . The pleiotropic effects of the Δmosom1 mutation on mycelial growth rate do , however , suggest that some of the observed major changes in gene expression may be a consequence of the slower growth rate and aberrant mycelial morphology of Δmosom1 mutants . Dissecting specific families of genes regulated by the moSom1 pathway during appressorium development will therefore be important in elucidating the underlying biological processes regulated by this signaling mechanism . In summary , based on results from this report , we have developed a model of the cAMP/PKA signaling pathway in M . oryzae that is shown in Figure 11 . Surface recognition and initiation of appressorium formation is regulated by the pathway . Moreover , the cAMP/PKA pathway is also involved in regulation of hyphal growth , asexual/sexual sporulation and invasive growth in host tissues . Free CpkA may activate MoSom1 protein to regulate appressium turgor generation through MoStu1 and to control sporulation and appressorium formation through MoCdtf1 . However , it is also possible that additional transcription factors are regulated by MoSom1 to control these developmental processes . The model will allow us to test the wider roles of the cAMP/PKA pathway in regulating fungal morphogenesis and plant infection in M . oryzae in future .
All mutants described in the present study were generated from the Magnaporthe oryzae wild-type strain Guy11 [74] , and are listed in Table S1 . Standard growth and storage procedures for fungal strains were performed , as described previously [58] . A . tumefaciens AGL1 was used for T-DNA insertional transformation . Escherichia coli strain DH-5α was used for routine bacterial transformations and maintenance of various plasmids in this study . Southern blot analysis was performed by the digoxigenin ( DIG ) high prime DNA labeling and detection starter Kit I ( Roche , Mannheim , Germany ) . General procedures for nucleic acid analysis followed standard protocols [75] . Total RNA was extracted from mycelium of M . oryzae using the SV Total RNA Isolation System ( Z3100; Promega Corp . ) according to the manufacturer's instructions . For construction of the gene replacement vector pMoSOM1-KO ( Figure S3C ) , 1 . 4 kb ( left border ) and 1 . 2 kb ( right border ) flanking sequences of the MoSOM1 gene locus were amplified using primer pairs of 3F/4R and 5F/6R ( Table S2; Figure S3C ) and cloned sequentially into pGEM-T easy vectors to generate pGEM-145L and pGEM-145R , respectively . The 1 . 4 kb HPH gene cassette , which encodes hygromycin phosphotransferase under control of the A . nidulans TrpC promoter [76] , was amplified with primers HPH-Kpn-F and HPH-Xba-R ( Table S2 ) using pCB1003 as a template and clone into pGEM-T easy vectors to give pGEM-HPH . The pGEM-HPH was digested with KpnI and ApaI and inserted the fragment from pGEM-145R with the same digestions to generate pGEM-HPH-R . The pMoSOM1-KO was constructed by insertion SpeI and XbaI fragment from pGEM-145L into corresponding site of pGEM-HPH-R . To construct complementation vector pMoSOM1-GFP , a 4 . 3 kb fragment including 2 . 8 kb MoSOM1 gene-coding sequence and a 1 . 5 kb promoter region were amplified using primers 145H-Nde-F and 145H-Hind-R ( Table S2 ) and then cloned into pGEM-T easy vectors to produce pGEM-SOM . The pMoSOM1-GFP was generated by ligation of pGEM-SOM with the 1 . 5 kb GFP allele , which was amplified using primers GFP-Hind-F and GFP-Xho-R ( Table S2 ) . The pMoSOM1-DKO vector was constructed by replacing the HPH of pMoSOM1-KO with a 0 . 94 kb bar gene cassette encoding phosphinothricin acetyl transferase under control of the A . nidulans TrpC promoter , which was amplified with primers Bar-Xba-F and Bar-Kpn-R ( Table S2 ) using pMLH21-bar [77] as a template . The MoSOM1 over-expression vector , pOE-MoSOM1 , was constructed by insertion the 4 . 3 kb fragment ( 2 . 8 kb MoSOM1 gene-coding sequence and 1 . 5 kb GFP cassette ) , which was amplified with the primers 145OE-Xho-F and GFP-Xho-R ( Table S2 ) using the pMoSOM1-GFP as a template , into the corresponding site of pCB1532 with the A . nidulans trpC promoter . A similar strategy was used to construct the gene replacement vector pMoCDTF1-KO . About 1 . 2 kb ( left border ) and 1 . 5 kb ( right border ) flanking sequences MoCDTF1 gene locus were amplified using primer pairs of 7F/8R and 9F/10R ( Table S2; Figure S3E ) and cloned sequentially into pGEM-T easy vectors to generate pGEM-1303L and pGEM-1303R , respectively . The pGEM-1303R was digested with SacI and XbaI and the released fragment was inserted into the corresponding site of pGEM-HPH to produce pGEM-1303HR . The pGEM-1303HR was digested with KpnI and ApaI and then inserted with the fragment liberated from pGEM-1303L to generate pMoCDTF1-KO . To construct complementation vector pMoCDTF1-GFP , a 5 . 7 kb fragment including 4 . 1 kb MoCDTF1 gene-coding sequence and a 1 . 6 kb promoter region were amplified using primers 1303H-Aat-F and 1303H-Kpn-R ( Table S2 ) and then cloned into pGEM-T easy vectors to produce pGEM-CDTF . The pMoCDTF1-GFP was generated by ligation of pGEM-CDTF with the 1 . 5 kb GFP allele , which was amplified using primers GFP-Kpn-F and GFP-Xho-R ( Table S2 ) . To construct the MoMSB2 gene replacement vector pMoMSB2-KO ( Figure S3A ) , a 4 . 2 kb fragment spanning the MoMSB2 locus was amplified with primers 1F and 2R ( Table S2 ) and cloned into pGEM-T easy vector ( Promega , Madison , WI , U . S . A . ) , and a 1 . 7 kb Xho I and Spl I fragment containing the majority of the MoMSB2 ORF was removed and replaced sequentially with the 1 . 4 kb HPH gene cassette amplified with primers HPH-Spl-F and HPH-Xho-R ( Table S2 ) using pCB1003 as a template . For construction of complementation vector pMoMSB2-HB , a 4 . 2 kb fragment including 2 . 4 kb MoMSB2 gene-coding sequence and a 1 . 8 kb promoter region were amplified using primers 864H-Sal-F and 864H-Spe-R ( Table S2 ) and then cloned into pGEM-T easy vectors to produce pMoMSB2-HB . For deletion of the MAC1 gene , the gene deletion vector pMoMAC1-KO was generated using a similar strategy to pMoMSB2-KO . A 4 . 8 kb fragment spanning the MoMAC1 locus was amplified with primers MAC-KO-FP/MAC-KO-RP ( Table S2 ) and cloned into pGEM-T easy vector to give pGEM-MAC1 . The HPH gene cassette was amplified with the primers HPH-Hind-F and HPH-Hind-R ( Table S2 ) using PCB1003 as a template . The pMoMAC1-KO was constructed by insertion HPH gene cassette with HindIII ends into the corresponding restriction site of pGEM-MAC1 . The vector for deletion of MAGA gene was kindly provided by professor Hao Liu , Tianjin University of Science and Technology . The resulting vectors were linearized and transformed into M . oryzae Guy11 protoplasts to generate gene null mutants , respectively , as previously described [58] . Together with pCB1532 [78] vectors , the complementation vectors , pMoSOM1-GFP , pMoCDTF1-GFP and pMoMSB2-HB , were used to co-transform into their corresponding mutants , respectively . The vector pOE-MoSOM1 was used to transform Δmac1 and ΔcpkA mutants to generate strains that MoSOM1 was over-expressed , respectively . GFP fluorescence was observed using a Leica TCS SP5 inverted confocal laser scanning microscope ( Leica , Wetzlar , Germany ) . Three rounds of PCR amplification were carried out for the construction of pMoSOM1ΔLisH-GFP described as follows . First , 1 . 6 kb and 4 . 0 kb fragments were amplified with the primer pairs of 145H-Nde-F/LisH-R and LisH-F/GFP-Xho-R ( Table S2 ) using pMoSOM1-GFP as a template , respectively . Second , the two PCR products were mixed and performed PCR reaction ( 10 reaction cycles ) without adding primers . Third , a 5 . 6 kb fragment containing 1 . 5 kb native MoSOM1 promoter , 2 . 6 kb MoSOM1 gene-coding sequence ( without Lish domain ) and 1 . 5 kb GFP cassette was amplified by the primers 145H-Nde-F and GFP-Xho-R ( Table S2 ) using the mixture as a template . Finally , the pMoSOM1ΔLisH-GFP was generated by insertion of the 5 . 6 kb fragment into pGEM-T easy vector . A similar strategy was used to construct pMoSOM1ΔPKKK-GFP and pMoCDTF1ΔPPKRKKP-GFP vectors . The pMoSOM1ΔPKKK-GFP was generated from pMoSOM1-GFP using primer pairs of 145H-Nde-F/PKKK-R and PKKK-F/GFP-Xho-R ( 3 . 7 kb and 2 . 1 kb PCR products , respectively ) , whereas the pMoCDTF1ΔPPKRKKP-GFP was generated from pMoCDTF1-GFP using primer pairs of 1303H-Aat-F/1303CD-R and 1303CD-F/GFP-Xho-R ( 4 . 8 kb and 2 . 4 kb PCR products , respectively ) . The pMoSOM1ΔPSKRVRL-GFP was constructed by self-ligation of the PCR products amplified with primers PSK-F and PSK-R ( Table S2 ) using pMoSOM1-GFP as a template . The primers used for the constructions were listed in Table S2 . The pMoSOM1ΔLisH-GFP , pMoSOM1ΔPKKK-GFP , pMoSOM1ΔPSKRVRL-GFP were used to transform the Δmosom1 mutants to generate MoSOM1ΔLisH , MoSOM1ΔPKKK , MoSOM1ΔPSKRVRL , respectively . The pMoCDTF1ΔPPKRKKP-GFP was used to transform Δmocdtf1 to produce MoCDTF1ΔPPKRKKP . For cut-leaf assays , fragments were cut from the leaves of 10-day-old barley cv Golden Promise and 14-day-old rice cv CO-39 seedlings , both highly susceptible toward M . oryzae , and placed in plastic plates containing wetted filters . Mycelium from 2-day-old liquid CM cultures at 25°C was placed onto leaf sections and the plates were incubated in a cycle of 12 h of light and 12 h of dark at 25°C . Wounded leaves were prepared by removing the surface cuticle by abrasion with an emery board as described previously [79] . For spray-inoculation assays , conidial suspensions were diluted in 0 . 2% gelatin to 1×105 conidia ml−1 for rice infections using rice cv . CO-39 . Conidia were spray-inoculated using an artist's airbrush onto 14-day-old plants . Rice seedlings were incubated in plastic bags for 24 h to maintain high humidity and then transferred to controlled environment chambers at 25°C and 90% relative humidity with illumination and 14 h light periods . For root infection assays , rice seeds were germinated for 3 days at 28°C and then transferred to plates contained 2% water agar . Mycelial plugs were carefully placed rice roots . Each test was repeated three times . Disease lesions were examined and photographed after 5 days of incubation . Vegetative growth was assessed by measurement of colony diameter on plate cultures of M . oryzae grown on CM . For mycelium dry weight assays , the same size blocks ( 1×1 . 5 cm2 ) cut from 7-day-old CM cultures were blended and inoculated in flasks containing 150 ml liquid CM medium . The flasks were incubated at 25°C for 2 days ( 150 rpm ) . After incubation , the mycelia produced in liquid cultures were filtered and washed . The dry weight of each mycelium was determined after drying at 60°C for 24 h . Three replicates of each treatment were performed , and the experiment was repeated three times . Conidial development was assessed by harvesting conidia from the surface of 10-day-old plate cultures and by determining the concentration of the resulting conidial suspension using a haemocytometer . Appressorium development was assessed by allowing conidia at a concentration of 1×104 conidia ml−1 to germinate on hydrophobic GelBond films or onion epidermis and incubating them in a humid environment at 25°C . For appressorium formation from the tips of mycelia , mycelia of the wild-type strain Guy11 and mutant strains were harvested from 48 h liquid CM cultures , and the mycelium fragment suspensions were placed on hydrophobic GelBond film surfaces to allow appressorium development . Appressorium formation was observed after 24 h incubation at 25°C in darkness . Fertility assays were carried out by pairing Guy11 ( MAT1-2 ) and tested strains with standard tester strain TH3 ( MAT1-1 ) on oatmeal agar ( OMA ) plates , as described previously [60] . Each test was repeated three times . Total RNA was utilized for synthesis of the first strand cDNA using the PrimeScript™ 1st Strand cDNA Synthesis Kit ( D6110A , TaKaRa , Tokyo ) . The resultant cDNA was used as a template for quantitative RT-PCR ( qRT-PCR ) . qRT-PCR was performed with a SYBR Green Realtime PCR Master Mix Kit ( QPK-201 , TOYOBO , Osaka , Japan ) using an iCycler iQ™ Multicolor Real-Time PCR Detection System ( Bio-Rad , Munich , Germany ) . All qRT-PCR reactions were conducted in triplicates for each sample and the experiment was repeated three times . M . oryzae beta-tubulin gene ( MGG_00604 ) amplified with the primer pairs of BT-F/BT-R was used as an endogenous reference . The abundance of the gene transcripts was calculated relative to this control using the 2−ΔΔCT method [80] . All the primers used for qRT-PCR were listed in Table S2 . Yeast complementation was carried out as described previously [28] . The full length cDNA of MoSOM1 was amplified with primers SOM-E-F and SOM-Xh-R and cloned into pYES2 vector to generate pYES2-SOM1 . The yeast expression vector pYES2-SOM1 was transformed into the haploid mutant HLY850 and the diploid mutant HLY852 of S . cerevisiae , respectively . The transformants grown on SD-Ura plates were selected to test the ability of invasive growth on YPD plate and the pseudohyphal growth on SLAD plate supplemented with galactose . The yeast strains , MY1384 ( MATa wild type ) , HLY850 ( MATa flo8::hisG ura3-52 ) , CGx68 ( MATa/α wild type ) and HLY852 ( MATa/α flo8::hisG/flo8::hisG ura3-52/ura3-52 ) , were kindly provided by Professor Jiangye Chen of Shanghai Institute for Biological Sciences , Chinese Academy of Sciences . The Y2H assay was conducted according to the BD Matchmaker Library Construction & Screening Kits instructions ( Clontech , PaloAlto , CA , U . S . A . ) . The full-length cDNA of MoSOM1 , MoCDTF1 , MoSTU1 , MoLDB1 and CPKA was amplified with the primer pairs SOM-E-F/SOM-Xh-R , 1303YTH-E-F/1303YTH-E-R , STU-E-F/STU-E-R , LDB-E-F/LDB-S-R and CPK-F/CPK-R ( Table S2 ) , respectively . The cDNA of MoSOM1 was cloned into pGADT7 as the prey vector pGADT7-MoSOM1 and the other cDNAs were cloned into pGBKT7 as the bait vector , respectively . The resulting pGADT7-MoSOM1 and each bait vector were co-transformed into yeast strain AH109 . The Leu+ and Trp+ yeast transformants were isolated and assayed for growth on SD-Trp-Leu-His-Ade medium . Yeast strains for positive and negative controls were from the Kit . The M . grisea wild-type strain Guy11 and the mutants , SK27 ( Δmosom1 ) , AK58 ( Δmoldb1 ) [60] and Q-10 ( Δmoric8 ) [56] , were cultured in liquid CM medium at 28°C for 48 h in the dark ( at 200 rpm ) . The mycelium of these strains was harvested , and total RNA was extracted using the SV Total RNA Isolation System ( Z3100; Promega ) according to the manufacturer's instructions . The RNA samples were then sent to Beijing Genomics Institute ( BGI; Huada ) for serial analysis of gene expression ( SAGE ) .
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Magnaporthe oryzae , the causal agent of rice blast disease , is an important model fungal pathogen for understanding the molecular basis of plant-fungus interactions . In M . oryzae , the conserved cAMP/PKA signaling pathway has been demonstrated to be crucial for regulating infection-related morphogenesis and pathogenicity , including the control of sporulation and appressorium formation . In this study , we report the identification of two novel pathogenicity-related genes , MoSOM1 and MoCDTF1 , by T-DNA insertional mutagenesis . Our results show that MoSOM1 or MoCDTF1 are essential for sporulation , appressorium formatiom and pathogenicity , and also play a key role in hyphal growth , melanin pigmentation and cell surface hydrophobicity . Nuclear localization sequences and conserved domains of the MoSom1 and MoCdtf1 proteins are crucial for their biological function . MoSom1 interacts physically with the transcription factors MoCdtf1 and MoStu1 . We also show evidence that MoSom1 has the capacity to interact with CpkA , suggesting that MoSom1 may act downstream of the cAMP/PKA signaling pathway to regulate infection-related morphogenesis and pathogenicity in M . oryzae . Our studies extend the current understanding of downstream components of the conserved cAMP/PKA pathway and its precise role in regulating infection-related development and cellular differentiation by M . oryzae .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetic",
"mutation",
"microbiology",
"host-pathogen",
"interaction",
"gene",
"function",
"fungi",
"microbial",
"growth",
"and",
"development",
"fungal",
"reproduction",
"mycology",
"gene",
"expression",
"microbial",
"pathogens",
"biology",
"pathogenesis",
"molecular",
"biology",
"signal",
"transduction",
"genetics",
"molecular",
"cell",
"biology",
"spores",
"genetics",
"of",
"disease",
"genetics",
"and",
"genomics"
] |
2011
|
Two Novel Transcriptional Regulators Are Essential for Infection-related Morphogenesis and Pathogenicity of the Rice Blast Fungus Magnaporthe oryzae
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Understanding how binding events modulate functional motions of multidomain proteins is a major issue in chemical biology . We address several aspects of this problem by analyzing the differential dynamics of αvβ3 integrin bound to wild type ( wtFN10 , agonist ) or high affinity ( hFN10 , antagonist ) mutants of fibronectin . We compare the dynamics of complexes from large-scale domain motions to inter-residue coordinated fluctuations to characterize the distinctive traits of conformational evolution and shed light on the determinants of differential αvβ3 activation induced by different FN sequences . We propose an allosteric model for ligand-based integrin modulation: the conserved integrin binding pocket anchors the ligand , while different residues on the two FN10’s act as the drivers that reorganize relevant interaction networks , guiding the shift towards inactive ( hFN10-bound ) or active states ( wtFN10-bound ) . We discuss the implications of results for the design of integrin inhibitors .
Integrins are heterodimeric cell adhesion receptors , composed by the association of α and β subunits . They are typically characterized by a bilobular head and two legs that span the plasma membrane . Integrin ectodomains have been crystallized in a bent , genuflexed conformation ( corresponding to the inactive or closed state ) as well as in an open one ( corresponding to the active state ) with high-affinity for ligands . [1 , 2] Conformational changes from the bent to the open structures in integrin extracellular , transmembrane and cytoplasmic domains underlie a diverse range of biological processes , including cell migration , morphogenesis , immune responses , vascular haemostasis , cell-to-cell interaction and intracellular signal transduction . The dysregulation of these processes contributes to the pathogenesis of many diseases . [3] In particular , αvβ3 , αvβ5 and α5β1 integrins are involved in angiogenesis , tumor progression and metastasis , whereas the platelet αIIbβ3 receptor is central to haemostasis and contributes to thrombosis . [4–7] Determination of the crystal structure of the ectodomain of αvβ3 in the absence and presence of a prototypical RGD ligand unveiled the modular nature of integrins and clarified the basis of the divalent cation—mediated interaction with extracellular ligands . [1 , 8] The ectodomain of αvβ3 revealed a “head” attached to two “legs” in the native , full-length integrin , whereby the legs connect to short transmembrane and cytoplasmic segments . The integrin head consists of the seven-bladed β-propeller domain from the αV non-covalently bound to the βA domain of the β3 subunit ( Fig 1a ) . The αV leg is formed by an Ig-like “thigh” domain attached to two large colinear β-sandwich domains , designated calf-1 and calf-2 . The β3 leg is formed by an Ig-like hybrid domain , the βA projecting from one of its loops , a PSI domain , four EGF-like domains , and a membrane proximal β-tail domain ( βTD ) . The RGD sequence of physiologic ligands typically engages αvβ3 in a cleft between the β-propeller and βA domains , producing a characteristic electrostatic clamp between the guanidinium moiety of the RGD-triad and aspartic acids of the αv subunit and , at the same time , enabling RGD-Asp ( Oδ1/Oδ2 ) to coordinate a metal ion at MIDAS ( Metal Ion-Dependent Adhesion Site ) of the β3 subunit ( Fig 1b and 1c ) . [9] The crystal structures of the bound and unbound forms of αIIbβ3 and αvβ3 provide a structural model of the ligand-effects on the two proteins , and of their activation mechanism . [2 , 8 , 10] In this context , experiments have shown that ligand binding to the head of αIIbβ3 induces the opening of the hinge between the βA and hybrid headpiece domains , implying a transition from the closed unbound state to the open one upon ligand binding . [2 , 11–14] Different structural observations , based on soaking the RGD containing drug cilengitide into αvβ3 crystals have implied that the hinge is closed even in the presence of the ligand . [1 , 15] According to structural modeling based on EM-data for αvβ3 , and on EM , SAXS cryo-tomography and FRET for αIIbβ3 , extension at the knees unclasps integrins from a compact bent conformation , where the legs are bent at the knees and folded back against the head . Subsequently , in integrin headpiece opening towards the active state , the hybrid domain swings out , the βA domain evolves to the open conformation , and affinity for other endogenous ligands increases . The bend occurs between the thigh and calf-1 domain of αV and between EGF domain 1 and 2 of β3 . [8 , 11] Adair and co-workers suggested that ligand binding provides the energy for additional conformational changes , including perhaps genu-extension , thus triggering integrin out-in activation and signaling . [16] The conformational changes vary with cell type and the state and nature of the ligand . [8 , 11 , 17] Endogenous integrin-ligands include proteins such as fibronectin , fibrinogen , vitronectin , collagen , laminin , that mediate the coupling between the cell and the extracellular matrix ( ECM ) and cellular cytoskeleton through adaptor molecules like actin , talin , filamin etc . [17–20] The chemical and structural properties of the framework around the RGD sequence and the complementary features displayed by the integrin binding pockets have been shown to affect recognition between the partners and to determine the functional consequences of the interaction . [3] Fibronectin ( FN ) is a prominent example of how differential recognition between sequences is translated into different functional consequences . FN is a widely expressed ECM protein and a promiscuous ligand for integrins as well as for numerous other cell adhesion receptors . FN exists as a soluble dimeric glycoprotein of two monomers , each of them composed by three repeating modules . As in the case of other integrin ligands , FN-integrin interactions are mediated by the RGD motif located on the 10th type III repeat . Recently the 3D structures of αvβ3 bound to the 10th type III RGD domain of wild-type fibronectin ( wtFN10 ) and its high affinity mutant ( hFN10 ) have been solved by Arnaout and collaborators ( Fig 1b and 1c ) . [3] The main differences between the two mutants are found in the sequences flanking the RGD binding motif: specifically , wild-type fibronectin ( wtFN10 ) sequence -GRGDSPAS- is replaced by -PRGDWNEG- in high affinity fibronectin ( hFN10 ) . hFN10 -PRGDWNEG- sequence is more polar compared to wild-type -GRGDSPAS-; it also displays a larger surface due to the presence of the tryptophan substitution . These sequence differences appear to modulate the ligand effects on the integrin , ultimately affecting integrin activation . Indeed , while wtFN10 represents an integrin agonist , hFN10 behaves as a real antagonist . [3] Crystal structures of the αvβ3–hFN10 complex ( pdb code: 4MMZ ) provided an important structural framework to investigate the activity of hFN10 as a pure antagonist; here , the novel W1496 ( hFN ) -Y122 ( αvβ3 ) π-π interaction is hypothesized to ‘freeze’ the integrin in an inactive conformation ( Fig 1c ) . This βA tyrosine is largely conserved also in α5β1 and β2 integrins , supporting its key function in the activation process . [3] Moreover , the inward movement of Y122-βA described for the wild type FN ( wtFN10 ) complex ( pdb code: 4MMX ) would be incompatible with the integrin-hFN10 crystal structure where Y122 aromatic ring would clash with mutated fibronectin residue W1496hFN . From biophysical data and cell-based assays , hFN10 actually behaves as a pure antagonist , which does not induce activation-specific LIBS ( Ligand Induced Binding Site epitopes ) expression and also reduces cell spreading , that is an index of outside-in signaling by ligand-occupied integrins . Moreover , it does not affect the hydrodynamic radius of the soluble αvβ3 ectodomain , indicating a stable compact/bent form . [3] The above-mentioned studies and the availability of high resolution crystal structures of the two complexes provide an optimal starting point to investigate the differential aspects of functional dynamics induced by limited sequence differences in the FN ligands when bound to αvβ3 , and consequently shed light onto the determinants of integrin activation processes . Herein , we set out to compare several aspects of the dynamics of integrin αvβ3 in complex with wtFN10 or hFN10 , as well as in the unbound state ( apo ) , that can be linked to observed biological activities of the molecules . To progress along this route , we carry out microsecond long MD simulations of the two complexes and compare their dynamic evolution from the fine level of inter-residue coordinated fluctuations to larger scale domain motions . We characterize the main distinctive traits of conformational evolution of the two complexes and propose a model for the determinants of FN-induced differential activation of αvβ3 . It is to be noted that the main differences between the two complexes emerge at the level of internal dynamics and local reorganizations ( the RMSD between the X-ray structures of the integrin in the wtFN10 and hFN10 complexes is limited to 0 . 2 nm ) . Indeed , while no major spontaneous transition from the analogous starting crystal structures can be reported , the local structural and chemical organization similarity of the β3 subunit and of the coordination at the MIDAS/ADMIDAS sites between the wtFN10 complex and the S7/S8 states observed by Springer and coworkers [10] for αIIbβ3 may be indicative of the higher tendency for the wt complex to favor transition to the open state . Finally , we discuss the implications of our results for the design and optimization of ligand-mimetic integrin inhibitors .
αvβ3 ectodomain includes β-propeller ( aa . 1–438 ) and thigh ( aa . 439–599 ) domains of subunit α and βA ( aa . 109–352 ) and hybrid ( aa . 55–108 , 353–434 ) domains of chain β ( Fig 1 ) . Fibronectin subunit extends from aa . 1417 to 1507 , engaging the interfaces of αvβ3 . The two studied fibronectin molecules target αvβ3 placing the RGD motif at a crevice in the integrin head between the β-propeller and the βA domains making extensive contacts with both . RGD ligands bind to the integrin β subunit via a divalent metal ion located at the top of the βA domain , named the “metal ion-dependent adhesion site” ( MIDAS ) . Two additional ion-binding sites border the βA domain MIDAS on either side , which are termed the “ligand-induced metal-binding site” ( LIMBS ) and the “adjacent to the MIDAS” ( ADMIDAS ) . Differences in the sequences near the RGD binding motif at the α/β interface , combined to the different placing of the two FNs relative to the integrin in the complex , may induce dynamic events throughout the integrin structure that can translate into a differential activation of the integrin ( Fig 1 ) . In general , both αvβ3-FN10 complexes are stable and metal coordination , at both β-propeller and integrin-FN interface , is conserved during the whole length of MD simulations . However , significant differences in the structural evolution immediately emerge , indicating the influence of the FN sequences surrounding the RGD motif and of the initial orientations on the dynamics of the complexes . Indeed , Essential Dynamics ( ED ) [30] analysis ( Fig 2a ) on the trajectories highlights a rich behavior for the complex with wtFN10 , in which two conformational populations are observed , both converging to more compact complex structures than the starting one . In Fig 2b the representative structures of the two clusters are indicated: only a small deviation of the thighαv domain occurs in the less populated ensemble ( Fig 2b inset ) . In contrast , one main conformational ensemble is populated for the complex with hFN10 , which substantially corresponds to the crystallographic structure ( Fig 2a and 2c ) . Conformational variability of the uncomplexed integrin ( 1JV2 ) [8] at the level of large-scale displacements is analyzed here as a control by looking at the projections on main eigenvectors from PCA analysis of the respective MD trajectories . It is worth noting here that except for the absence of ligand and of the metal ions ( at LIMBS and MIDAS ) , the X-ray structure of the apo state shows a 0 . 258 nm RMSD deviation ( Calpha atoms ) from αvβ3 in wtFN10-bound form ( 4MMX ) and 0 . 234 nm rms deviation from αvβ3 in hFN10-bound structure ( 4MMZ ) . In general the conformational dynamics of the apo state seems to be richer than the one observed for the antagonist bound hFN10-complex , while not sampling all of the conformations of the agonist bound wtFN10-complex . Details of ED analysis are reported in supporting information ( S1 and S5 Figs ) . The structural transition between integrin active and inactive states is induced by global rearrangements of the headpiece upon fibronectin binding . To better characterize the global conformational changes that differentiate the two complexes , we analyzed the reciprocal orientations of selected subdomains in αvβ3 and of the bound wtFN10 or hFN10 . Accordingly , we defined the principal axes of integrin β3 subunit and fibronectin domains and calculated the correspondent torsion angle along the MD trajectories ( see Methods ) . [31] Consistent with ED findings , fibronectin in the high affinity complex maintains the crystallographic orientation and the torsion angle fluctuates only slightly ( +/- 20° ) , oscillating between 56° and 82° . On the other hand , well-defined rotational motions characterize the wtFN10 complex , where the angle ranges between 21° and 84° ( S2 Fig in supporting information ) . Comparable results were obtained with DynDom . [32 , 33] In two out of the three hFN10 replicas , DynDom fails to identify clusters of rotation vectors , therefore bound hFN10 is not captured by the algorithm as an independently moving domain . In the complex with wtFN10 , comparing starting and final conformations , the principal rotation axis discriminates motions of the fibronectin with respect to the β3 subunit , with a rotation angle of around 55° . The Radius of Gyration ( Rg ) and the evolution of the end-to-end distance of the thigh and hybrid domains ( defined between the Cα of two terminal residues -namely I592αv and R404β3 of thigh and hybrid domains , respectively ) were next monitored as descriptors of integrin dynamic response to different ligands ( Fig 3 ) . Moreover , given the critical role of ADMIDAS in βA domain allostery [21] we also monitored the relative positions of the carbonyl oxygen of M335β3 and ADMIDAS Mn2+ , since it was previously shown as a viable indicator of the closed-to-open transition in the eight steps atomic description of the RGD-induced opening occurring in the β3 subunit of αIIbβ3 . [10] In the last opening steps ( from state 6 to state 8 ) ADMIDAS ion shifts dragging along α1 helix as a rigid body with its coordinated D126β3 and D127β3 while M335β3 at the β6-α7 loop moves apart . [10] Fig 3 compares the distribution of Rg , end-end distances and M335β3 –ADMIDAS distances for αvβ3-wtFN10 and αvβ3-hFN10 complexes for all replicas: a more dynamic behavior is consistently observed for wtFN10 , indicated by the wider distributions of the distances between the thigh and hybrid domains , the Rg and the M335β3-ADMIDAS distances . In wtFN10 X-ray structure M335β3-ADMIDAS distance is 14 . 6 Å while in hFN10 is 2 . 9 Å . For the wtFN10 system the distribution shows a broad range of values around ~ 12 . 4 Å , while for hFN10 the distribution is more sharply centered around two values , 2 . 6 Å and 5 . 2 Å . Time-dependent evolution of M335β3-Mn2+-ADMIDAS distances together with representative integrin conformations are given in S3 Fig to account for the tails in the distribution analysis of the hFN10 system . Notably , even though some variations in the M335β3-Mn2+-ADMIDAS for the hFN system can transiently become similar to the wtFN system , these variations are not paralleled by the global rearrangement of the integrin . Taken together , these first observations support the onset of different dynamic regimes in the two complexes , suggesting that the ability of the wtFN10 complex to explore a larger conformational ensemble may be linked to a more favorable tendency towards activation . The type of bound fibronectin and the consequent differences in the corresponding X-ray structures appear to have a profound influence on the dynamic evolution of the complexes . To gain fine-grained insights into the determinants of the dynamic differences we calculated the fluctuations of pairwise amino acid distances ( Distance Fluctuation , DF ) [34] in the MD trajectories of the two complexes ( see Methods ) . Such measure has been previously shown to shed light on the effects of ligands on internal long-range pair coordination . [35] While intra-domain coordination is somewhat expected given the spatial proximity between amino acids , the analysis of coordination patterns between residues that belong to different domains can aptly highlight internal dynamic modulations that depend on the identity of the bound ligand . The DF matrix for the complex with wtFN10 ( Fig 4a ) indicates more fluctuating internal dynamics . Regions of strong dynamic coordination ( low fluctuation ) alternate with regions where inter-residues distances show large fluctuation , i . e . low coordination . In particular , αv-thigh and β3-hybrid domains appear as the regions of greatest variation . In an interesting contrast , the presence of high-affinity FN ( hFN10 ) reverberates in the increase of overall rigidity of the αvβ3 integrin , characterized by patterns of highly coordinated ( low fluctuation ) residue pairs that diffuse throughout the structure of the whole protein: in particular , in Fig 4b DF matrices show that thigh and hybrid domains of the αv and β3 are highly coordinated . Increased rigidity and coordination among different domains may oppose the onset of conformational changes required for integrin activation . Additional information on the mechanistic determinants of different functional properties is obtained by analyzing the residue-based root mean square fluctuations of αvβ3 in the two complexes . Interestingly , this simple measure shows that the largest divergences occur at the αvβ3 interacting surfaces . In particular , fluctuations in the β-propeller-βA interfaces in the wtFN10 complex are much larger than in the case of the high-affinity complex . Averaged fluctuations and relative standard deviations for the three replicas per system are displayed in Supporting Information in S4 Fig . Overall , these data indicate that the two different FN10 sequences trigger specific differential dynamic responses in the two complexes , evident at the level of macroscopic structural changes as well as at the level of microscopic internal coordination . Binding of wtFN10 or hFN10 at the interface between the integrin alpha and beta subunits may thus be linked to the onset of different functionally oriented conformational events . It is necessary to underline here that MD simulations carried out on the uncomplexed αvβ3 as control show that the apo dynamics is less rigid than the hFN10 and more reminiscent of the dynamical character of agonist bound wtFN10 ( S5 Fig ) . It is to be noted here that the apo system lacks LIMBS and ADMIDAS ions due to crystallization conditions . The knowledge obtained so far on differential aspects of the αvβ3-fibronectin dynamics is complemented here by a comparative analysis of the fine specific interactions in the interface region that can be aptly used to make manifest the relatedness between the binding of different sequences flanking the RGD motif and the origin of specific functional dynamics . The high affinity -PRGDWNEG- sequence in hFN10 favors a characteristic placing of the RGD motif at the αvβ3 interface , in which fibronectin W1496hFN is observed to limit the mobility of such fragment and of the whole hFN10-complex as a consequence: at the interacting surfaces , hFN10 shows extensive packing with bulky aromatic amino acids from the βA domain . At the beginning of the simulation , aromatic side-chains of Y122β3 , W129β3 , Y1446hFN and W1496hFN align to get closer to one another and form a tightly packed aromatic cluster . The stability of such association is validated by pairwise centroid distances and interplanar angle ( θ ) evolution along the full-length simulation time ( 1 . 5 μs ) ( Fig 5 and S6 Fig ) . At the same time , this conformation permits the electrostatic clamp of the Arginine guanidinium of the RGD motif with D218αv , D150αv and it favors the stabilization of the initial coordination of Mn2+ ions at LIMBS , ADMIDAS and MIDAS sites . Overall , the hydrophobic packing may 'direct' the onset of novel interactions , acting as the driving force for the stabilization in solution of the structure obtained by crystallography . The combination of bulky and packed organization of residues , electrostatic stabilizing interactions and metal coordination sum up to impede movements in the immediate vicinity , limiting the motional freedom of hFN10 ( Fig 2 and S1 Fig ) . The final consequence is that no conformational rearrangement is observed , and the original/crystallographic orthogonal orientation at the interface is conserved . Overall , in the hFN10 complex the conformation of the β3 subunit and the coordination at MIDAS/ADMIDAS are qualitatively similar to the S1 state of αIIβ3 integrin described by Springer [10] as a closed/inactive state , as already mentioned . It should be noted that this similarity is mostly qualitative , as the two integrins represent different systems , with different organizations and sequences . In contrast , in wtFN10 , the fibronectin domain bends upon βA domain providing access to dynamic states that lead to viable conformations alternative to the ones observed in the crystal structures . The Oδ1/Oδ2 Asp ( RGD ) coordination of the metal ion at MIDAS appears to be less stable than the correspondent one in hFN10 ( S7 Fig ) . Furthermore , for wtFN10 the classical interaction between R1493wtFN and D150αv and D218αv is broken to form a novel interaction with D219αv while for hFN10 is mainly conserved . On the other hand , the coordination of D1495FN of RGD motif to the MIDAS cation is kept in all replicas of both wtFN10 and hFN10 systems ( S7 Fig ) . Finally , in wtFN10 the sequence flanking the RGD motif , -GRGDSPAS- , cannot establish the hydrophobic association determined by the tryptophan indole ( -PRGDWNEG- ) in hFN10 . [1 , 2] In wtFN10 Y1446wtFN is engaged in interactions with αv chain ( see S2 Table ) while integrin W129β3 ( βA ) is free to flip outward , populating an alternative rotameric state . The higher conformational variability of W129β3 ( βA ) in the complex with wtFN10 compared to hFN10 is clearly highlighted by structural cluster analysis ( see Methods and S8 Fig ) . In the wtFN10 case , two major conformational clusters are visited , corresponding to the starting X-ray-like and flipped orientations of the side chain . The two conformations are reminiscent of the S7/S8 states defined by Springer and coworkers [10] , respectively . In contrast , in hFN10 the indole side-chain is mostly exposed to the solvent in an arrangement overall similar to the starting conformation . The observed α1-W129β3 rotamer switch in wtFN10 causes a general rearrangement in the interaction network by invading the space of the β6-α7 loop , consistent with the mechanism proposed by Springer and collaborators for the last steps toward αIIbβ3 opening path . [10] In hFN10 , W129β3 preferentially stabilizes around the starting conformation , thanks to extended packing with hFN10 W1496hFN ( to be noted that this residue corresponds to S1496 in wtFN10 ) . Overall , in the wtFN10 complex the conformation of the β3 subunit and the coordination of MIDAS/ADMIDAS are qualitatively similar to the S7/S8 states of αIIβ3 integrin ( 3ze1 . pdb , chain B ) described by Springer and coworkers as an open/active state . [10] These structural observations indicate that the behavior of the wtFN10-integrin complex is much richer than that of hFN10 . It must be noted here that we are not describing the opening transition of integrin , but we are observing the possibility for the complex with wtFN10 to explore a diverse range of local interaction networks and conformational states of specific residues that are compatible with different activation states described by Springer and coworkers [10] , albeit for a different yet related system . Such considerations are consistent with the initial hypothesis that integrin may react to binding a protein ligand primarily via a fine-tuned reorganization of intra-protein interaction networks and dynamic states , which can eventually be connected to the onset of large scale motions . Next , we calculated hydrogen bonds and salt bridges at the interacting surfaces of FN10 and integrin . Apart from aspartic acids from the β-propeller ( D150αv , D218αv ) that coordinate the arginine guanidinium of the RGD motif ( R1493FN ) , novel interactions are established during the simulations that can be used to discriminate the two complexes . Compared to the X-ray starting structure , only during the wtFN10 complex simulations we observed the formation of new salt bridges with the β-propeller residues . Important electrostatic interactions are R1448wtFN-D218αv , R1445wtFN-D218αv , -D150αv and -D148αv and R248αv-E1462wtFN . Two different salt bridges are formed with β subunit of both systems: R1448wtFN-E312β3 and R1445hFN-D251β3 ( S2 Table ) . In wtFN10 simulation , the crystallographic Y122β3 backbone bond with the Oδ2 oxygen of Asp ( RGD ) is transient and it is lost in the first frames of conformational rearrangement . Thus an outward movement of the tyrosine exposes it to the solvent . In contrast , in hFN10 the indole moiety of the W flanking residue of the RGD-optimized sequence -PRGDWNEG- freezes Y122β3 at the top of α1 helix via hydrophobic packing as already illustrated ( Fig 5 ) . Notably , the hydrogen bond between Asp ( RGD ) and Y122β3 ( and consequently the β1-α1 loop ) seems to play a critical role in regulating the lifetime of the principal RGD-αvβ3 bond ( Asp-MIDAS ) by shielding it from free water molecules , as already reported by Vogel and collaborators . [21] Consistently , previous docking studies have conferred a critical role to Y122β3 in the ligand-receptor recognition process . [36 , 37] Another important interaction between hFN10 and β3 concerns D251β3 . This amino acid stably forms a salt-bridge with R1445hFN . During the simulation of the wtFN10 system D251β3 does not form any H-bonds or salt bridges with the receptor and the same arginine can be engaged by different amino acids from the αv domain in the wtFN10 , e . g . D218αv-R1445wtFN ( see S2 Table and S9 Fig ) . Due to its nature and position , such interaction ( alternating between α and β chain ) is crucial to stabilize the orientation of the fibronectin onto the integrin ectodomain . Not surprisingly , the N215β3-D1495FN ( RGD ) hydrogen bond ( one of the crystallographic interactions ) is the only conserved contact displayed by all replicas , and in hFN10 it is also stably engaged by the other carboxylate oxygen of the aspartic acid of the RGD . Such interaction is observed in approximately 23% of the total simulation time ( 1 , 5 μs ) in the wtFN10 whereas it is present in the 38% for the hFN10 . This observation can be related to the swing-out mechanism of the hybrid domain . The RGD coordination of this asparagine , located in the loop connecting helices α2 and α3 , is incompatible with the α1-β1 motion and then with the hinge opening . Recently , similar conclusions were reported for not-inducing swing-out of small isoDGR containing peptides . [24] Next , we analyzed the structural responses of the MIDAS and ADMIDAS binding sites to the presence of either FN10 form . Experimentally , the difference between open and closed conformation at the βA-hybrid domain interface is translated into a ~3 Å displacement of the MIDAS and ADMIDAS-coordinating β1-α1 loop of the βA domain , which alters affinity for RGD-partner binding by ~1 , 000-fold in αIIbβ3 . [10] ADMIDAS metal ion moves toward the MIDAS metal ion also in α5β1 . [38] These rearrangements are parallel to βA domain opening to a high-affinity state . To this end , we monitored Mn2+ distance at MIDAS and ADMIDAS and correlated it with the β-hybrid opening . In good agreement with experimental observations , we observe smaller MIDAS-ADMIDAS Mn2+ distances for wtFN10 simulations . In particular , shorter Mn2+- Mn2+ spacing coincides with the largest Thigh-Hybrid relative distortion , at long-range distances ( S10a Fig ) . This is not the case of hFN10 where MIDAS-ADMIDAS separation is conserved and settled around a longer distance than in the wild type ( S10b Fig ) . From the cumulative distribution of Mn2+-Mn2+ , it is evident that hFN10 is characterized by a dominant peak centered at 8 . 5 Å , with a queue of less populated bins at higher distances . In the case of wtFN10 , the distribution is wider and with bins of comparable populations spanning a larger set of distances , reaching smaller values ( ~6 Å ) . As we have already commented above , observed local rearrangements do not necessarily associate to global conformational changes ( S3 Fig ) . Overall , analysis of binding networks shows once more that the wtFN10 explores diverse sets of interactions with αvβ3 , favoring the population of different conformational ensembles than the ones determined by hFN10 . Globally , wtFN10 may determine a deviation from the original crystal structure leading to the integrin opening motion linked to activation , while hFN10 stabilizes the complex in the closed conformation .
hFN10 acts as a pure antagonist of αvβ3 and lacks the partial agonism that is often observed in other protein ligands that exploit RGD as a recognition motif , as well as RGD-based peptidomimetics . [3] At the β-propeller-βA interface FN inserts into the binding cleft by orienting its RGD motif to enable Asp ( Oδ1/Oδ2 ) to coordinate the metal ion at MIDAS site . The binding of the recognition triad occurs across the αv and β3 , where the guanidinium of the Arginine ( RGD ) shifts between D218αv and D150αv of the β-propeller domain of the αv subunit . While RGD binding takes place in a similar fashion in the two complexes , dissimilar orientations of the full-length fibronectins , wtFN10 and hFN10 , on the top of the integrin determine different local interaction networks that reverberate in dissimilar structural rearrangements ( Fig 2 and S2 Fig ) : wtFN10 rotates upon the βA domain in the first nanoseconds of the simulations , making several stable novel contacts with superficial amino acids from both integrin chains . In contrast , hFN10 persists in its original orientation for the full trajectory , establishing interactions mainly with the βA lobe . Differential effects are arguably attributable to changes in the sequences flanking the RGD recognition motif . X-ray resolution of hFN10-αvβ3 complex shows a π-π edge-to-face interaction for Y122β3 and W1496hFN . Along the simulation , starting on the top of helix α1 , we observe an increased association of highly bulky and aromatic amino acids , that form a stable and tightly packed hydrophobic core at the interacting surfaces , involving W129β3 and Y122β3 from βA and Y1446hFN and W1496hFN from high affinity fibronectin ( Fig 5 and S6 Fig ) . The crystallographic distance between Y122β3 and W1496hFN centroids is 4 . 9 Å while the interplanar angle θ is 25 . 8° . The relative orientation and distance between these residues is well preserved along the trajectory . Distances between their centroids and the distributions of angles between their correspondent planes ( θ ) can be considered in the range of general hydrophobic π-stacking for aromatic residues ( distance between 4 . 9 and 10 . 4 Å , θ angle between 1 . 2° and 89 . 9° , for stacked and T-shaped arrangements ) . [39–42] In the wtFN10 complex , Y122β3 forms a π-cation interaction with R214β3 of the βA chain and this packing is stably preserved along the simulation ( distance between 4 . 6 and 6 . 9 Å ) . Moreover , corresponding integrin-W129β3 in the wtFN10 complex results very flexible , pointing alternatively towards the solvent or towards the interior of the protein ( see S8 Fig ) . Large changes in position and rotamer of this residue have been extensively discussed and linked to the activation opening described for αIIbβ3 . [10] These data point to the role of the high-affinity RGD sequence in hFN10 in favoring the characteristic orthogonal docking of fibronectin on the top of αvβ3 . The stable orientation and interactions of the hFN10 domain rigidify the whole complex , increasing internal coordination throughout the α and β subunits of the integrin . In this framework , the lack of the bulky tryptophan chain flanking the RGD region in wtFN10 can be considered as the determinant for the conformational re-organization observed in the wtFN10-αvβ3 complex . In our model , such a voluminous amino acid at the surface of hFN10 may prevent large movements and therefore represents the lock of the “activation” reaction , freezing and screening Y122β3 from solvation , in line with previous research . Structural and mutational studies support a critical role for the novel W1496hFN-Y122β3 π-π interaction in 'freezing' the integrin in an inactive conformation . [3] The different interaction networks and microscopic conformational dynamics may represent the molecular determinants for the observed functional differences between the two complexes . Together , these data point to αvβ3 functioning in specific ways determined by the sequences and recognition motifs of endogenous protein ligands . A key question in elucidating integrin activation relates to what determines the onset of specific functionally oriented motions and the ability to predict their consequences: this would allow the distinction of the role of the ligand as an agonist or an antagonist . In our study , we distinguish and characterize opening motions vs . conformational stabilization as a function of differences in the ligand binding sequences: in this frame of thought , the extensive and stable packing determined by the tryptophan side chain in hFN10 acts as a blocker for the initial series of conformational events necessary for integrin activation , namely β3 opening and overall reorganization of domain distances and orientations . In contrast , the absence of tryptophan in wtFN10 determines a different set of contacts that translates into a much more pronounced internal flexibility of the integrin domains paralleled by the ability to explore a wider portion of conformational space , which can ultimately favor integrin activation . Our results also highlight the role of the interaction networks of Y122β3 as the driver controlling conformational changes: this tyrosine is located at the top of the β5 strand that runs across hybrid and βA domains of the β3 subunit and the direct link to RGD-binding ( Fig 5 ) . Indeed , while the RGD binding site ( namely , aspartic acids from the β-propeller—devoted to the electrostatic clamp—and metal ions within the βA-coordinated by Asp ( RGD ) carboxyl oxygen ) is almost identical in the low-affinity and high-affinity form of the integrin , large conformational changes are seen at the hybrid lobe upon activation and , more precisely , at the hinge angle between βA and hybrid domains . [21] In this context , the “hinge opening” is considered as the first step that initiates the global genu-extension of the cytoplasmic tails . In fact , disclosing of the β-tail can only occur after the extension of the hybrid and βA domains , induced by the hinge opening . On these bases , we can consider the mechanism of ligand-controlled ( de ) activation of integrin in the light of allosteric control concepts . The integrin binding pocket is conserved and serves to anchor the incoming ligand . The pocket interactions with different ligand sequences determine the accurate re-positioning of the FN domains: specific residues flanking the common RGD recognition motif in the two FNs modulate interactions with the integrin , supplying the critical foundation that allows the Y122β3 . The residues of either FN10 interacting with Y122β3 act then as the driver residues that control the shifts of integrin population from the inactive ( hFN10-bound ) to the ( partially ) activated state ( wtFN10-bound ) through a specific reorganization of pre-existing interaction networks around Y122β3 . We thus speculate that the extent of reorganization around the integrin binding site may determine the shift towards inactive or active states . The mechanism of stabilization of functional states differs as a function of the sequences of the ligands , and antagonism is determined by the presence of bulky , aromatic moieties flanking RGD that optimally pack in the integrin recognition site and consequently block hinge opening and domain reorganization . Agonism is favored by the absence of such flanking motifs , which allows more conformational freedom and pushes integrin towards the active conformation . Delivering related but independent set of information , PCA and DF analyses can be combined to capture the main determinants of the differential conformational dynamics of the integrin induced by different ligand: the former characterizing large-scale collective motions , the latter identifying the sub-blocks and motifs that sustain the 3D fold organization necessary for activity . In addition , to further support the hypothesis that different interaction patterns at the binding site can trigger a differential global rearrangement of the integrin complex , we applied the linear mutual information method to unveil concerted correlations . This method can detect correlated motion regardless of the relative orientation and includes nonlinear contributions . [44] This analysis ( S11 Fig ) corroborates previous observations indicating that wtFN10 and hFN10 binding determine different dynamic responses of the integrin . In this framework , cross-correlations are markedly increased when hFN10 is bound at the αvβ3 interface . Moreover , correlations diffuse throughout the 3D structure . In the case of wtFN10 , mutual information-based analysis shows that the mechanism for transmission stops at the local level , whereby high coordination appears to entail primarily residues that are proximal to the ligand . In the remainder of the integrin , higher coordination patterns appear to involve intra-domain relationships among residues . In contrast , in the case of hFN10 , high mutual information relationships between residues span the whole complex “overcoming” the subdomains structural organization . Changes in correlations again are in agreement with previously discussed dynamical features observed during MD simulations . Furthermore , it is important to emphasize here that quantitatively sampling the conformational changes at the basis of integrin activation/blockage by unbiased atomistic simulations is extremely demanding . Besides the use of pulling or targeted MD simulations which gave unprecedented insight into the basis of conformational changes [21–23] , we hypothesize that such conformational changes cannot be simply described by one single reaction coordinate or by a combination of a limited number of simple reaction coordinates for metadynamics-type simulations . Indeed , we think that domain rotations and reorganizations should be taken into account . In conclusion , the type of fine atomistic interactions and their action on microscopic conformational changes can help provide a molecular explanation of observed inhibiting vs . activation effects . Interestingly , we have shown that the comparative analysis of a combination of global measures ( overall flexibility , large scale domain rearrangements ) and of local structural environment variations in the recognition regions can reflect the difference between induced active and inactive states . Characterizing the determinants of microscopic dynamics from MD simulations can help rationalize the onset of functional protein motions , that can be linked to experimentally-observed structural and functional modifications . In conclusion , these considerations may be useful in the design of small molecule modulators of the function of αvβ3 integrin function: we propose that it may be possible to realize peptidomimetics containing the RGD sequence in an optimal arrangement for stable binding , whose role as antagonist or agonist can be modulated by the type and stereoelectronic properties of the RGD-flanking groups . By comparing the results of MD simulations with the determinants of integrin modulation induced by different FN sequences , we can investigate the effect of the aromatic substituent at the scaffold nitrogen atoms ( exploring bulky side chains of aromatic amino acids ) and of the recognition sequence ( examining RGD-like motifs ) on αvβ3 integrin internal dynamics . [44–45] The identification of peptidomimetic ligands able to efficiently mimic the behavior of the high affinity fibronectin mutant ( underlying pure antagonism ) could lead to the generation of new compounds that are unable to promote integrin activation and thus can act as pure antagonists . In particular , the presence of large aromatic moieties may aptly block integrin opening , providing a new generation of real antagonists . [46–50] This hypothesis is supported by the recent observation that the modification of the barbourin KGDW sequence ( venom disintegrin ) led to the development of a cyclic hexapeptide ( eptifibatide ) that demonstrated high affinity for αIIbβ3 . [46]
Molecular dynamics simulations were carried out on αvβ3-wtFN10 and αvβ3-hFN10 , and on the unbound form of the integrin . Starting models were retrieved from Protein Data Bank with access number 4MMX , 4MMZ , and 1JV2 , respectively . Integrin extracellular domain was cut at the Thigh domain of the αv , including β-propeller and Thigh domains ( residues 1–599 ) and at the β-hybrid domain of the β3 , including βA and hybrid domains ( residues 55–434 ) ; whereas fibronectin type-III domain 10 comprises residues 1417–1507 . To the best of our knowledge , no previous simulations have been carried out on the β-propeller—Thigh full-length domain of the αv chain . [21 , 51] Wild-type fibronectin ( wtFN10 ) tripeptide RGD-enclosed sequence -GRGDSPAS- is replaced by -PRGDWNEG- in high affinity fibronectin ( hFN10 ) in X-ray structures . Because Mn2+ ions are known to regulate integrin activation and are present under physiological conditions , crystallographic Mn2+ are kept at the three integrin metal ion binding sites as well as at the β-propeller domain . The MD simulation package Amber v12 was used to perform computer simulation applying Amber-ff99SB*ildn force field . [52] The two systems were solvated , in a simulation box of explicit water molecules ( TIP3P model ) , [53] counter ions were added to neutralize the system and periodic boundary conditions imposed in the three dimensions . Mn2+ ions were modeled based on hydration free energy parametrization derived from Musco et al . [24] Final simulated systems are made of ~ 200 000 atoms . After minimizations , systems were subjected to an equilibration phase where water molecules and protein heavy atoms were position restrained , then unrestrained systems were simulated for a total of 3 microseconds , in a NPT ensemble; Langevin equilibration scheme and Berendsen thermostat were used to keep constant temperature ( 300 K ) and pressure ( 1 atm ) , respectively . Electrostatic forces were evaluated by Particle Mesh Ewald method [54] and Lennard-Jones forces by a cutoff of 8 Å . All bonds involving hydrogen atoms were constrained using the SHAKE algorithm . [55] To enhance sampling three independent replicas of 500 ns ( 3*0 . 5 μs = 1 . 5 μs ) were run for each system with different initial velocities . Figures are rendered using VMD . [56] We computed distance fluctuations , DFij , along simulations to assess the intrinsic flexibility of proteins . Given rij the distance between Cα atoms of residues i and j: DFij= < ( rij−<rij> ) 2> distance fluctuation , DFij , is defined as the time-dependent mean square fluctuation of the distance rij , where the brackets indicate the time-average over the trajectory . DF is calculated for any pair of Cα along simulation time . Low DF values indicate highly coordinated residues . [34] Based on the assumption that the major collective modes of fluctuation could be linked to protein function ( Essential Dynamics [30] ) , MD simulations have been analyzed by means of principal components analysis ( PCA ) . This method recovers the modes that produce the greatest contributions to the atomic root mean square deviations in the given dynamic ensemble . Thus large-scale collective motions can be collected , as well as the extreme conformations of the system along the simulation , providing information of time-dependent transitions . Additionally , we carried out linear mutual information as introduced by the Grubmuller group . [43 , 57] See supporting material for details . Definitions of rotation axes and torsion angles around these axes help to quantify conformational changes . Principal axes were determined for wtFN10 and hFN10 β3 integrin domains throughout the simulation time to follow structural transitions . Measurements were obtained by UCSF Chimera package . [31] Protein domains can be determined from the difference in the parameters governing their quasi-rigid body motion , and in particular their rotation vectors . Given a structure , by superimposing each main-chain segment in its initial conformation onto its final conformation by least-squares best fitting , we can define the displacement vectors representing the rigid body displacement of the segment . A K-means clustering algorithm is used to identify dynamic domains from clusters of rotation vectors corresponding to main-chain segments . The program DynDom takes two conformations , the initial and the final state , and determines dynamic domains on the basis of the conformational change . [32–33] Cluster analysis was performed over the region of α1 ( βA ) including W129 ( aa . M124-I131 ) of the β3 chain , using Gromos method for clustering , described by Daura et al . [58]: To find clusters of structures in a trajectory , the RMSD of atom positions between all pairs of structures is determined . For each structure the number of other structures for which the RMSD is 0 . 2 nm or less is calculated . The structure with the highest number of neighbors is taken as the center of a cluster , and forms together with all its neighbors a ( first ) cluster . The structures of this cluster were thereafter eliminated from the pool of structures . The process was repeated until the pool of structures was empty . In this way , a series of non-overlapping clusters of structures was obtained . Central structure of each cluster is provided . 5000 time frames per replica ( 15000 per system ) are used in the calculation .
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Interactions between proteins are at the basis of all biological processes in the cell . In this context , the study of conformational responses of protein receptors to the binding of endogenous ligands may be a source of inspiration for the design of small molecule modulators that permit to control such biological processes . To progress towards this goal , we need to unravel the molecular determinants that underlie the correlations between sequences , structures and the onset of functional motions in the receptor , eventually illuminating the links between the fine atomic-scale protein-ligand interactions and the large-scale protein motions . Herein , we have concentrated on the multidomain receptor αvβ3 integrin bound to two different sequences of the endogenous ligand fibronectin: the wild type one , wtFN10 , which acts as an agonist activating the receptor , and a high affinity mutant , hFN10 , which acts as a true antagonist inhibiting the receptor . Through the comparative analysis of several dynamic descriptors at different levels of resolution , from the residue to domain level , we shed light on the salient conformational dynamics differences determined by fibronectin sequence mutations: we show that it is possible to identify interaction hotspots in the integrin binding site that specifically respond to the fibronectin sequence variations , and allosterically drive conformational changes towards integrin activation ( in the case of wtFN10 binding ) or inhibition ( hFN10 binding ) . Finally we propose an allosteric model of integrin regulation that can be used in the design of small molecule integrin inhibitors or modulators .
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2017
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High Affinity vs. Native Fibronectin in the Modulation of αvβ3 Integrin Conformational Dynamics: Insights from Computational Analyses and Implications for Molecular Design
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Leptospirosis is a worldwide prevalent zoonosis and chronic kidney disease ( CKD ) is a leading global disease burden . Because of pathophysiological changes in the kidney , it has been suggested that these conditions may be associated . However , the extent of this interaction has not been synthetized . We aimed to systematically review and critically appraise the evidence on the association between leptospirosis and CKD . Observational studies with a control group were selected . Leptospirosis , confirmed with laboratory methods , and CKD also based on a laboratory assessment , were the exposures and outcomes of interest . The search was conducted in EMBASE , MEDLINE , Global Health , Scopus and Web of Science . Studies selected for qualitative synthesis were assessed for risk of bias following the Newcastle-Ottawa Scale . 5 , 981 reports were screened , and 2 ( n = 3 , 534 ) were included for qualitative synthesis . The studies were conducted in Taiwan and Nicaragua; these reported cross-sectional and longitudinal estimates . In the general population , the mean estimated glomerular filtration rate ( eGFR ) was lower ( p<0 . 001 ) in people testing positive for antileptospira antibodies ( eGFR = 98 . 3 ) than in negative controls ( eGFR = 100 . 8 ) . Among sugarcane applicants with high creatinine , those who were seropositive had lower eGFR ( mean difference: -10 . 08 ) . In a prospective analysis , people with high antileptospira antibodies titer at baseline and follow-up , had worse eGFR ( p<0 . 05 ) . Although the available evidence suggests there may be a positive association between leptospirosis and CKD , whereby leptospirosis could be a risk factor for CKD , it is still premature to draw conclusions . There is an urgent need for research on this association .
Globally , the incidence , mortality and disability due to chronic kidney disease ( CKD ) have increased , mainly driven by established risk factors such as diabetes and hypertension . [1 , 2] Despite the growing body of evidence on CKD , those cases that are not related to well-known risk factors , i . e . , CKD of unknown origin ( CKDu ) , have been less systematically studied and their risk factors have not been clearly identified . [3] A recent systematic review , which only focused on a limited geographical area ( Mesoamerica ) but it is the only one which has conducted a formal risk of bias assessment and meta-analysis , identified that male sex , family history of CKD and low altitude were positively associated with CKD ( defined as estimated glomerular filtration rate ( eGFR ) <60 mL/min/1 . 73m2 ) . [3] Moreover , the authors signalled that there was insufficient evidence to draw strong conclusions about other risk factors . [3] However , this work did not include search terms regarding infectious diseases that may be associated with CKD . This could be the case of leptospirosis , [4 , 5] which impact in acute kidney injury has been well documented , [6] though has also been labelled as an emerging risk factor for CKDu . [7] Because to the best of our knowledge previous reviews have not focused on this potentially new risk factor–leptospirosis–either , [8–10] we aimed to ascertain the association between leptospirosis and CKD . We conducted a systematic review and critical appraisal of the literature . In so doing , we have summarized the available epidemiological evidence providing further insights on the strength of this emerging association and signalling potential research gaps to better understand the role of leptospirosis on CKD .
This is a systematic review and critical appraisal of the scientific literature . This work aimed to answer the research question: is leptospirosis an associated factor or a risk factor for CKD ? This work followed the PRISMA guidelines ( S1 Checklist ) and was registered at PROSPERO ( CRD42018111229 ) . [11] The population of interest included men and women of any age and geographical location; also , the study population could have included population-based samples , occupational studies or hospital-based samples . No intervention of interest was studied . The comparison group should have included people free of leptospirosis at the time of kidney assessment or at the baseline assessment for prospective longitudinal studies . The outcome of interest was reduced kidney function as per eGFR , [12] high serum creatinine or chronic kidney disease of unknown origin . Studies were selected if the exposure ( independent variable ) was leptospirosis diagnosis; cases should have had biological ( laboratory-based ) confirmation . Studies were selected if they had followed an observational design of any kind , namely cross-sectional , case-control or cohort studies . Only original investigations were included , i . e . , case reports , editorials , letters , reviews or simulation studies were excluded; in addition , any other descriptive studies where no comparison group was analysed were excluded . Finally , studies should have analysed data at the individual level , i . e . , ecological studies were not included . Reports were excluded if they only studied people with established risk factors for impaired kidney health: diabetes ( of any kind ) , hypertension and glomerulonephritis . Animal model studies were excluded as well . The search was conducted in Ovid , including EMBASE , MEDLINE and Global Health; we also searched Scopus and Web of Science . These were searched from inception without language restrictions . The terms used in these search engines are showed in S1 Text . The search was conducted on September 27th , 2018 . Search results were downloaded , and duplicates were dropped . Titles and abstracts were screened by two independent reviewers ( RMC-L , JGA-R , CA-F and KO-A ) following the criteria above detailed . Results on which both reviewers agreed that should be included , as well as those results on which the reviewers disagreed , were selected for full-text examination . The full text of the selected reports was sought and studied by two independent reviewers ( RMC-L , JGA-R , CA-F and KO-A ) following the same criteria; if there were discrepancies between reviewers these were solved by consensus among them . Both selection phases were conducted using the online tool Rayyan . [13] The reviewers developed a data extraction form which was not modified during the data collection process . This was an Excel sheet containing relevant information to answer the research question , including: ascertainment methods of the exposure and outcome of interest , levels of biomarkers of kidney function , and association estimates between leptospirosis and kidney biomarkers . A positive association between leptospirosis and CKD implied that the former was a risk factor for the latter; similarly , a negative association between leptospirosis and eGFR implied that higher leptospirosis infection ( e . g . , serum titers ) was associated with lower eGFR thus leptospirosis was a risk factor for CKD . Data extraction was conducted by one reviewer ( RMC-L ) and independently verified by another reviewer ( JGA-R ) . Along with data extraction , risk of bias of individual studies was assessed following the Newcastle-Ottawa Scale ( NOS ) . [14] This process was conducted by one reviewer ( CA-F ) and independently verified by another reviewer ( RMC-L ) . Because there were few results and large heterogeneity among them , a quantitative synthesis ( e . g . , meta-analysis ) was not conducted . Results are summarized qualitatively , and where relevant , association estimates were summarized . No human subjects participated in this study . Therefore , it was considered of minimal risk and no approval was sought from an ethics committee .
The search yielded 5 , 981 results ( 50 from Embase , Medline and Global Health; 5 , 556 from Scopus; and 375 from Web of Science ) , and after duplicates were removed 5 , 888 titles and abstracts were screened . After this screening process , 27 reports were studied in detail , two of which were selected for qualitative synthesis ( S1 Fig ) . Details about the excluded studies are presented in S1 Text . The selected studies were published in the last three years , [15 , 16] and conducted in different world regions: Nicaragua ( Riefkohl et al . [15] ) and Taiwan ( Yang et al . [16] ) . One report yielded cross-sectional results , [15] while the other one presented both cross-sectional and longitudinal estimates . [16] In total , these studies included 3 , 534 people . [15 , 16] Riefkohl et al . included people based on their job ( e . g . , sugarcane workers or sugarcane applicants ) . [15] Table 1 presents additional details about the study populations . Both studies objectively assessed the exposure and outcome of interest using blood and urine samples ( Table 2 ) . [15 , 16] Furthermore , both studies analysed more sophisticated biomarkers than creatinine alone , these included: neutrophil gelatinase-associated lipocalin ( NGAL ) ; kidney injury molecule–1 creatinine ratio ( KIM–1/Cr ) ; monocyte chemoattractant protein–1 ( MCP–1 ) ; interleukin-18 ( IL-18 ) ; and N-acetyl-D-Glucosaminidase ( NAG ) . [15 , 16] Yang el al . reported that 1 , 034 ( out of 3 , 045 ) people were positive for antileptospira antibody;[16] in addition , in the follow-up subsample , 88 . 4% were positive at baseline . On the other hand , Riefkohl et al . reported that 29 . 0% of the study population had microscopic agglutination test ( MAT ) equal or greater than 100 , i . e . , suggesting a positive case of leptospirosis . [15] In a cross-sectional analysis Yang and colleagues showed that the mean eGFR was lower in people positive for antileptospira antibodies in comparison to their negative counterparts ( p<0 . 001 ) : eGFR = 98 . 3 ( SD: 0 . 4 ) ml/min/1 . 73m2 vs eGFR = 100 . 8 ( SD: 0 . 6 ) ml/min/1 . 73m2 . [16] Moreover , they reported that being seropositive for Leptospira was associated with four ( univariate ) and three ( multivariate ) less eGFR units , in comparison to those who were negative for antileptospira antibody; of note , the multivariable model accounted for seventeen potential confounders including diabetes and hypertension , established risk factors for impaired kidney health . This preliminary evidence already suggest that leptospirosis may be associated with impaired kidney function , regardless other relevant risk factors . Further analysis in people with diabetes and in individuals without diabetes showed that the effect of seropositive Leptospira was stronger among the former than the latter group . Yang et al . also conducted a two-year follow-up finding that , among people who had antileptospira antibody titer equal or greater than 400 at both time points , i . e . , at baseline and at follow-up , the eGFR was lower at follow-up ( p<0 . 05 ) . [16] No strong difference was retrieved in people whose titer were equal or greater than 400 at baseline and at follow-up their titer were zero or between 100 and 200 . [16] In a cross-sectional endeavour , Riefkohl’s team reported that among sugarcane applicants with elevated creatinine , those who were seropositive for antileptospira antibodies had lower mean eGFR ( mean difference: -10 . 08 , 95% CI: -24 . 12; 3 . 96 ) than seronegative subjects . Further details about these results , and main findings regarding other biomarkers of kidney health , are depicted in Table 2 . The risk of bias assessment is shown in Table 3 , and further details are available in S1 Text . The Riefkohl’s paper was deemed to have serious risk of bias in the selection and comparability domains .
This qualitative systematic review of the literature found two observational studies addressing the association between leptospirosis and CKD . [15 , 16] These reports suggest that leptospirosis may be associated with impaired kidney health , as per eGFR and other highly-sensitive kidney biomarkers ( e . g . , KIM–1/Cr and NGAL ) . [15 , 16] In addition , a two-year follow-up effort including 88 individuals found that sustained high antileptospira antibodies titer was associated with worse eGFR . [16] Certain jobs may be associated with impaired kidney health , and Riefkohl et al reported lower eGFR in sugarcane applicants with positive antileptospira antibody titer . [15] The findings of this review show there is a non-negligible dearth of evidence about this association; nevertheless , after accounting for their limitations , the available evidence already suggests there may be a positive association between leptospirosis and CKD whereby leptospirosis is associated with higher odds and risk of CKD . This observation deserves further attention from the clinical and epidemiological community . This is a comprehensive literature review including five search engines which cover several world regions . Although our search covered relevant veterinary or zoonosis sources , we did not search any specific search engine of these disciplines which could have retrieved extra results . However , we argue this is a minor limitation because these information sources would have focused on other aspects of leptospirosis rather than on their impact on human health or clinically relevant outcomes such as kidney function . Although Yang et al . studied a fairly large sample size , the two-year follow-up results only included 88 people . [16] This small sample size compromises the validity and extrapolation of their findings . In this line , the fact that Riefkohl et al . ’s study population was selected based on their jobs , prevents their findings to be extrapolated to the general population too , i . e . , there could have been selection bias . [15] These limitations urgently call to conduct larger and longer research efforts to better understand the true association between leptospirosis and CKD . Even though both studies reported relevant and promising results , they did not properly account for sources of confounding bias and missing data . [15 , 16] This also invites the research community to design stronger studies to address the association of interest and to analyse the results following comprehensive methods and techniques to account for missing observations and confounding factors ( e . g . occupational exposure or comorbidities such as diabetes mellitus and hypertension ) . In addition , we invite infectious diseases researchers to also consider risk factors for non-communicable diseases when conducting research or statistical analysis . [17] It has been proposed that leptospirosis may be a risk factor for CKD through two different pathophysiological pathways . [7] Acute kidney injury is a well-known complication of leptospirosis[6] , which if not treated promptly could progress to CKD . Therefore , the occurrence of acute kidney injury during leptospirosis infection could signal higher CKD risk in these patients . [18 , 19] After recovery of the acute infection , some patients might persistently carry leptospirosis in the kidney , which added to other factors such as extreme heat and dehydration , could exacerbate the kidney injury and lead to CKD . [15] Animal models have shown that chronic Leptospira infection results in tubulointerstitial nephritis and interstitial fibrosis . [20] The proteins of Leptospira outer membrane provoke inflammation and tubular damage through activation of Toll-like receptors and factor-beta/Smad-associated fibrosis pathway . Toll-like receptors trigger a cascade that ends with activation of nuclear transcription factor kappa B and mitogen-activated protein kinases . [21–23] These changes would lead to irreversible kidney damage , i . e . , CKD . To the best of our knowledge this is the first systematic review to ascertain the association between leptospirosis and CKD . Although the results support there may be a positive association between these illnesses , the epidemiological evidence is still weak and deserves additional and more comprehensive studies . Studies randomly selecting subjects from the general population , specifically in areas of high endemicity of leptospirosis , are needed to assess the strength of the association of interest . In this line , prospective cohort studies are very much needed so that preliminary evidence on causality is available . Adequate analytical methods , such as causal inference techniques , could be applied and we encourage clinicians and epidemiologist to work together on these endeavours . From a basic science and immunology point of view , a better characterization of the involved serovars seems relevant . Yang et al . only tested for one serovar ( Leptospira santarosai serovar Shermani ) and found strong and even prospective evidence about a possible positive association between leptospirosis and CKD . [16] On the other hand , Riefkohl et al . tested for several serovars and found that the most common ones were Bratislava and Canicola , among others . [15] These findings may imply that the negative effect of leptospirosis on kidney function exists regardless of the serovar , at least among the ones already studied . Whether this premature conclusion is true , and whether a specific serovar has a larger effect , remains unknown . It has been suggested that Leptospira may asymptomatically colonize the human kidney . [24] If in fact leptospirosis leads to CKD , or it is at least partially associated , new research projects could try to identify who with an asymptomatically colonization might have diminished kidney function in the future . This would also imply detecting where asymptomatically colonization is possible . This may not be a static task because due to climate change , migration or poor sanitation , one may find leptospirosis where previously there were not any cases . We encourage human and veterinary epidemiologists to work on these pending tasks . There is a serious dearth of evidence to accurately assess the association between leptospirosis and CKD . Although still premature , available observational evidence following cross-sectional and prospective designs suggest there may be an association between these conditions , whereby leptospirosis could be a potential risk factor for CKD . Given the relevance of these pathologies , leptospirosis as a worldwide-spread zoonosis and CKD as a major health and disability burden , [1 , 25 , 26] their association should be further studied to better understand their interaction and find new prevention avenues . The new knowledge could guide prevention strategies and explain CKD in the absence of other established risk factors .
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Leptospirosis is an infection that can affect the kidneys acutely , though it seems that even after the acute infection there could be risk of a long-term impaired kidney function . The evidence on this matter is sparse and limited , thus the need to comprehensively seek , synthetize and appraise the available scientific literature . In so doing , this work has found preliminary evidence that leptospirosis may be associated with impaired kidney function as per eGFR . This work and findings strongly reveal that more research is needed to quantify and characterize the long-term risk of CKD among those who had had leptospirosis infection . The raising burden of non-communicable diseases paired with a still non-negligible burden of communicable and neglected tropical diseases in low- and middle-income countries , deserve the synergism of these two broad fields for the benefit of patients and population health .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"chronic",
"kidney",
"disease",
"tropical",
"diseases",
"biomarkers",
"bacterial",
"diseases",
"crops",
"neglected",
"tropical",
"diseases",
"medical",
"risk",
"factors",
"kidneys",
"plants",
"veterinary",
"science",
"research",
"and",
"analysis",
"methods",
"sugarcane",
"infectious",
"diseases",
"veterinary",
"diseases",
"zoonoses",
"grasses",
"crop",
"science",
"epidemiology",
"research",
"assessment",
"creatinine",
"leptospirosis",
"agriculture",
"biochemistry",
"eukaryota",
"anatomy",
"systematic",
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"biology",
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"life",
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"system",
"nephrology",
"organisms"
] |
2019
|
Leptospirosis as a risk factor for chronic kidney disease: A systematic review of observational studies
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Many persistent viral infections are characterized by a hypofunctional T cell response and the upregulation of negative immune regulators . These events occur days after the initiation of infection . However , the very early host-virus interactions that determine the establishment of viral persistence remain poorly uncharacterized . Here we show that to establish persistence , LCMV must counteract an innate anti-viral immune response within eight hours after infection . While the virus triggers cytoplasmic RNA sensing pathways soon after infection , LCMV counteracts this pathway through a rapid increase in viral titers leading to a dysfunctional immune response characterized by a high cytokine and chemokine expression profile . This altered immune environment allows for viral replication in the splenic white pulp as well as infection of immune cells essential to an effective anti-viral immune response . Our findings illustrate how early events during infection critically dictate the characteristics of the immune response to infection and facilitate either virus control and clearance or persistence .
The innate antiviral immune response is primarily triggered by recognition of virally derived molecules , a . k . a . pathogen associated molecular patterns ( PAMPs ) , by host cell pathogen recognition receptors ( PRR ) , resulting in the induction of type-I interferons ( IFN-I ) , a group of molecules that exhibit potent anti-viral properties and also contribute to the expansion and survival of specific anti-viral cytotoxic T lymphocytes [1]–[4] . Accordingly , viruses have evolved a plethora of mechanisms to counteract the induction of IFN-I and downstream events triggered by IFN-I signaling [5]–[9] , which often play critical roles in virulence [8] , [10]–[13] . Similar to many other viruses , although LCMV infection induces a strong IFN-I response , it also encodes proteins that counteract the induction of IFN-I [14]–[17] . Notably , we [18] and others [19] have recently reported that , unexpectedly , IFN-I induced early during infection of mice with the immunosuppressive strain clone 13 ( Cl13 ) of LCMV plays a critical role in the establishment of Cl13 persistence . These findings illustrate how IFN-I can both hamper and promote virus infection . Thus , in the case of LCMV , although IFN-I is important in induction and maintenance of a persistent viral infection [18] , [19] , early IFN-I induction has been shown to decrease viral titers during the first few days of infection [20] , [21] and mice lacking the type-I IFN receptor never clear a persistent infection . LCMV is an enveloped virus containing a bi-segmented , negative strand RNA genome that encodes for four proteins [22]–[24] . The virus nucleoprotein ( NP ) binds to viral RNA to form the nucleocapsid and associates with the virus polymerase ( L protein ) to form the virus ribonucleoprotein ( RNP ) complex that directs virus RNA replication and gene transcription [25] , [26] . NP has also been shown to be responsible for the anti-interferon activity of LCMV [27] . The glycoprotein is expressed as a single polypeptide ( GPC ) that is rapidly cleaved into GP1 , GP2 and a stable signal peptide which form a complex at the virus surface that mediates virus receptor recognition and cell entry [28]–[30] . LCMV encodes also a small RING finger protein ( Z ) that is a bona fide functional matrix protein and driving force of arenavirus budding [31]–[33] . To investigate differences driving events leading to either acute or persistent viral infection , we used infection of mice with Armstrong ( Arm ) and Cl13 strains of LCMV , which are genetically closely related and share identical CD8+ and CD4+ T cell epitopes but exhibit drastic different phenotypes in their ability to establish persistence . In adult immunocompetent mice Arm causes an acute infection , while Cl13 establishes a persistent one [34] . Genetic and biochemical analysis revealed that only two amino acid differences between these strains of the total 3 , 356 amino acids encoded by the virus are required for the Cl13-induced persistent infection [35]–[37] . A leucine at position 260 within GP1 allows for a strong binding to the cognate LCMV receptor , α-dystroglycan ( αDG ) [36] , [38]–[40] , while a glutamine at position 1079 in the viral polymerase allows for faster and more robust multiplication in vivo in selected cell types such as dendritic cells ( DCs ) and macrophages [37] , [41] , [42] . Thus , compared to Arm , Cl13 exhibits a more robust multiplication in plasmacytoid DCs ( pDCs ) [37] , [43] , conventional DCs ( cDCs ) [39] , [42] , [44] , macrophages [41] and fibroblastic reticular cells ( FRCs ) [45] , [46] , all cell types essential for establishing an anti-viral immune response required to control and terminate an acute infection . Early infection of large numbers of pDCs by Cl13 [18] , [37] , [43] , leads to its multiplication in the white pulp [39] , [47] , disruption of dendritic cell ( DC ) function [48] , [49] , disorganization of splenic architecture [50]–[52] , upregulation of negative immune regulators [44] , [53]–[56] and dysfunctional cytotoxic [57] and helper T cells [58]–[60] necessary for creating and maintaining an immune environment in which Cl13 can persist . To better understand the basis of how early events dictate the course of whether an infection becomes acute or persistent , we asked 1 ) how the non-persistent Arm and persistent Cl13 strains of LCMV affect the early induction of the IFN-I response , and 2 ) how early induction of the IFN-I response affect subsequent events during infection with Arm or Cl13 . Our results reported here show that LCMV persistence is dependent on an early dysregulated innate immune response that is associated with rapid viral proliferation . These early events are essential for Cl13 invasion into the splenic white pulp and infection and replication in selected cell types that lead to suppression of the T cell response . On the other hand , an immune response characterized by a subdued cytokine and chemokine signature in the serum and a decreased rate of LCMV multiplication early during infection prevent the establishment of LCMV persistence .
Murine infection with immunosuppressive strains of LCMV , such as Cl13 , result in higher infection of , and multiplication within , pDCs when compared to mice infected with LCMV strains , such as Arm , that cause acute viral infection terminated by a robust T cell response [18] , [36] , [37] , [39] , [43] . To examine whether one of these two phenotypes was dominant over the other , we injected mice with a dose of Cl13 ( 2×106 i . v . ) that causes a persistent infection in adult immunocompetent mice . Concurrently , these mice also received the same dose of Arm . Mice that received both Cl13 and Arm simultaneously did not clear early and had similar serum titers compared to mice receiving PBS and Cl13 ( mock primed ) ( Fig . 1A ) . These data indicate that the persistent phenotype of Cl13 was dominant over the acute phenotype of Arm . To determine if Arm was able to trigger an immune response capable of blocking the establishment of Cl13 persistence , we primed mice first with Arm before Cl13 infection . Mice primed with Arm ( 2×106 i . v . ) twelve hours prior to Cl13 infection ( 2×106 i . v . ) , cleared infection in two weeks despite similar serum titers at day 5 post-Cl13 infection ( Fig . 1A ) . When six hours separated the priming ( Arm ) dose and Cl13 infection , 40% of mice cleared the infection demonstrating that Cl13 must be able to modify the host immune response early after infection in order to establish a persistent infection in its host . To understand how Arm triggers a primed immune response that facilitated control and clearance of a subsequent infection with Cl13 , we used a non-propagating Arm for priming prior to Cl13 infection . This non-propagating recombinant LCMV has the gene for the enhanced green fluorescent protein ( EGFP ) in place of the gene encoding GPC ( ArmΔGPC ) . Trans-complementation of ArmΔGPC with GPC of Arm results in a single-cycle infectious virus that can infect and replicate in infected cells , but cells infected with this virus cannot produce infectious progeny viruses . Mice primed with ArmΔGPC cleared Cl13 infection within two weeks if the priming dose was administered at least 8 hours prior to Cl13 infection ( Fig . 1B ) . One mouse ( of five ) cleared the infection when primed four hours before Cl13 infection ( Fig . 1B , right panel ) . Mice primed with ArmΔGPC up to ten hours before Cl13 infection had similar viral titers at day 5 post-infection whether or not they later cleared the infection . When mice were primed at 12 hours before Cl13 infection , viral titers at day 5 were lower than those of mock primed mice ( Fig . 1B ) . Adult immunocompetent mice infected with Cl13 never clear virus from their kidneys despite undetectable viral titers from all other organs and serum four months post-infection [61] . To assess whether Cl13 had completely cleared from ArmΔGPC primed mice infected with Cl13 , we plaqued tissue homogenates from the spleens , livers , lungs , kidneys , hearts , and brains of these animals 18 days post-infection . We did not detect the presence of virus ( <200 pfu/gram ) in any of these organs . Mice primed with a non-propagating Cl13 virus also cleared the infection suggesting that neither tropism nor differences in Cl13 viral gene expression were responsible for a primed-mediated clearance of Cl13 ( Fig . 1C ) . When the priming agent ( either Arm or ArmΔGPC ) was UV-inactivated , thereby unable to express viral genes or replicate , Cl13 was able to persist in mice ( Fig . 1D – E ) . Altogether , these data demonstrate that the priming agent must be able to either express viral genes or replicate , or both , to trigger an immune response capable of clearing a Cl13 infection . Furthermore , viral propagation was not necessary to generate this response as priming with ArmΔGPC induced an immune response that efficiently cleared Cl13 . Infection with a lower dose of Cl13 ( 1×105 i . v . ) does not lead to persistent infection but causes weight loss and a death for less than 25% of infected mice [62] . Priming of mice with a high dose ( 2×106 i . v . ) of ArmΔGPC 12 hours prior to a lower dose ( 1×105 i . v . ) Cl13 infection prevented weight loss and mortality . Mock primed mice ( injected with a similar volume of PBS ) lost nearly 20% of their original weight ( Fig . 1F ) and had a 10 . 5% fatality rate ( 2/19 mice ) . These data demonstrate that priming with ArmΔGPC not only leads to clearance of a high dose Cl13 infection , but also prevents the disease associated with a lower dose Cl13 infection . T cells from adult immunocompetent mice infected with Cl13 have a decreased function ( T cell exhaustion ) that leads to their inability to clear the infection [57] . Viral titers in serum at five days post-infection were similar in mock and ArmΔGPC primed Cl13 infected mice infected with Cl13 ( Fig . 1B , mice primed 8 and 10 hours prior to Cl13 infection ) . We therefore tested whether a cytotoxic T cell ( CTL ) response was responsible for clearance in primed mice . CTL function was assayed by incubating splenocytes isolated from mock or ArmΔGPC primed C57Bl6/J ( H-2b ) mice with immunodominant H-2Db-restricted LCMV-derived peptide epitopes GP33–41 and NP396–404 , which are conserved between Arm and Cl13 , and assessing intracellular expression of interferon-γ ( IFNγ ) and tumor necrosis factor-α ( TNFα ) in CD8+ T cells by flow cytometry . We found that CTLs from Cl13 infected mice primed with ArmΔGPC had a robust T cell response to GP33–41 and NP396–404 LCMV-derived peptides comparable to CTLs from mice infected with Arm alone ( Fig . 2A ) . In comparison , significantly fewer CTLs isolated from mock primed Cl13 infected mice expressed IFNγ and TNFα , a phenotye typical of a persistent infection . Only T cells isolated seven days post Cl13 infection and stimulated with GP33 did not show significant differences , however significant differences were seen between these groups at 11 days post Cl13 infection . To further ensure that CD8+ T cells were responsible for viral clearance in this model we conducted depletion experiments . Mice were depleted of CTLs using CD8 specific antibodies , primed with ArmΔGPC and infected with Cl13 12 hours after priming ( Fig . 2B and 2C ) . Mice treated with non-relevant isotype control antibodies cleared a primed Cl13 infection while primed Cl13 infected mice depleted of CTLs were viremic at days 7 and 11 ( Fig . 2B ) confirming that CTLs were responsible for clearing LCMV . CD8+ T cell depletion was very efficient at both days 7 and 11 post Cl13 infection ( Fig . 2C ) . Clearance of Cl13 from ArmΔGPC primed mice was mediated by CTLs , raising the possibility that the introduction of viral antigens ( via ArmΔGPC administration ) 8 hours prior to Cl13 infection led to an 8 hour advantage in T cell generation , and provided enough time to tip the balance in favor of viral clearance . Under this scenario , we would expect a similar , but earlier induction of the innate immune response in primed mice compared to mock primed mice in the first 24 hours after Cl13 infection . To examine this possibility we measured expression levels of a panel of cytokines in serum samples collected at several times after priming and infection . We found that cytokine levels in the serum of Cl13-infected mice varied substantially between primed and mock primed mice . These differences fall roughly into two groups . The first group ( Fig . 3 , top three rows ) was defined by cytokines that were induced in both mock and ArmΔGPC primed mice . Within this group , IFNα , CCL4 , CXCL1 , and CXCL10 were upregulated following ArmΔGPC priming but prior to infection with Cl13 , suggesting that these cytokines were upregulated not only upon Clone 13 infection but also by the priming agent . Overall , for each cytokine , expression was higher at its peak in mock primed compared with ArmΔGPC primed Cl13 infected mice . The second group ( Fig . 3 , bottom 3 rows ) was defined by cytokines that were expressed at significantly lower levels in ArmΔGPC primed compared to mock primed Cl13 infected mice suggesting that priming with ArmΔGPC affected the ability of these cytokines to be upregulated by Cl13 infection . Interestingly , IFNα and IFNβ expression fall into these separate groups . Whereas ArmΔGPC primed and mock primed Cl13 infected mice expressed significant amounts of IFNα which peaked 6–12 hours earlier than in mock primed Cl13 infected mice , only negligible amounts of IFNβ were detected in the serum of primed mice during the first 24 hours following Cl13 infection . This difference between IFNα and IFNβ is similar to what has been observed between Arm and Cl13 infected mice [18] . Importantly , results shown in Fig . 3 indicate that an immune response in mice that clear Cl13 infection within two weeks is characterized by lower overall expression levels of cytokines and chemokines compared to mock primed mice . Conversely , a dysregulated innate immune response early after Cl13 infection is associated with the establishment of viral persistence . In addition , while we have previously observed that in Cl13-infected mice IFN-I signaling was responsible for the upregulation of a wide variety of cytokines and chemokines [18] , in ArmΔGPC primed mice , early IFNα expression is associated with an overall decrease in cytokine and chemokine expression compared to mock primed Cl13 infected mice . We found that ArmΔGPC priming 8–10 hours before Cl13 infection did not affect virus levels in serum of Cl13-infected mice at day 5 post-infection ( Fig . 1A and B ) , but early induction of IFNα in primed mice may have affected viral multiplication at earlier infection times . To examine this , we titered virus from the organs and serum at 24 and 72 hours after Cl13 infection of mock primed mice and mice primed with ArmΔGPC 10 hours prior to Cl13 infection . After 24 hours of infection , we observed a significant decrease ( ∼1 log10 ) in LCMV titers in most organs tested ( Fig . 4 , top panel ) . However , by three days post-infection , titers between both groups were similar ( Fig . 4 , bottom panel ) . At three days post-infection , Cl13 and Arm are largely found within the white and red pulp , respectively , in the spleen [39] , [47] and dissemination of Cl13 in to the white pulp of the spleen has been associated with the ability of Cl13 GPC to interact with the host receptor αDG [38] . Viral titers in total spleen homogenates were similar at three days post-infection in ArmΔGPC and mock primed mice infected with Cl13 ( Fig . 4 ) . To examine possible differences in Cl13 distribution between ArmΔGPC and mock primed mice , we prepared spleen tissue sections from mice that were mock primed and ArmΔGPC primed 10 hours before Cl13 infection at three days post-infection and stained them with fluorescently conjugated antibodies against the viral GPC and MOMA-1 , a marker of metallophilic macrophages found on the inner border of marginal zones adjacent to the white pulp . Viral antigen in spleens from ArmΔGPC primed Cl13 infected mice was found largely bordering the white pulp in marginal zone regions while viral antigen in spleens of mock primed Cl13 infected mice was found in both marginal zones and in the white pulp of the spleen ( Fig . 5 ) . These data show that while ArmΔGPC primed and mock primed Cl13 infected mice had similar viral titers , there were clear differences in the localization of LCMV GPC . Together with data from Fig . 3 , our results strongly suggest that a dysregulated immune response early during Cl13 infection is likely an essential component to the movement of Cl13 out of the marginal zones and into the splenic white pulp . To assess whether cellular tropism differed between mice primed with ArmΔGPC 10 hours before Cl13 and mock primed Cl13 infected mice , we made single cell suspension preparations from spleens of these mice three days after Cl13 infection and quantified the presence of viral NP in various cell types by flow cytometry . Highly significant differences were seen in plasmacytoid DCs ( pDCs ) ( mock primed: 56 . 4±1 . 6; ArmΔGPC primed: 30 . 2±1 . 7 , p = 0 . 00003 ) , macrophages/monocytes ( mock primed: 11 . 4±1 . 6; ArmΔGPC primed: 2 . 1±0 . 6 , p = 0 . 0017 ) and FRCs ( mock primed: 9 . 0±0 . 4; ArmΔGPC primed: 1 . 7±0 . 3 , p = 0 . 000007 ) from primed and mock primed Cl13 infected mice ( Fig . 6A ) . These cell types have been associated with persistent viral infection and essential for a proper anti-viral immune response [41] , [43] , [45] , [46] , [63] , [64] . Additionally , we observed significant differences in T ( mock primed: 1 . 3±0 . 2; ArmΔGPC primed: 0 . 5±0 . 04 , p = 0 . 001 ) and B cells ( mock primed: 2 . 9±0 . 2; ArmΔGPC primed: 0 . 9±0 . 08 , p = 0 . 0001 ) . Higher numbers and percentages ( though low overall ) of infected T and B cells may be due to the increased presence of virus in splenic white pulp ( Fig . 5 ) where a vast majority of T and B cells reside . We observed increased costaining of LCMV NP with F4/80 , a marker of mature macrophages , in mock primed Cl13 infected mice ( Fig . 6B ) three days post-infection . This is coincident with invasion of LCMV into the red pulp , where as GPC staining was restricted mostly to the marginal zones in spleens of ArmΔGPC primed Cl13 infected mice . Additionally , we observed increased costaining of LCMV GPC with ER-TR7 , a marker of stromal cells in secondary lymphoid tissue , particularly FRCs . Notably , some ( but not all ) of the reticular fibroblasts lining the red pulp sinus contained viral antigen as well as some FRCs present in the white pulp in the spleens of mock primed Cl13 infected mice three days post-infection ( Fig . 6C ) . Fig . 6D shows the architecture and antibody staining of spleens from naïve ( uninfected ) mice for comparison . Together , our data show that viral propagation into red and white pulp was coincident with a wider viral cellular tropism . Viral antigen in the spleens of ArmΔGPC primed Cl13 infected mice was primarily limited to areas within and near metallophillic macrophages that typically line the inner portion of the marginal sinus . Mice lacking the IFN-I receptor ( IFNAR1-/- ) failed to clear a primed Cl13 infection ( Fig . 7A ) confirming that the early IFN-I signaling is required for early clearance . Downstream of IFNAR1 engagement with IFN-I , signal transducer and activation of transcription 2 ( STAT2 ) forms a complex with STAT1 and IRF9 to form the transcription factor , ISGF3 . STAT2 null mice also failed to clear a primed Cl13 infection ( Fig . 7B ) . Because pDCs are prodigious producers of type-I IFN , we tested whether mice that express a hypomorphic Slc15a4 ( termed “feeble” ) , a protein essential to IFN-I production in pDCs upon stimulation of TLRs , can clear an ArmΔGPC primed Cl13 infection . The phenotype in feeble mice , is restricted to pDCs and has no effect on conventional DCs [20] , [63] . Feeble mice cleared the primed infection similarly to its wild-type counterparts ( Fig . 7C ) indicating that release of IFN-I from pDCs due to TLR stimulation is not necessary to clear a primed infection . IFN-I is induced upon stimulation of PRR by PAMPs . In the case of LCMV , viral RNA is likely a primary PAMP due to the inability of mice that lack TLR7 , which recognizes ssRNA [65] , [66] , to clear Cl13 infection [67] . However Cl13 infected TLR7-/- mice primed with ArmΔGPC have undetectable viral titers after two weeks of infection ( Fig . 7D ) suggesting that the IFN responsible for clearance was not generated though TLR7 stimulation . Viral RNA can also be sensed by the cytosolic PRRs RIG-I and MDA-5 , both of which signal through the adaptor MAVS/IPS-1 leading to nuclear translocation of IRF3 , upregulation of IRF7 expression and subsequent induction of IFN-I gene expression [9] . MAV/IPS-1-/- mice failed to clear ArmΔGPC primed Cl13 infected mice early ( Fig . 7E ) . Moreover , IRF3 and IRF7 expression was also required for priming mediated clearance ( Fig . 7F ) . These data indicate that IFN induced through cytoplasmic RNA sensing of ArmΔGPC was responsible for clearance of primed Cl13 infected mice . Serum IFNα was measured in Cl13 infected wild-type ( WT ) and MAVS/IPS-1-/- mice . Most of the IFNα present in serum 24 hours post-infection was due to signaling through cytoplasmic RNA sensors ( Fig . 7G ) . Cytoplasmic RNA sensors are found in both hematopoetic and non-hematopoetic cells . To determine the origin of the serum IFNα , we generated bone marrow chimeric mice between MAVS/IPS-1-/- and Ly5 . 1 congenic WT mice . Chimeric mice displayed >95% reconstitution as measured by analyzing the presence of CD45+Ly5 . 1+ and CD45+Ly5 . 1- cells in blood by flow cytometry . Eight weeks after bone marrow transfer , mice were primed with ArmΔGPC twelve hours prior to Cl13 infection . Serum samples were taken at 15 , 24 and 39 hours post-infection and analyzed by ELISA for IFNα . MAVS/IPS-1-/- mice reconstituted with WT bone marrow showed similar levels of serum IFNα as WT mice reconstituted with WT bone marrow ( Fig . H , left panel ) , revealing that MAVS/IPS-1 signaling in hematopoietic cells is responsible for circulating IFNα . Interestingly , we observed a spike of IFNα in the serum of MAVS/IPS-1-/- mice that was not present in other groups . Consistent with previous findings [18] , in the absence of IFN-I , viral titers were significantly higher than those measured in WT controls ( Fig . 7A , 7B and 7F ) . We posited that increased viral titers in these mice would result in higher levels of virally derived TLR agonists that would trigger a more robust TLR response leading to the spike in IFNα seen in this group but not in others . Indeed , when we measured the serum titers of these chimeric mice , viremia was ten-fold higher in MAVS/IPS-1-/- mice reconstituted with MAVS/IPS-1-/- bone marrow at 15 and 24 hours post Cl13 infection compared to any other group . Decreased viremia in these mice at 39 hours post-infection followed the spike of IFNα observed in the serum at 24 hours . Although no IFNα was detected in the serum of WT mice reconstituted with MAVS/IPS-1-/- bone marrow , viral titers were lower in these mice compared to MAVS/IPS-1-/- mice reconstituted with MAVS/IPS-1-/- bone marrow suggesting that non-hematopoietic cells contributed to early viral control even if they did not contribute to measurable IFNα in the serum . Altogether , these data indicate that the majority of IFNα produced early in primed mice responsible for early clearance of Cl13 is produced by hematopoetic cells through triggering of cytoplasmic RNA sensors .
In this paper we report how early events affect the establishment of a persistant infection by comparing the outcome of infection of mice with the Cl13 strain of LCMV under two different immune environments . For this we used a model of LCMV infection where mice were first primed with a non-propagating LCMV ( ArmΔGPC ) , or mock-primed with PBS , to examine how a subsequent infection with the immunosuppressive strain Cl13 was able to overcome the effects of virally induced cytoplasmic RNA sensing in primed mice . Despite encoding a NP capable of counteracting IFN-I induction , the LCMV Arm isolate triggers a potent IFN-I response followed by an effective T cell response that results in viral clearance in lieu of a persistent infection . In contrast , the LCMV Cl13 isolate overcomes this response through rapid and robust multiplication that leads to immune dysregulation through the upregulation of cytokines and chemokines , dissemination from marginal zones , infection of important immune cells , and hyporesponsive T cell response . Such “exhausted” T cells are unable to clear the infection , thereby creating an environment favorable to viral persistence . Notably , some mice primed with the non-persistent Arm strain of LCMV as early as 4–6 hours prior infection with Cl13 were able to control and clear Cl13 while all mice primed after 8 hours prior to infection were able to clear a Cl13 infection . Hence , a narrow temporal window exists between the host's ability to mount an appropriate anti-viral immune response through triggering of cytoplasmic PPR and a dysregulated immune response caused by rapid viral propagation . When inoculated concurrently , Cl13 overcame the innate immune response triggered by cytoplasmic PRRs induced by the priming agent , a response also presumably generated by Cl13 itself . However , if this antiviral innate immune response was triggered as early as 4 hours prior to infection , then Cl13 could not overcome the response in some mice while all mice primed 8 hours prior to infection cleared the infection within two weeks . Under these conditions , viral growth slowed , as evidenced by lower viral titers in serum and organs after 24 hours of infection . Despite the initial comparative decrease in viral titers between primed and unprimed mice , viral titers in primed mice recovered to levels of unprimed mice after 3 days of infection but persistent infection did not occur . This suggests that antigen load is not solely responsible for T cell exhaustion . However , the possibility remains that either a high antigen load earlier than day 3 post-infection or high antigen load in distinct areas of the spleen affects T cell responses . Infection of white pulp area of the spleen , a region where important interactions between cells of the innate and adaptive immune response occur , is a hallmark of persistent LCMV infection [39] , [47] , and eventually leads to the disruption of splenic architecture [50]–[52] . LCMV initially infects the marginal zone and it was unclear what determined movement of LCMV from marginal zones to the white pulp . LCMV isolates that cause persistent infection uniformly bind αDG with 2 to 3 orders higher affinity than isolates that cause acute infection [30] , [39] , [68] , which led to the conclusion that cellular tropism is a main factor mediating the movement of LCMV into the white pulp . However , our results have uncovered a critical contribution of the immune environment in restricting LCMV to marginal zone areas . Therefore , the combination of both cellular tropism through GP-αDG interactions and the immune environment via the increased expression of chemokines , likely facilitates viral invasion into the white pulp and infection of relevant cell types . LCMV NP is a robust inhibitor of cytoplasmic RNA sensing pathways [15]–[17] , [27] , [69] , but published data showed a robust IFN-I induction upon LCMV infection [18] , [21] , [69] . Here we present evidence that cytoplasmic pathogen sensing mediated through the signaling molecule MAVS/IPS-1 plays a critical role in production of IFN-I following infection with LCMV of adult immunocompetent mice . This finding suggests that NP is only partially effective in preventing MAVS/IPS-1-mediated induction of IFN-I by infected cells during LCMV infection . It is also possible that in vivo DCs might sense LCMV-infected cells by a mechanism that is independent of LCMV multiplication in DCs , resulting in production of IFN-I by DCs that are not productively infected [70] , [71] . The anti-IFN-I activity of NP may serve to dampen the IFN-I response to levels that can be overcome by strains of LCMV , like Cl13 , capable of multiplying very rapidly in vivo . The IFN-I response to LCMV infection also has a proviral effect as signaling through IFNAR is necessary for upregulation of negative immune regulators , disruption of splenic architecture and maintaining viral persistence [18] . LCMV Cl13 likely overcomes the initiation of an effective anti-viral immune response not by abrogating the IFN-I response , but by overstimulating the immune response through rapid growth in and near hemotopoetic cells , which are responsible for initiating an appropriate anti-viral immune response through IFN-I signaling . Counterintuitively , we observed that an early induction of IFN-I mediated by ArmΔGPC priming led to the lack of the upregulation of cytokines that are dependent on IFN-I signaling . The rapid increase in viral titers in mock primed Cl13 infected mice likely contributed to immune simulation and dysregulation through continuous triggering of innate sensors . Indeed , we observed an increase in IFN in the absence of cytoplasmic RNA sensing that was coincident with high viremia whereas we did not observe this later induction of IFN-I in infected mice with lower LCMV serum titers . In conclusion , our data indicate that interactions between virus and host within hours of infection dictate the course of a persistent infection . The ability of LCMV to persist depends on counteracting the effects of a programmed early immune response mediated by cytoplasmic RNA sensors likely through rapid viral replication . The resultant dysregulated immune response is critical for LCMV invasion into the white pulp of the spleen and infection of pDCs , macrophages , and FRCs , cells necessary for an effective anti-viral immune response . The better we understand how these early immunological events affect the establishment of a persistent viral infection , the more likely the development of more efficacious vaccines for persistent viral infection and development of anti-viral therapies at various stop points in this cycle .
Husbandry and handling of mice conformed to guidelines set forth by the National Institutes of Health Guide for the Care and Use of Laboratory Animals and the Department of Animal Resources at TSRI . Experiments involving mice described in this manuscript were performed in an AAALAC accredited vivarium at The Scripps Research Institute ( TSRI ) ( Vertebrate Animal Assurance No . A3194-01 ) and were approved by TSRI's Animal Care and Use Committee ( Protocol #09-0098 ) . C57Bl6/J mice were purchased from the Rodent Breeding Colony at the Scripps Research Institute ( TSRI ) . MAVS/IPS-1-/- mice were generously provided by Michael Gale ( University of Washington ) . IRF3-/- and IRF7-/- mice were generously provided by Tadatsugu Taniguchi ( University of Tokyo ) . Mice were infected intravenously with 2×106 focus-forming units ( FFUs ) of virus unless otherwise indicated . Priming of mice with either Arm or ArmΔGPC at 2×106 FFUs occurred 10–12 hours before Cl13 infection unless otherwise indicated . Mock primed mice received an injection of PBS of equal volume and at the same time as the Arm or ArmΔGPC primed mice . Quantification of viremia was conducted by drawing blood from the retro-orbital sinus of mice under isofluorine anesthesia and isolating serum at 6000 rpm for 10 minutes on a tabletop microcentrifuge . Organs were harvested from euthanized mice and homogenized in RPMI containing 10% FBS at a volume that corresponded to the weight of each organ . Viral titers were obtained by plaque assay on Vero cells with either serum or clarified homogenates . Virus stocks were generated through passage on BHK-21 cells . Non-propagating viruses were generated using reverse genetic technology [72] and passaged on BHK-21 cells transfected with plasmids encoding for LCMV GPC . Titers of viral stocks were acquired by fluorescence focus assay as described [73] . UV-inactivation was performed by direct exposure to UV by incubation on a transilluminator ( Ultra-Violet Products , Inc . ) for 30 minutes . Only UV inactivated viral stocks that displayed undetectable viral titers ( <100 ffu/mL ) were used . Vero cells ( African green monkey kidney cells originally acquired from the American Type Culture Collection ∼40 years ago ) were propagated in Eagles MEM ( Gibco ) supplemented with 7% FBS , 100 g/mL penicillin/streptomycin ( Gibco ) , 2 mM L-glutamine ( Gibco ) . BHK-21 cells were grown 10% FBS , 100 g/mL penicillin/streptomycin , 2 mM L-glutamine , 7% tryptose phosphate broth solution ( Sigma ) , and 0 . 56% glucose ( wt/vol ) . The following anti-mouse antibodies were purchased from BD Biosciences: PE CD4 ( RM4 . 5 ) , PE IFNγ , PerCP-Cy5 . 5 CD3 ( 145-2c11 ) , PE Ly6G ( 1A8 ) , and PerCP-Cy5 . 5 NK1 . 1 ( 145-2c11 ) . The following anti-mouse antibodies were purchased from eBioscience: FITC TNFα ( MP6-X722 ) , PE SiglecH ( 440C ) , PerCP-Cy5 . 5 CD11c ( N418 ) , PerCP-Cy5 . 5 Ly6c ( HK1 . 4 ) , e450 and APC CD8a ( 53–6 . 7 ) , e450 B220 ( RA3-6B2 ) , e450 and PE-Cy7 CD90 . 2 ( 53–2 . 1 ) , e450 and PE-Cy7 CD19 ( 1D3 ) , PE-Cy7 CD3 ( 145-2c11 ) , PE-Cy7 CD21/CD35 , PE-Cy7 CD11b ( M1/70 ) , APC and APC-Cy7 CD45 ( 30F11 ) , APC CD31 ( 390 ) , and APC PDCA-1 ( 927 ) . The following anti-mouse antibodies were purchased from BioLegend: PE gp38 ( 8 . 1 . 1 ) , PE-Cy7 CD8a ( 53–6 . 7 ) , and APC F4/80 ( BM8 ) . Anti-LCMV NP antibody ( VL-4 ) was purchased from BioXcell and conjugated to Alexa Fluor 488 using antibody labeling kit from Life Technologies . As described previously [54] , blood was harvested and erythrocyte depleted . Cells were then incubated with H-2b restricted epitopes LCMV GP33–41 and NP396–404 along with 50 U/mL IL-2 . After 1 hour , 1 mg/mL Brefeldin A was added . After an additional 5 hours , cells were harvested , incubated with antibodies against CD16/32 ( Fc block ) , stained with antibodies against CD3ε and CD8α , fixed , permeablized ( BD Cytofix/Cytoperm Kit ) and subsequently stained with fluorescently conjugated antibodies against IFNγ and TNFα . For quantification of splenocytes harboring viral nucleoprotein , spleens were harvested , incubated with 1 mg/mL collagenase D/100 µg DNaseI and dissociated as above . Cells were incubated with antibodies against CD16/32 ( Fc block ) , stained with the indicated antibodies to identify various cell types , fixed , permeablized , and incubated with fluorescently conjugated antibodies against LCMV NP ( VL-4 ) . Flow cyotometry was performed using a LSR II ( Becton-Dickinson ) and subsequent analyzed with FlowJo software ( TreeStar , Inc . ) P values are from t tests calculated using Microsoft Excel . IFNα and IFNβ quantification from serum was performed using VeriKineTM Mouse IFNα and IFNβ ELISA Kits ( R&D Systems ) . Levels of all other cytokines and chemokines were analyzed using 32-Plex multiplex ELISA ( Millipore ) . Spleens were harvested , flash frozen in OCT ( Tissue-Tek ) , and cut into 6–8 µm sections . Sections were fixed in 4% paraformaldehyde , blocked with 10% FBS and stained overnight at 4°C with rat anti-mouse MOMA-1 ( ab51814 ) at 1∶200 , rat anti-mouse ER-TR7 ( ab51824 ) at 1∶200 , or rat anti-mouse F4/80 ( BM8 ) at 1∶200 and guinea pig anti LCMV GP serum ( 1∶1000 ) . Sections were washed and incubated with 1∶200 dilutions of AlexaFluor 488-conjugated anti-guinea pig IgG antibodies ( Invitrogen ) and Alexa Fluor 568 anti-rat IgG ( Invitrogen ) followed by subsequent washes and mounting with medium from Vector Laboratories . Sections were visualized with a Zeiss Axiovert S100 immunofluorescence fitted with an automated xy stage and an Axiocam color digital camera . Mosaic micrographs were taken with a 5x objective and assembled using Axiovision Software ( Zeiss ) . All other images were taken with a 20x objective . Ly5 . 1 congenic C57Bl6/J mice and MAVS/IPS-1-/- mice were γ-irradiatd ( 1000 rads ) and kept on antibiotic feed for 5 weeks . Eight weeks after irradiation , mice were primed with 2×106 ffu ArmΔGPC and infected with 2×106 ffu of Cl13 12 hours later . All graphs were made using GraphPad Prism software with SEM calculated and displayed . Significance was calculated using either GraphPad Prism software or Microsoft Excel as indicated .
|
Lymphocytic Choriomenengitis Virus ( LCMV ) is an important model for the investigation of the pathogenesis of persistent viral infections . As with humans infected with hepatitis C and Human Immunodeficiency Virus-1 , adult mice persistently infected with immunosuppressive strains of LCMV express high levels of negative immune regulators that suppress the adaptive T cell immune response thereby facilitating viral persistence . Unknown , however , is whether and how very early interactions between the virus and the infected host affect the establishment of a persistent infection . Here , we describe host-virus interactions within the first 8–12 hours of infection are critical for establishing a persistent infection . While early induction of an anti-viral type-I interferons is essential for the subsequent adaptive immune response required to clear the virus , LCMV is able to overcome the programmed innate immune response by over-stimulating this response early . This affects not only the rate of viral growth in the host , but also the ability to infect specific immune cells that help shape an effective adaptive immune response . We further describe how and where LCMV is sensed by this early immune response , identify the critical timing of early virus-host interactions that lead to a persistent infection , and identify an early dysregulated immune signature associated with a persistent viral infection . Altogether , these observations are critical to understanding how early virus-host interactions determines the course of infection .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immune",
"evasion",
"viral",
"immune",
"evasion",
"viral",
"persistence",
"and",
"latency",
"viral",
"transmission",
"and",
"infection",
"virology",
"biology",
"and",
"life",
"sciences",
"immunology",
"immune",
"suppression",
"microbiology",
"immune",
"response"
] |
2015
|
Early Virus-Host Interactions Dictate the Course of a Persistent Infection
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Replication of plus-strand RNA viruses depends on recruited host factors that aid several critical steps during replication . Several of the co-opted host factors bind to the viral RNA , which plays multiple roles , including mRNA function , as an assembly platform for the viral replicase ( VRC ) , template for RNA synthesis , and encapsidation during infection . It is likely that remodeling of the viral RNAs and RNA-protein complexes during the switch from one step to another requires RNA helicases . In this paper , we have discovered a second group of cellular RNA helicases , including the eIF4AIII-like yeast Fal1p and the DDX5-like Dbp3p and the orthologous plant AtRH2 and AtRH5 DEAD box helicases , which are co-opted by tombusviruses . Unlike the previously characterized DDX3-like AtRH20/Ded1p helicases that bind to the 3′ terminal promoter region in the viral minus-strand ( − ) RNA , the other class of eIF4AIII-like RNA helicases bind to a different cis-acting element , namely the 5′ proximal RIII ( − ) replication enhancer ( REN ) element in the TBSV ( − ) RNA . We show that the binding of AtRH2 and AtRH5 helicases to the TBSV ( − ) RNA could unwind the dsRNA structure within the RIII ( − ) REN . This unique characteristic allows the eIF4AIII-like helicases to perform novel pro-viral functions involving the RIII ( − ) REN in stimulation of plus-strand ( + ) RNA synthesis . We also show that AtRH2 and AtRH5 helicases are components of the tombusvirus VRCs based on co-purification experiments . We propose that eIF4AIII-like helicases destabilize dsRNA replication intermediate within the RIII ( − ) REN that promotes bringing the 5′ and 3′ terminal ( − ) RNA sequences in close vicinity via long-range RNA-RNA base pairing . This newly formed RNA structure promoted by eIF4AIII helicase together with AtRH20 helicase might facilitate the recycling of the viral replicases for multiple rounds of ( + ) -strand synthesis , thus resulting in asymmetrical viral replication .
Host factors co-opted for replication of plus-stranded ( + ) RNA viruses are critical in each step of the well-orchestrated infection process . After translation of the viral mRNA-sense genomic RNA ( s ) , the viral ( + ) RNA and the viral replication proteins together with host RNA-binding proteins ( RBPs ) are recruited to the site of viral replication in membranous cellular compartments . Ultimately , the process leads to the assembly of the membrane-bound viral replicase complexes ( VRCs ) , followed by the activation of the polymerase function of the viral RNA-dependent RNA polymerase ( RdRp ) , and initiation of complementary RNA synthesis on the viral ( + ) RNA template [1]–[4] . Subsequent ( + ) -strand synthesis in the VRCs takes place in an asymmetric manner , producing excess amounts of ( + ) -strand progeny , which is released from replication to participate in encapsidation , cell-to-cell movement and other viral processes . Although the roles of host factors in facilitating the replication process of ( + ) RNA viruses have been extensively characterized in recent years [1]–[3] , [5]–[11] , our current understanding of the role of cellular RBPs , which constitute one of the largest groups of host factors identified is incomplete [1] , [12] , [13] . The co-opted RBPs likely affect several steps in viral RNA replication , including viral ( + ) RNA recruitment , stabilization of the viral RNA , VRC assembly and viral RNA synthesis . Tomato bushy stunt virus ( TBSV ) is a plant RNA virus with a single ∼4 , 800 nt genomic RNA and has two essential replication proteins , p33 and p92pol , required for TBSV replicon ( rep ) RNA replication in yeast ( Saccharomyces cerevisiae ) model host [14] , [15] . The membrane-bound tombusvirus VRC contains p33 and p92pol , and the tombusviral ( + ) repRNA , which serves both as a template and as a platform during VRC assembly and activation [16]–[20] . Interestingly , the tombusvirus VRC contains at least seven host proteins as resident members , including glyceraldehyde-3-phosphate dehydrogenase ( GAPDH , encoded by TDH2 and TDH3 in yeast ) [21] , the heat shock protein 70 chaperones ( Hsp70 , Ssa1/2p in yeast ) [22]–[25] , pyruvate decarboxylase ( Pdc1p ) [25] , Cdc34p E2 ubiquitin conjugating enzyme [26] , eukaryotic translation elongation factor 1A ( eEF1A ) [27] , [28] , eEF1Bγ [29] , and Ded1p DEAD-box helicase [30] . The VRC also contains two temporary resident proteins , Pex19p shuttle protein [31] and the Vps23p adaptor ESCRT protein [28] , [32] , [33] . Detailed mechanistic studies revealed that the cellular Hsp70 , eEF1A and Vps23p are involved in the assembly of the VRC , while the functions of host RBPs , such as eEF1A , eEF1Bγ , GAPDH and Ded1p , are to regulate viral RNA synthesis by the VRC [3] , [21]–[24] , [29] , [30] , [34] . In spite of our growing understanding of TBSV replication and TBSV-host interaction , many questions remain . Indeed , multiple genome-wide screens and global proteomics approaches with TBSV using yeast as host identified ∼500 host factors , which interact with viral replication proteins or affect TBSV replication [25] , [26] , [28] , [34]–[38] . Among the host proteins identified are 11 host ATP-dependent RNA helicases out of 39 known yeast helicases that could be involved in TBSV replication . DEAD-box proteins constitute the largest family of RNA helicases , which perform ATP-dependent RNA duplex unwinding , RNA folding , remodeling of RNA-protein complexes , and RNA clamping in cells . DEAD-box helicases are involved in all aspects of cellular metabolism [39]–[41] and affect replication of many viruses [42] , including plant RNA viruses [43] . Plant RNA helicases are also implicated in plant responses to abiotic stress and pathogen infections [44]–[46] . The many cellular RNA helicases identified in TBSV screens are intriguing because , tombusviruses and other small RNA viruses do not code for their own helicases [47] , [48] . These viruses likely recruit host helicases in order to facilitate viral replication . The best-characterized member of the helicase family involved in tombusvirus replication is the yeast Ded1p ( the human DDX3-like ) and the similar plant AtRH20 DEAD-box helicases , both of which promote ( + ) -strand synthesis [30] . Ded1p and AtRH20 bind to the 3′-end of the TBSV minus-strand RNA , making the promoter sequence accessible to p92pol for initiation of ( + ) -strand RNA synthesis . Additional characterization of Ded1p and the similar yeast Dbp2p ( similar to human p68 ) DEAD-box helicases revealed that these helicases play major and overlapping roles in ( + ) -strand synthesis [30] , [49] . Altogether , the identification of 11 yeast RNA helicases involved in tombusvirus replication suggests that tombusviruses likely co-opt a number of host RNA helicases and these helicases might have a number of unique functions in viral replication . In this paper , we characterized the novel pro-viral functions of two yeast RNA helicases , which were among those identified in previous screens , and their plant orthologs in TBSV replication . We found that the yeast Dbp3p ( DEAD box protein 3 , human DDX5-like ) and Fal1p ( eukaryotic translation initiation factor 4AIII-like ) DEAD box helicases , which are involved in ribosome biogenesis in yeast [50]–[52] , and the orthologous Arabidopsis RH2 and RH5 helicases bind to a critical replication enhancer element ( REN ) present in a 5′ proximal region of the TBSV minus-strand RNA . We show that these cellular helicases can locally unwind the double-stranded ( ds ) structure within the REN of the replication intermediate in vitro . These activities by the host helicases enhance in vitro replication and plus-strand RNA synthesis , and the accumulation of TBSV RNA in yeast and plants . We also demonstrate that AtRH2 and AtRH5 helicases work synergistically with the DDX3-like AtRH20 helicase ( that binds to the 3′ promoter sequence ) to facilitate plus-strand synthesis in an asymmetric manner . Altogether , these co-opted host DEAD box helicases greatly enhance TBSV replication by interacting with the viral ( − ) RNA and the replication proteins within the VRCs .
To characterize the functions of host RNA helicases in TBSV replication , first we overexpressed 5 yeast RNA helicases in yeast and tested their effects on TBSV replicon ( rep ) RNA accumulation via Northern blotting ( Fig . S1 ) . These helicases were chosen from the 11 previously identified yeast helicases from several complementary high throughput screens using yeast and tombusviruses [26] , [28] , [35] , [36] , [38] , [53] , [54] . Overexpression of all 5 host RNA helicases increased TBSV accumulation , with the yeast Dbp3p showing the highest ( over 2-fold increase ) stimulation ( Fig . S1A–B ) . The overexpression of these host helicases did not affect the accumulation of p33 replication protein ( Fig . S1A–B ) , suggesting that the effects of this group of RNA helicases are not through increased translation of viral replication proteins . Altogether , the observed 30-to-140% increase in TBSV RNA accumulation due to overexpression of these yeast helicases is significant since overexpression of most yeast proteins nonspecifically reduces TBSV accumulation by 20–30% as demonstrated before based on individual overexpression of 5 , 500 yeast proteins [26] , [54] . Thus , these helicases likely play stimulatory roles in TBSV replication . For additional in-depth studies , we have selected the yeast Dbp3p and the highly similar Fal1p ( human eIF4AIII-like ) together with the orthologous Arabidopsis AtRH2 ( Fal1p ortholog and human eIF4AIII-like ) and AtRH5 ( Dbp3p ortholog , human DDX5-like ) helicases [44]] . Overexpression of AtRH2 and AtRH5 stimulated ( up to 3-fold increase ) TBSV repRNA accumulation in yeast ( Fig . 1A ) . The stimulation of tombusvirus accumulation ( we used Cucumber necrosis virus , CNV , which is very closely related to TBSV ) was also robust in Nicotiana benthamiana host plants when AtRH2 and AtRH5 RNA helicases were overexpressed ( up to ∼2-fold increase in tombusvirus genomic RNA accumulation , and ∼6-fold increase in subgenomic RNA2 accumulation , Fig . 1B , lanes 1–4 and 9–12 versus 5–8 ) , suggesting that these cellular helicases are important host factors . While overexpression of AtRH2 and AtRH5 did not affect the phenotype of uninoculated N . benthamiana plants , the tombusvirus-induced symptoms were intensified ( Fig . 2 ) and the symptoms appeared faster ( not shown ) , when compared with the control host plants not overexpressing the AtRH2 and AtRH5 helicases . Simultaneous co-overexpression of AtRH2 and AtRH5 increased tombusvirus replication by up to 2-fold ( Fig . 1B , lanes 13–16 ) , similar to the level obtained with individual overexpression of AtRH2 and AtRH5 . Also , the symptoms induced by tombusvirus infection in plants with co-overexpression of AtRH2 and AtRH5 were comparable to those induced by the individual overexpression of AtRH2 and AtRH5 ( Fig . 2 ) . Therefore , it is likely that AtRH2 and AtRH5 play comparable and overlapping functions in tombusvirus replication . Overall , the host helicase overexpression studies in yeast and plant established that AtRH2 and AtRH5 helicases and the orthologous yeast Dbp3p and Fal1p helicases could support increased level of tombusvirus RNA replication in host cells . To test if AtRH2 , AtRH5 and the yeast Dbp3p and Fal1p play a comparable role with the previously analyzed yeast Ded1p ( human DDX3-like ) and Dbp2p ( human p68-like ) and the ortologous AtRH20 DEAD-box helicases [30] , [49] , first we performed in vitro RNA binding experiments with affinity-purified recombinant helicase proteins . Using four different cis-acting regions present in the TBSV ( − ) repRNA ( Fig . 3A ) , we found that AtRH2 and AtRH5 bind to a unique cis-acting sequence in a 5′ proximal region of the minus-strand RNA , called RIII ( − ) , which carries a well-defined RNA replication enhancer ( REN ) element ( Fig . 3B , lanes 6–7; and S2A , lanes 5–6 ) [55] , [56] . The recombinant yeast Dbp3p and Fal1p RNA helicases showed similar RNA binding characteristics to the RIII ( − ) REN in vitro ( Fig . S2B–C ) . Importantly , binding of AtRH2 , AtRH5 and the yeast Dbp3p and Fal1p to the TBSV RIII ( − ) REN element is a novel feature for co-opted host helicases . Indeed , the previously characterized AtRH20 and the yeast Ded1p and Dbp2p DEAD-box helicases bound the most efficiently to RI ( − ) sequence carrying the plus-strand initiation promoter ( Fig . 3A; Fig . 3D versus 3C ) [30] , [49] . This striking difference in recognition of two separate cis-acting elements [indeed RIII ( − ) REN is located close to the 5′end , while RI ( − ) promoter region is situated at the 3′ end of the viral ( − ) RNA , Fig . 3A] by these host helicases indicate that their functions and the mechanism of stimulation of TBSV RNA replication must be different . To confirm binding of AtRH5 to the RIII ( − ) REN , we also performed UV cross-linking experiments with 32P-labeled DI-72 ( − ) RNA and cold competitors ( Fig . 3E ) . The 82 nt complete RIII ( − ) REN was a better competitor than similar-sized RNA lacking one of the stem-loop structure and a “bridge” sequence that can base-pair with RI ( − ) sequence via long-range interaction ( Fig . 3F , lanes 6–7 versus 2–3 ) [57] . Our data also support a role for the bridge sequence in binding to AtRH5 ( compare lanes 4–5 with 2–3 , Fig . 3F ) . The efficient binding of AtRH2 , AtRH5 and the yeast Dbp3p and Fal1p to the RIII ( − ) REN region indicates that these helicases might facilitate the unwinding of RNA structures within the RIII ( − ) REN during replication . Therefore , we tested if recombinant AtRH2 and AtRH5 could unwind partial RNA duplexes , which are known to hinder RdRp-driven RNA synthesis [58] , [59] . We chose partial duplex for this assay , because DEAD-box helicases are not processive enzymes and can only unwind short duplexes [40] . Interestingly , addition of purified AtRH2 and AtRH5 unwound the partial RNA duplex ( Fig . 4B , lanes 1 and 2 ) and the yeast Dbp3p and Fal1p showed similar activities ( lanes 7–8 ) . In contrast , Ded1p and AtRH20 helicases did not efficiently unwind the RNA duplex formed only within the RIII ( − ) REN ( Fig . 4B , lanes 3 and 6 ) . The failure to unwind this partial duplex by Ded1p and AtRH20 is likely due to the preference of these helicases to bind the RI ( − ) sequence of the tombusvirus RNA ( Fig . 3 ) [30] , [49] . Overall , the unwinding assay further supported that AtRH2 , AtRH5 and the yeast Dbp3p and Fal1p have a novel pro-viral function during TBSV replication that is based on interaction with the RIII ( − ) REN element . To test the direct effect of AtRH2 and AtRH5 helicases on TBSV RNA synthesis , we utilized detergent-solubilized and affinity-purified tombusvirus replicase from yeast with down regulated Fal1p ( Fig . 5A ) . The requirement for Fal1p helicase on RNA synthesis was supported by the observed ∼55% decrease of the in vitro activity of the purified replicase obtained from yeast with depleted Fal1p when compared with the replicase from yeast expressing Fal1p at high level ( Fig . 5B ) . This purified replicase can only synthesize complementary RNA products on added TBSV templates allowing for the measurement of the level of RNA synthesis [15] , [16] . We found that addition of purified recombinant AtRH2 and AtRH5 helicases to the purified tombusvirus replicase ( obtained from Fal1p depleted yeast ) programmed with the DI-72 ( − ) repRNA stimulated ( + ) -strand synthesis by up to 2-fold ( Fig . 5C , lanes 2 versus 3 and 7 versus 8 ) . Interestingly , AtRH2 and AtRH5 helicases stimulated the production of both full-length ( + ) -strand repRNA product ( via de novo initiation ) and the 3′-terminal extension product ( 3′TEX; due to initiation of complementary RNA synthesis by self-priming from the 3′ end of the template , instead of de novo initiation [60]–[62] ) . This is in contrast with AtRH20 and Ded1p helicases , which stimulated the production of mostly full-length ( + ) -strand repRNA product ( Fig . 5C , lanes 4 and 9 ) [30] , [49] . Time-course experiments with the purified tombusvirus replicase and a minimal template carrying RI ( − ) and RIII ( − ) REN sequences confirmed that AtRH2 and AtRH5 helicases stimulated the production of both full-length ( + ) -strand RNA and 3′TEX products by ∼two-fold at both early and late time points ( Fig . 5D ) . Interestingly , the stimulation of RNA synthesis by AtRH2 and AtRH5 helicases is lost when we used a ( − ) repRNA lacking 5′ sequences including RIII ( − ) REN region ( Fig . 5E , lanes 3–4 versus 1 ) . This is in contrast with AtRH20 , which was able to stimulate the tombusvirus replicase activity on this template RNA ( Fig . 5E , lane 2 ) . This observation was confirmed in in vitro time-course experiments with the purified tombusvirus replicase and a template lacking RIII ( − ) REN by showing the absence of stimulation of RNA synthesis products by AtRH2 and AtRH5 helicases at both early and late time points ( Fig . 5F ) . Therefore , we suggest that AtRH2 and AtRH5 helicases depend on RIII ( − ) REN to facilitate the overall efficiency of template use and RNA synthesis on the ( − ) RNA template by the tombusvirus replicase . To test if AtRH2 and AtRH5 helicases could also stimulate RNA synthesis by the tombusvirus replicase on dsRNA templates , which are formed during TBSV replication ( Kovalev et al , in press ) , we used partial dsRNA duplexes ( Fig . 6A ) . The tombusvirus replicase is inefficient utilizing these dsRNA templates in vitro [30] , [49] , [59] . The addition of recombinant AtRH2 and AtRH5 helicases stimulated RNA synthesis by up to ∼2 . 5-fold in vitro on a partial dsRNA template that had both RI ( − ) and RIII ( − ) REN as part of the duplex ( Fig . 6A , construct ΔRII and 6B , lanes 2 and 8 versus 1 and 7 ) . This level of stimulation was comparable to that obtained with AtRH20 that targets the RI ( − ) sequence ( Fig . 6B , lanes 3 and 9 ) . AtRH2 and AtRH5 helicases also stimulated RNA synthesis on a complete dsRNA template ( Fig . S3A and S3B , lanes 9 and 11 versus 14 ) , producing mostly ( + ) RNA products ( Fig . S3C ) . Importantly , the stimulation of RNA synthesis by AtRH2 and AtRH5 helicases was lost when a partial dsRNA template lacking the RIII ( − ) REN was used ( Fig . 6A , construct ΔRIII and 6B , lanes 5 and 11 versus 4 and 10 ) . In contrast , AtRH20 was still able to stimulate RNA synthesis on this template ( Fig . 6B , lanes 6 and 12 ) , as predicted based on the ability of AtRH20 to bind to RI ( − ) sequence [30] , [49] . Based on these data , we conclude that AtRH2 and AtRH5 helicases can stimulate ( + ) RNA synthesis on dsRNA templates in the presence of RIII ( − ) REN by the tombusvirus replicase . To further study the roles of AtRH2 and AtRH5 helicases in TBSV replication , we used whole cell extracts ( CFE ) prepared from yeast containing temperature-sensitive ( ts ) Fal1p and lacking Dbp3p to support cell-free TBSV replication . TBSV ( + ) RNA has been shown to perform one full cycle of replication , starting with VRC assembly , ( − ) RNA synthesis and finally production of excess amount of ( + ) -strands , in the CFE-based replication assay when purified recombinant p33 and p92pol replication proteins are included [24] , [63] . The CFE-based replication assay showed that ( + ) RNA synthesis decreased by ∼2-fold when compared with the control CFE prepared from yeast with high level of Dbp3p and wt Fal1p ( Fig . S4B , lane 4 versus 1 ) . This is in contrast with ( − ) RNA synthesis ( represented by dsRNA product ) , which was unchanged when the CFE was prepared from dbp3Δ/ts-fal1 yeast versus wt yeast . However , addition of purified AtRH2 or AtRH5 to the CFE assay increased ( + ) RNA production by ∼60–70% , while the ( − ) RNA ( in the form of dsRNA ) was unchanged ( Fig . S4C–D ) . Thus , these data with CFE-based approaches confirm that AtRH2 and AtRH5 and the ortologous yeast helicases are important for ( + ) RNA synthesis during TBSV replication in vitro . To examine if AtRH2 and AtRH5 helicases are present within the tombusvirus replicase complex , we FLAG affinity-purified the tombusvirus replicase from yeast cells actively replicating TBSV repRNA [15] , [28] . The yeast cells also expressed either His6-tagged AtRH2 or His6-AtRH5 helicases from plasmids . We found that the solubilized and affinity-purified tombusvirus replicase preparation , which is highly active on added templates in vitro ( not shown ) , contained His6-AtRH2 ( Fig . 7A , lane 2 ) , while His6-AtRH2 was undetectable in the control yeast sample obtained using the same affinity purification ( Fig . 7A , lane 3 ) . Formation of active replicase complex was not necessary for His6-AtRH2 to become co-opted since the inactive purified replicase [the tombusvirus replicase is inactive in the absence of the viral RNA; [19] , [63]] contained His6-AtRH2 when derived from yeast lacking the viral repRNA ( Fig . 7A , lane 1 ) . We found that His6-AtRH5 showed similar characteristics in these co-purification experiments ( Fig . S5A ) . To test if the TBSV p33 replication protein interacts directly with AtRH2 and AtRH5 , we performed membrane-based split-ubiquitin yeast two-hybrid assay . This assay confirmed the interaction between p33 and AtRH2 and AtRH5 ( Fig . 7B ) . The yeast Dbp3p and Fal1p DEAD-box helicases also interacted with p33 in this assay ( Fig . S5B ) . To test what region within the TBSV p33 replication protein is involved in the interaction with AtRH2 and AtRH5 , we performed pull-down experiments with MBP-tagged p33 derivatives from E . coli . These experiments revealed that the RPR-motif in p33 involved in viral RNA-binding was responsible for interacting with both AtRH2 and AtRH5 ( Fig . S6A–B ) . Interestingly , the interaction of p33 with AtRH2 and AtRH5 did not affect the ability of p33 to bind to the viral ( + ) repRNA in vitro ( Fig . S7 ) . The interaction between p33 and ( + ) repRNA is required for recruitment of the viral ( + ) RNA into replication [17] , [64] . Based on the above interaction data , we suggest that the viral p33 replication protein ( and p92 replication protein , which within its N-terminal region contains the p33 sequence due to the expression strategy ) co-opts AtRH2 and AtRH5 DEAD-box proteins from the host cells into the viral replicase complexes to aid the replication process . The difference in viral RNA-binding by AtRH2/AtRH5 versus AtRH20 suggests that these groups of helicases could have synergistic effect on tombusvirus replication . This was tested by co-expressing AtRH5 and AtRH20 in N . benthamiana leaves replicating the tombusvirus RNA ( Fig . 8 ) . Interestingly , AtRH5 and AtRH20 host proteins , when co-expressed together , had the largest ( up to ∼5 . 5-fold , Fig . 8A , lanes 13–16 ) effect on viral genomic RNA accumulation in comparison with the ∼2-fold increase for separate expression of AtRH20 ( lanes 5–8 ) and AtRH5 ( lanes 9–12 ) . Also , the symptom development of tombusvirus-infected plants was the most severe and the fastest when the two helicases were co-expressed ( Fig . 8C ) . Based on these data , we suggest that AtRH5 and AtRH20 have a synergistic effect on tombusvirus replication ( see further explanation in discussion ) .
One of the hallmark features of ( + ) RNA virus replication is the asymmetric nature of RNA synthesis [12] , [69]–[71] . The replication process leads to the production of abundant ( + ) RNA progeny , while the ( − ) RNA templates are likely sequestered in dsRNA forms within the VRCs . The presented in vitro data based on the solubilized/purified tombusvirus replicase and the CFE assay containing the membrane-bound VRC indicate that the eIF4AIII-like RNA helicases can mainly stimulate TBSV ( + ) -strand synthesis , while their effects on ( − ) RNA synthesis have not been observed ( not shown ) . The recombinant eIF4AIII-like RNA helicases enhanced ( + ) -strand synthesis by the purified recombinant tombusvirus replicase , it is possible that these helicases directly affect TBSV RNA synthesis via affecting the structure of the RNA templates , including the RIII ( − ) REN . However , we cannot fully exclude that AtRH2 , AtRH5 and the yeast Fal1p and Dbp3p helicases could also affect the activity of the VRC due to their interactions with p33 and p92 ( Fig . 7 ) . Overall , the recruitment of eIF4AIII-like DEAD-box helicases for replication of a small RNA virus is remarkable , and we suggest that small ( + ) RNA viruses likely co-opt two or more different host helicases that interact with different cis-acting elements in the viral RNA to aid viral replication . Based on their RNA binding features and their abilities to unwind dsRNA regions only locally , we propose that the helicase functions of AtRH2 , AtRH5 and the yeast Fal1p and Dbp3p are likely important for unwinding of the RIII ( − ) REN region in the dsRNA structure formed within the VRCs during TBSV replication . Why is local unwinding of dsRNA within the RIII ( − ) REN stimulatory for replication ? We suggest that the locally opened RIII in the dsRNA form might allow the bridge sequence within the RIII ( − ) REN to participate in a long-range base-pairing with the 3′end of the ( − ) RNA , thus bringing the 5′ and 3′ terminal sequences of the ( − ) RNA in close vicinity ( Fig . 9 ) . This could facilitate ( + ) -strand synthesis and the reutilization of the viral replicases ( VRCs ) for multiple rounds ( as discussed below ) . However , the long-distance base pairing between the “bridge” in RIII ( − ) REN and the cPR promoter in RI ( − ) , both of which are buried in the dsRNA structure , should also depend on opening the dsRNA form within RI ( − ) . This function is unlikely performed by eIF4AIII-like RNA helicases . Instead , we have previously demonstrated that the subverted DDX3-like AtRH20/Ded1p helicases could open up the dsRNA structure within the RI ( − ) sequence [30] . In summary , based on this and previous publications [21] , [30] , [49] , the emerging picture with TBSV is that this virus utilizes co-opted RNA-binding host proteins to regulate asymmetric viral RNA replication . The recruited host proteins are needed for specific interactions with various cis-acting sequences in the viral ( − ) RNA because the viral p33/p92 replication proteins bind to TBSV ( − ) RNA nonspecifically [72] . We propose that , first , the recruited eIF4AIII-like RNA helicase proteins bind to RIII ( − ) REN , while the DDX3-like AtRH20/Ded1p helicases bind to RI ( − ) sequence . The interactions of two groups of helicases with the viral dsRNA likely opens up the 5′ proximal RIII ( − ) REN and the 3′ terminal promoter region from the dsRNA structure present in the VRCs . Then , long-distance RNA-RNA interaction between the bridge sequence in the RIII ( − ) REN and the 3′ terminal sequence [57] could “circularize” the ( − ) RNA template and bring the p92 RdRp protein from the 5′ end back to the 3′ end for a new round of ( + ) -strand synthesis ( Fig . 9 ) . As proposed earlier [30] , [49] , an additional function of AtRH20/Ded1p is to further unwind local secondary structure within RI ( − ) to promote the association of the cellular GAPDH with an AU-rich internal site and proper positioning of the GAPDH-p92 RdRp complex [73] over the ( + ) -strand initiation promoter , leading to robust ( + ) RNA synthesis . Therefore , we propose that the synergistic effect between the two groups of subverted helicases , host GAPDH and the viral p92pol might promote efficient recycling of the viral RdRp , resulting in multiple rounds of ( + ) RNA synthesis on the same dsRNA template ( Fig . 9 ) . This strategy could be beneficial for the virus by allowing asymmetric RNA synthesis on dsRNA templates , thus leading to excess amount of progeny ( + ) RNAs . It is currently not known if other viruses might also use two different groups of cellular helicases to aid their replication . However , HIV retrovirus , which also lacks viral-coded helicases , has been shown to recruit several cellular helicases , including DDX3 , for various steps of its infection cycle [74]–[76] . In addition , host DEAD-box helicases have been shown to affect virus infections , including translation of viral proteins [77]–[79]; viral RNA replication [43] , [80]–[83]; subgenomic RNA synthesis [84]; reverse transcription [85]; virus assembly [86]; virus-mediated regulation of host gene transcription [87] , and the activity of many anti-viral proteins [88]–[90] . Therefore , the emerging picture is that RNA viruses subvert multiple members of the cellular RNA helicase family during infections .
Saccharomyces cerevisiae strain BY4741 , Δdbp3 ( YKO library ) and TET::Fal1 yeast strain ( yTHC library ) , were obtained from Open Biosystems ( Huntsville , AL , USA ) . Ts-Fal1 yeast strain was from a yeast ts strain collection [91] . Yeast strain NMY51 was obtained from Dualsystems . Δdbp3/ts-Fal1 yeast strain was generated as follows: plasmid pYM-14 ( EUROSCARF ) [92] was used for PCR with primers #5011 and #5012 ( Table S1 ) to amplify the Dbp3 deletion cassette . Ts-Fal1 yeast was transformed with the obtained PCR product and the suitable yeast strain was selected on G418 containing plates . Then , yeast strains were grown in liquid media and genomic DNA was isolated . The correct deletion site was checked by PCR with primers #2215 and #5019 using genomic DNA as a template . PCR products of yeast helicase genes were obtained as follows: yeast genomic DNA was used as a template for amplification by PCR with primers #4612 and #4613 for DBP3; #4569 and #4570 for DBP5; #4611 and #4756 for DBP7; #2351 and 4825 for TIF1; and #4893 and #4894 for FAL1 . The generated PCR products were digested with BamHI and XbaI in the case of DBP3 and DBP5 and with BglII and XbaI in the case of DBP7 . Plasmids pYC-His ( provided by Dr . Daniel Barajas ) and pMalc-2x ( New England Biolabs ) were digested with BamHI and XbaI and pPr-N ( Dualsystems ) was digested with BamHI and NheI and ligated with the similarly treated PCR products of DBP3 , DBP5 and DBP7 . The PCR products of plant helicases were obtained as follows: Total RNA was isolated from A . thaliana and used for RT-PCR with primers #4816 and #4817 for AtRH2; #4813 and #4871 for AtRH4; #4819 and #4820 for AtRH5; and #4822 and #4823 for AtRH7 . The obtained PCR products were digested with BamHI and SalI in the case of AtRH2 , AtRH4 , AtRH7 , FAL1 and TIF1 and BglII and SalI in the case of AtRH5 . Plasmid pYC-His was digested with BamHI and XhoI and plasmids pMalc-2x , pPr-N , pET-30c ( + ) ( for AtRH2 and AtRH5 ) , and pGD-35S ( for AtRH2 , AtRH4 , AtRH5 and AtRH7 ) were digested with BamHI and SalI and ligated to similarly treated PCR products of AtRH2 , AtRH4 , AtRH5 and AtRH7 , FAL1 and TIF1 . Overexpression plasmid pGD-RH20 was obtained as follows: AtRH20 sequence was amplified using primers #4318 and #4473 and pMAL-RH20 [49] as a template . The obtained PCR product was digested with BamHI and SpeI and inserted into pGD-35S plasmid , which was digested with BamHI and XbaI . The plasmids pGBK-HIS-Cup-Flag33/Gal-DI-72 expressing Flag-tagged p33 of cucumber necrosis virus ( CNV ) and the TBSV DI-72 repRNA [93] , pGAD-Cup-Flag92 [94] , pGD-CNV and pGD-p19 [95] were described earlier . Cultures of Agrobacterium tumefaciens C58C1 strain carrying pGD-RH2 , pGD-RH5 , pGD-RH20 ( individually ) with pGD-CNV and pGD-p19 were prepared and infiltrated into leaves of N . benthamiana as described earlier [95] . Agrobacterium culture carrying empty pGD-35S plasmid was used as a negative control . During multiple overexpression , we used the Agrobacterium cultures with the following density: 0 . 15 OD600 for pGD-CNV , 0 . 15 for pGD-p19 and 0 . 7 for one of pGD-RHx ( or empty pGD ) or 0 . 35 for each of pGD-RHx when combination of two was applied to the same leaf . Plant samples from infiltrated leaves were taken 60 hours after infection . RNA was isolated and Northern blot analysis was performed using previously described [14] , [95] . For selected samples , proteins were isolated and total proteins level was adjusted based on Coomassie-blue staining . For Western blot analysis , anti-p33 antibody was used ( a generous gift of Herman Scholthof , Texas AM University ) . Pictures of infected plants were taken 7 days after agroinfiltration . Recombinant MBP-tagged helicase proteins , the MBP-tagged TBSV p33 and p92 replication proteins and several truncated MBP-tagged p33C derivatives ( described earlier ) were expressed in E . coli and purified as published earlier with modifications [72] , [96] . Briefly , the expression plasmids were transformed into E . coli strain BL21 ( DE3 ) CodonPlus . Protein expression of the selected helicase proteins was induced by isopropyl-β-D-thiogalactopyranoside ( IPTG ) for 8 h at 23°C and in the case of viral proteins p33 and p92 at 16°C . After the collection of cells by centrifugation ( 5 , 000 rpm for 5 min ) , the cells were resuspended and sonicated in low-salt column buffer ( 30 mM HEPES-KOH pH 7 . 4 , 25 mM NaCl , 1 mM EDTA , 10 mM β-mercaptoethanol ) . To remove cells debris , the lysate was centrifuged at 14 , 000 rpm for 5 min , followed by supernatant incubation with amylose resin ( NEB ) for 15 min at 4°C . After careful washing of the columns , the proteins were eluted with MBP-elution buffer [column buffer containing 0 . 18% ( W/V ) maltose] . Purification of His6-tagged AtRH2 and His6-AtRH5 ( using plasmids pET30-RH2 or pET30-RH5 ) was carried out using ProBond ( Invitrogen ) resin ( washed with column buffer , containing 60 mM Imidazole and eluted with column buffer [lacking β-mercaptoethanol] , containing 1 M Imidazole ) , following otherwise the same protocol as for the MBP-tagged proteins . Purified proteins were aliquoted and stored at −80°C . Proteins used for the replication assays were at least 95% pure , as determined by SDS-PAGE ( not shown ) . For in vitro pull-down assay , purified His6-tagged helicase proteins ( 200 µg ) were loaded onto MBP columns , containing bound MBP-tagged p33C derivatives and incubated with mixing for 25 min at 4°C [30] . The columns were washed three times with cold column buffer and the bound protein complexes were eluted with MBP-elution buffer . The eluates were analyzed for the presence of His6-tagged proteins by SDS-PAGE , followed by Coomassie blue staining or Western blotting with an anti-His antibody . PCR products for “+bridge” and “Δbridge” constructs ( Fig . 3E ) were prepared as follows: pGBK-HIS-Cup-Flag33/Gal-DI-72 was used as a template for PCR with primer pairs #5480 and #5481 , or #5480 and #5482 , respectively . The generated PCR products were used to obtain +bridge RNA ( 86 nt in length ) or Δbridge RNA ( 73 nt in length ) , each starting from position 368 in DI-72 . The RNA transcripts were synthesized on the PCR templates using T7-based transcription [97] . The RNA transcripts used in CFE-based replication or replicase assays were purified as described earlier [97] . The 32P-labeled or unlabeled four separate regions ( RI-IV , Fig . 3A ) and the full-length DI-72 ( + ) and ( − ) RNAs were produced as published [72] . Full-length FHV-derived DI-634 ( + ) or ( − ) RNA was produced as described [30] . The amounts of transcripts were quantified by UV spectrophotometer ( Beckman ) . Partial dsRNA duplexes [ ( − ) R124/ ( + ) DI-72 and ( ( − ) R134/ ( + ) DI-72] for in vitro replicase assay were prepared as follows: approximately 2 pmol of 32P-labeled ( − ) R134 or ( − ) R124 were annealed to unlabeled DI-72 ( + ) RNA in STE buffer ( 10 mM TRIS , pH 8 . 0 , 1 mM EDTA , and 100 m M NaCl ) by slowly cooling down the samples ( in 20 µl ) from 94°C to 25°C in 30 min . Complete DI-72 dsRNA duplexes were prepared using Replicator RNAi kit ( Finnzymes ) . Briefly , DI-72 ( + ) -strand RNA , which was synthesized with T7 transcription , was used as a template for synthesis of DI-72 dsRNA by Phi6 RNA polymerase in vitro . Purity of dsRNAs was tested with agarose gel-electrophoresis . EMSA was performed as described previously [17] , with modifications: the binding assay was done in the presence of 20 mM HEPES ( pH 7 . 4 ) , 50 mM NaCl , 10 mM MgCl2 , 1 mM DTT , 1 mM EDTA 5% glycerol , 6 U of RNasin and 0 . 1 mg tRNA in a 10 µl reaction volume . Approximately 0 . 1 pmol of 32P-labeled RNA probes , 0 . 6 µg of purified recombinant proteins and 0 . 15 or 0 . 3 µg of unlabeled RNA were used in template competition assay . For the assay , we used 0 . 02 µg MBP-p33C , 0 . 1 pmol of 32P-labeled SLR2 RNA ( the stem-loop sequence from RII ) [17] and MBP-AtRH2 ( or MBP-AtRH5 ) , in 0 . 02 , 0 . 06 , 0 . 2 or 0 . 6 µg amounts . Strand separation assay was performed as published [58] . Briefly , about 2 pmol of 32P-labeled RIII ( − ) or RI/II/III ( − ) were annealed to unlabeled DI-72 ( + ) RNA in STE buffer by slowly cooling down the samples ( in 20 µl ) from 94°C to 25°C in 30 min . 0 . 6 µg of purified recombinant helicase proteins ( in MBP elution buffer ) or MBP as a negative controls were added separately to the partial dsRNA duplex in the RdRp buffer . 2 mM of ATP was added to the reaction . Reaction mixtures were incubated for 15 min at room temperature and loaded onto 5% nondenaturing polyacrylamide gel as described previously [58] . Some samples were treated with proteinase K after the assay . The incubation with proteinase K lasted for 10 min at 37°C using 0 . 5 µl of proteinase K from stock of 20 mg/ml ( dissolved in 50 mM Tris-HCl pH 8 . 0 , supplemented with 1 . 5 mM CaCl2 ) , followed by loading onto 5% nondenaturing polyacrylamide gel . The UV-cross-linking assay was performed as described [98] . The 10 µl reaction mixture contained 1 µg purified MBP-tagged AtRH2 or AtRH5 proteins , respectively , 0 . 5 nM 32P-UTP-labeled RNA probe , 10 mM HEPES , pH 7 . 9 , 100 mM KCl , 1 mM MgCl2 , 10% glycerol , and 1 µg tRNA . Unlabeled RNA transcripts of RIII ( − ) or “+bridge” and “Δbridge” constructs ( all RNAs were comparable in length ) were used as competitors in 0 . 1 to 0 . 3 µg amounts in the competition assay . After the formation of RNA–protein complexes during incubation of the reaction mixtures at room temperature for ∼15 min , we transferred the reaction mixtures to a 96-well plate on ice . To cross-link the RNA and protein , we irradiated the reaction mixtures on ice at 254 nm wave-length for 10 min using an UV Stratalinker 1800 ( Stratagene ) . Then , we digested the unprotected RNAs by 1 mg/ml RNase A for 15 min at 37°C . Samples were mixed and boiled for 10 min in 1× SDS loading dye . Analysis was performed using SDS-PAGE and phosphorimaging [98] . Yeast strain Δdbp3 was transformed with plasmids pGBK-HIS-Cup-Flag33/Gal-DI-72 , pGAD-Cup-Flagp92 and pYC-Gal-6×HisRH2 or pYC-Gal-6×HisRH5 to co-express the cellular helicases with the viral replication proteins in yeast cells actively replicating the TBSV repRNA . The transformed yeast strains were selected on SC-ULH− plates and then pre-grown overnight at 29°C in selective media containing 2% glucose [49] . After that yeast strains were pelleted by centrifugation at 2 , 000 rpm for 3 min , we washed the pellet with SC-ULH− media containing 2% galactose and 50 µM CuSO4 , yeast were grown for 36 hours in SC-ULH− media containing 2% galactose at 23°C . Pelleted yeasts ( about 200 mg ) were used for affinity-purification of FLAG-p33 and FLAG-p92 with anti-FLAG M2 agarose as published previously [15] , [49] . FLAG-p33 was detected with anti-Flag antibody ( 1/10 , 000 dilution ) and AP-conjugated anti-mouse antibody ( 1/10 , 000 ) . His6-AtRH2 or His6-AtRH5 proteins were detected with anti-His antibody from mouse ( 1/10 , 000 dilution ) and AP-conjugated anti-mouse ( 1/10 , 000 ) followed by NBT-BCIP detection [15] , [49] . The split-ubiquitin assay was based on the Dualmembrane kit3 ( Dualsystems ) . pGAD-BT2-N-His33 , expressing the CNV p33 replication protein ( bait construct ) , has been published earlier [26] . Yeast strain NMY51 was co-transformed with pGAD-BT2-N-His33 and one of the prey constructs carrying the cDNA for a given helicase or pPR-N-RE as a negative control or pPR-N-SSA1 as a positive control [26] . Yeasts were plated onto Trp2−/Leu2− synthetic minimal medium plates . After transformed colonies were picked with a loop and re-suspended in water , we streaked them onto TLH− ( Trp2−/Leu2−/His2− ) plates to test for helicase protein-p33 interactions as described [26] . To study the effect of over-expression of yeast and plant helicase proteins on DI-72 repRNA replication in yeast , we transformed S . cerevisiae strain BY4741 with three plasmids: pGBK-HIS-Cup-Flag33/Gal-DI-72 , pGAD-Cup-Flag92 and one of the following plasmids: pYC-His-RH2 , pYC-His-RH4 , pYC-His-RH5 , pYC-His-RH7 , pYC-His-Dbp3 , pYC-His-Dbp5 , pYC-His-Dbp7 , pYC-His-Fal1 , pYC-His-Tif1 ( empty pYC plasmid was used as a control ) . After the selection of transformed yeast cells on SC-ULH− plates , they were pre-grown in SC-ULH− media containing 2% glucose for 24 h at 29°C . Then cells were centrifuged at 2 , 000 rpm for 3 min , washed with SC-ULH− media containing 2% galactose and resuspended in SC-ULH− media containing 2% galactose and 50 µM CuSO4 . After growing yeast cells for 14 h at 23°C , they were used for total RNA extraction and Northern blotting and Western blotting as previously published [15] . After yeast strain TET:Fal1 was transformed with plasmids pGBK-HIS-Cup-Flag33/Gal-DI-72 and pGAD-Cup-Flag92 , it was pre-grown in SC-ULH− media containing 2% glucose at 29°C . Then yeast cells were centrifuged at 2 , 000 rpm for 3 min , washed with SC-ULH− media containing 2% galactose and resuspended in SC-ULH− media containing 2% galactose and 50 µM CuSO4 in the presence or absence of 1 mg/ml Doxycycline . After growing for 24 h at 23°C , and yeasts were pelleted and the replicase was purified according to a previously published procedure [25] . Briefly , approximately 200 mg of wet yeast cell pellet were resuspended in TG buffer [50 mM Tris–HCl [pH 7 . 5] , 10% glycerol , 15 mM MgCl2 , 10 mM KCl , 0 . 5 M NaCl , and 1% [V/V] yeast protease inhibitor cocktail ( Ypic ) ] and homogenized in FastPrep Homogenizer ( MP Biomedicals ) by glass beads . After the membrane fraction was solubilized with 1 ml TG buffer containing 1% Triton X-100 , 1% [V/V] Ypic , Flag-p33 and Flag-p92 were affinity purified on anti-FLAG M2-agarose affinity resin ( Sigma ) . Replicase complex was eluted with 200 ml elution buffer [50 mM Tris–HCl [pH 7 . 5] , 10% glycerol , 15 mM MgCl2 , 10 mM KCl , 50 mM NaCl , 0 . 5% Triton X-100 , and 0 . 15 mg/ml Flag peptide ( Sigma ) ] . In vitro RdRp activity assays with the purified tombusvirus replicase preparations were performed by using DI-72 ( − ) RNA , RI/II ( − ) RNA or partial dsRNA [such as ( − ) RI/II/IV/ ( + ) DI-72 or [ ( − ) RI/III/IV/ ( + ) DI-72] or complete dsRNA templates . RNase ONE digestion to remove single-stranded 32P-labeled RNA was performed at 37°C for 30 min in a 1× RNase ONE buffer containing 0 . 1 µl of RNase ONE ( Promega ) [49] . We prepared cell-free extract ( CFE ) from BY4741 or Δdbp3/ts-Fal1 yeast strains as described earlier [24] . The CFE-based TBSV replication assays were performed in 20 µl total volume containing 2 µl of CFE , unlabeled 0 . 15 µg DI-72 ( + ) RNA or RI/II/IV ( + ) RNA transcripts , 200 ng purified MBP-p33 , 200 ng purified MBP-p92pol and 200 ng purified MBP-tagged helicase proteins . The assays were performed as published [24] , [63] . Fractionation of the assay products was done as follows: after 3 h of incubation at 25°C , reaction mixtures were centrifuged at 21 , 000× g for 10 min to separate the “soluble” ( supernatant ) and “membrane” ( pellet ) fraction . Then the membrane fraction was re-suspended in reaction buffer . Both fractions were then treated as separate samples during phenol/chlorophorm extraction , ethanol precipitation . The samples were dissolved in 1×RNA loading dye and analyzed by PAGE electrophoresis in 5% polyacrylamide gel containing 8 M urea with 0 . 5× Tris-borate/EDTA buffer as described [24] , [63] . For the detection of the 32P-labeled dsRNAs generated in the CFE assays , we prepared the RNA samples in 1× RNA loading dye ( containing 25% formamide ) , followed by dividing the samples into two equal fractions; one half was loaded on the gel without heat-treatment , while the other half was heat-treated for RNA denaturation at 85°C for 5 min and analyzed by PAGE [27] .
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Genome-wide screens for host factors affecting tombusvirus replication in yeast indicated that subverted cellular RNA helicases likely play major roles in virus replication . Tombusviruses do not code for their own helicases and they might recruit host RNA helicases to aid their replication in infected cells . Accordingly , in this paper , the authors show that the yeast eIF4AIII-like Fal1p and Dbp3p and the orthologous plant AtRH2 and AtRH5 DEAD-box helicases are co-opted by Tomato bushy stunt virus ( TBSV ) to aid viral replication . The authors find that eIF4AIII-like helicases bind to the replication enhancer element ( REN ) in the viral ( − ) RNA and they promote ( + ) -strand TBSV RNA synthesis in vitro . Data show that eIF4AIII-like helicases are present in the viral replicase complex and they bind to the replication proteins . In addition , the authors show synergistic effect between eIF4AIII-like helicases and the previously identified DDX3-like Ded1p/AtRH20 DEAD box helicases , which bind to a different cis-acting region in the viral ( − ) RNA , on stimulation of plus-strand synthesis . In summary , the authors find that two different groups of cellular helicases promote TBSV replication via selectively enhancing ( + ) -strand synthesis through different mechanisms .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"biochemistry",
"infectious",
"diseases",
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2014
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The Expanding Functions of Cellular Helicases: The Tombusvirus RNA Replication Enhancer Co-opts the Plant eIF4AIII-Like AtRH2 and the DDX5-Like AtRH5 DEAD-Box RNA Helicases to Promote Viral Asymmetric RNA Replication
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Trypanosoma brucei brucei infects livestock , with severe effects in horses and dogs . Mouse strains differ greatly in susceptibility to this parasite . However , no genes controlling these differences were mapped . We studied the genetic control of survival after T . b . brucei infection using recombinant congenic ( RC ) strains , which have a high mapping power . Each RC strain of BALB/c-c-STS/A ( CcS/Dem ) series contains a different random subset of 12 . 5% genes from the parental “donor” strain STS/A and 87 . 5% genes from the “background” strain BALB/c . Although BALB/c and STS/A mice are similarly susceptible to T . b . brucei , the RC strain CcS-11 is more susceptible than either of them . We analyzed genetics of survival in T . b . brucei-infected F2 hybrids between BALB/c and CcS-11 . CcS-11 strain carries STS-derived segments on eight chromosomes . They were genotyped in the F2 hybrid mice and their linkage with survival was tested by analysis of variance . We mapped four Tbbr ( Trypanosoma brucei brucei response ) loci that influence survival after T . b . brucei infection . Tbbr1 ( chromosome 3 ) and Tbbr2 ( chromosome 12 ) have effects on survival independent of inter-genic interactions ( main effects ) . Tbbr3 ( chromosome 7 ) influences survival in interaction with Tbbr4 ( chromosome 19 ) . Tbbr2 is located on a segment 2 . 15 Mb short that contains only 26 genes . This study presents the first identification of chromosomal loci controlling susceptibility to T . b . brucei infection . While mapping in F2 hybrids of inbred strains usually has a precision of 40–80 Mb , in RC strains we mapped Tbbr2 to a 2 . 15 Mb segment containing only 26 genes , which will enable an effective search for the candidate gene . Definition of susceptibility genes will improve the understanding of pathways and genetic diversity underlying the disease and may result in new strategies to overcome the active subversion of the immune system by T . b . brucei .
Sleeping sickness ( African trypanosomiasis ) continues to pose a major threat to 60 million people in 36 countries in sub-Saharan Africa . The estimated number of new cases is currently between 50 000 and 70 000 per year ( WHO 2006 – African trypanosomiasis - http://www . who . int/mediacentre/factsheets/fs259/en/ ) . The disease is caused by infection with the tsetse fly-transmitted [1] protozoan haemoflagellate Trypanosoma brucei , which has three major sub-species: T . b . gambiense , T . b . rhodesiense and T . b . brucei . Two of them , T . b . gambiense and T . b . rhodesiense cause sleeping sickness in humans and can also infect animals; thus domestic and wild animals are an important parasite reservoir ( WHO 2006 - http://www . who . int/mediacentre/factsheets/fs259/en/ ) . The third species , T . b . brucei infects a wide range of mammals , but is unable to infect humans because it lacks the SRA ( serum resistance-associated ) protein that prevents lysis induced by Apolipoprotein L1 , which is present in normal human serum [2] , [3] . T . b . equiperdum and T . b . evansi , which are derived from T . b . brucei , are adapted to transmission without development in tsetse fly , allowing these parasites to spread outside the African tsetse belt [4] . Upon the bite of the mammalian host by trypanosome-infected tsetse fly ( Glossina ssp . ) , the parasites multiply locally in the skin and elicit a local host response in the form of an indurated skin lesion called the chancre . Eventually , the parasites enter the blood circulation via lymph vessels and can survive in the blood circulation throughout the infection of the host ( reviewed in [5] , [6] ) , remaining continually exposed to the host's immune system . T . brucei species have the ability to penetrate the walls of capillaries and invade interstitial tissues , but they always remain extracellular as opposed to T . cruzi [6] . During the meningo-encephalitic phase of the infection parasites pass into brain where they cause serious pathology [7] . African trypanosomes have evolved very sophisticated evasion mechanisms to survive in chronically infected host . These evasion mechanisms include antigenic variation of the variant surface glycoprotein ( VSG ) [8] and the induction of alterations in the host's defense system , such as excessive activation of the complement system leading to persistent hypocomplementemia [9] , anemia , thrombocytopenia [9] , down regulation of nitric oxide production [10] , polyclonal B-lymphocyte activation [11] , and marked immunosuppression [12] , [13] . Most likely African trypanosomes induce also other , yet undiscovered , changes in the physiology of the infected host , which might interfere with effective control of the parasite [6] . Due to genetic and biological relatedness of T . b . brucei to other Trypanosoma species , many host responses to their infections are shared and therefore many aspects of human African trypanosomiasis ( HAT ) as well as livestock and horses infections are studied in experimental mouse infection with T . b . brucei . These experiments revealed great genetic variability among mouse strains in response to T . b . brucei , however not all results can be compared with each other because they were obtained in different experimental conditions using different T . b . brucei isolates . Strains DBA/2 , BALB/c , BALB . B , and C3H/He are susceptible to T . b . brucei and display higher parasitemia , survive for a shorter time , whereas strains C57BL/10 , C57BL/6 , and B10 . D2 are relatively resistant and survive a longer time [14] , [15] . In another experiment BALB/c mice exhibited higher parasitemia than C57BL/6 , but they did not differ in survival [16] . Comparison of C57BL/6 and 129/SvEv showed that 129/SvEv exhibited higher parasitemia and lower specific IgM ( but not IgG ) antibody levels than C57BL/6 mice . Parasitemia was higher in 129Sv/Ev , but the weight loss , mortality and the number of trypanosomes in brain was higher in C57BL/6 [7] . CBA/N mice , deficient in production of a thymus-dependent high affinity antibody subset [17] survived longer than the strains CBA/CaT6 and A/J and had slightly lower splenomegaly , but all three strains exhibited similar numbers of circulating parasites [18] . Mouse genes controlling susceptibility to trypanosomiasis caused by the subgenus T . ( Nannomonas ) congolense [19]–[22] and by sub-genus T . ( Schizotrypanum ) cruzi the causative agent of Chagas disease [23] , have been successfully mapped , but a genome-wide search for susceptibility loci to the subgenus T . ( Trypanozoon ) brucei has not yet been attempted . We have therefore analyzed the genetic control of T . b . brucei resistance using the recombinant congenic ( RC ) strains of the BALB/c-c-STS/Dem ( CcS/Dem ) series . This series comprises 20 homozygous strains all derived from two parental inbred strains: the “background” strain BALB/c and the “donor” strain STS . Each CcS/Dem strain contains a different , random set of approximately 12 . 5% genes of the donor strain STS and approximately 87 . 5% genes of the background strain BALB/c [24] . This series has been successfully used to study genetics of complex diseases ( partly reviewed in van Wezel et al . 2001 [25] ) , including infection with Leishmania major [26]–[30] and Bordetella pertussis [31] . In the present work , we show that RC strain CcS-11 differs in survival from both parental strains BALB/c and STS . In the cross between BALB/c and CcS-11 , we mapped four genetic loci that influence survival after T . b . brucei infection . Two of these loci have individual effects; the other two operate in mutual non-additive interaction . This is the first report of genetic loci controlling resistance to T . b . brucei .
Mice of strains tested for survival BALB/cHeA ( BALB/c ) ( 10 females , 10 males ) , STS/A ( 10 females , 10 males ) , CcS-5 ( 10 females , 10 males ) , CcS-11 ( 10 females , 10 males ) , CcS-16 ( 9 females , 9 males ) and CcS-20 ( 10 females , 10 males ) were 13 to 23 weeks old ( mean 17 , median 17 ) at the time of infection . Splenomegaly , hepatomegaly , body weight changes and serum levels of seven cytokines and chemokines were analyzed using females of BALB/c ( 22 infected , 22 non-infected ) , STS ( 17 infected , 13 non-infected ) and CcS-11 ( 25 infected , 26 non-infected ) , which were 8 to 19 week old ( mean 13 , median 13 ) at the time of infection . When used for these experiments , CcS/Dem strains passed more then 38 generation of inbreeding and therefore were highly homozygous . The regions of RCS' genomes inherited from the BALB/c or STS parents were defined [32] . 169 F2 hybrids between CcS-11 and BALB/c ( age 22 and 23 weeks at the time of infection ) were produced at the Institute of Molecular Genetics . They comprised 85 females and 84 males and were tested simultaneously as a single experimental group . During the experiment , mice were placed into individually ventilated cages behind a barrier . The research had complied with all relevant European Union guidelines for work with animals and was approved by the Institutional Animal Care Committee of the Institute of Molecular Genetics AS CR and by Departmental Expert Committee for the Approval of Projects of Experiments on Animals of the Academy of Sciences of the Czech Republic . The strain of Trypanosoma brucei brucei ( AnTar1 ) was a generous gift of Jan van den Abbeele , Institute of Tropical Medicine “Prince Leopold” , Antwerp , Belgium . Parasites stored in liquid nitrogen were thawed and used to infect BALB/c males by intraperitoneal inoculation . Four to five days after infection , 10 µl of tail blood was collected , diluted in 90 μl of 1% formaldehyde in PBS , and the trypanosomes were counted in a Bürker counting chamber . Subsequently , tail blood was diluted in RPMI containing L-glutamine , sodium bicarbonate and glucose ( Cat . Nr . R8758 , Sigma , St . Louis , MO ) in order to contain appropriate numbers of parasites for inoculation ( Please see below ) . Mice were inoculated intraperitoneally with 2 . 5×104 bloodstream forms of T . b . brucei ( AnTar1 strain ) in 50 . µl of RPMI containing L-glutamine , sodium bicarbonate and glucose ( Cat . Nr . R8758 , Sigma , St . Louis , MO ) . Survival time was measured in days following the day of challenge ( day 0 ) . In the mice infected with T . b . brucei , 90 µl of blood were obtained 2 days after infection for determination of cytokine and chemokine levels . Mice were killed 10 days after inoculation . The blood , spleen , and liver were collected for the further analysis . Levels of GM-CSF ( granulocyte-macrophage colony-stimulating factor ) , CCL2 ( chemokine ( C-C motif ) ligand 2 ) /MCP-1 ( monocyte chemotactic protein-1 ) , CCL3/MIP-1α ( macrophage inflammatory protein-1α ) , CCL4/MIP-1β ( macrophage inflammatory protein 1-β ) , CCL5/RANTES ( regulated upon activation , normal T-cell expressed , and secreted ) , CCL7/MCP-3 ( monocyte chemotactic protein-3 ) and TNF-α , in serum were determined using Mouse chemokine 6-plex kit ( Bender MedSystems , Vienna , Austria ) and Mouse TNF-α simplex kit as multiplex assay . The kit contains two sets of beads of different size internally dyed with different intensities of fluorescent dye . The set of small beads is used for GM-CSF , CCL5/RANTES , CCL4/MIP-1β and TNF-α and set of large beads for CCL3/MIP-1α , CCL2/MCP-1 and CCL7/MCP-3 . The beads are coated with antibodies specifically reacting with each of the analytes ( chemokines ) to be detected in the multiplex system . A biotin secondary antibody mixture binds to the analytes captured by the first antibody . Streptavidin – Phycoerythrin binds to the biotin conjugate and emits fluorescent signal . Test procedure was performed in the 96 well filter plates ( Millipore , Billerica , MA , USA ) according to the protocol of Bender MedSystem . Beads were analyzed on flow cytometer LSR II ( BD Biosciences , San Jose , CA , USA ) . Concentrations of cytokines were determined by Flow Cytomix Pro 2 . 4 software . The limit of detection of each analyte was determined to be for GM-CSF 12 . 2 pg/ml , CCL2/MCP-1 42 pg/ml , CCL7/MCP-3 1 . 4 pg/ml , CCL3/MIP-1α 1 . 8 pg/ml , CCL4/MIP-1β 14 . 9 pg/ml , CCL5/RANTES 6 . 1 pg/ml , TNF-α2 . 1 pg/ml respectively . DNA was isolated from tails using a standard proteinase procedure . The strain CcS-11 differs from BALB/c at STS-derived regions on eight chromosomes [32] . These differential regions were typed in the F2 hybrid mice between CcS-11 and BALB/c using 14 microsatellite markers ( Research Genetics , Huntsville , AL , and Generi Biotech , Hradec Králové , Czech Republic ) : D1Mit403 , D3Mit45 , D7Mit25 , D7Mit18 , D7Mit282 , D7Mit259 , D8Mit85 , D10Mit46 , D10Mit12 , D12Mit37 , D16Mit73 , D19Mit51 , D19Mit60 and D19Mit46 ( Table S1 ) . The average distance between any two markers in the chromosomal segments derived from the strain STS or from the nearest BALB/c derived markers was 8 . 7 cM . DNA was amplified in a 20-µl PCR reaction with 0 . 11 µM of forward and reverse primer , 0 . 2 mM concentration of each dNTP , 1 . 5 mM MgCl2 ( except marker D7Mit259 , for which the optimal concentration was 2 . 5 mM ) , 50 mM KCl , 10 mM Tris-HCl ( pH 8 . 3 ) , and 0 . 5 U of Perfect Taq Red Polymerase ( Central European Biosystems , Prague , Czech Republic ) and approximately 40 ng of tail DNA . PCR reaction was performed using the DNA Engine Dyad® Peltier Thermal Cycler ( Bio-Rad , Hercules , CA ) , according to the following scheme: an initial hot start 3 min at 94°C , followed by 40 cycles of 94°C for 30 s for denaturing , 55°C for 60 s for annealing ( except marker D7Mit259 , for which optimal Ta = 52°C ) , 72°C for 60 s for elongation , and finally 3 min at 72°C for final extension . Each PCR product was electrophoresed in 3% agarose gel containing 80% of MetaPhor® Agarose ( Cambrex Bio Science Rockland , Inc . , Rockland , ME ) and 20% of UltraPure™ Agarose ( Invitrogen , Carlsbad , CA ) for 15 min to 2 h at 150 V . To map precisely Tbbr2 on STS derived segment of strain CcS-11 on proximal part of chromosome 12 [32] we used 8 microsatellite markers: D12Mit10a , D12Mit11 , D12Mit209 , D12Mit182 , D12Mit104 , D12Mit240 , D12Mit170 , Dtnb ( dystrobrevin , beta ) and 4 SNPs; rs48212577 , rs4229232 , rs50154157 and rs50776991 ( Generi Biotech , Hradec Králové , Czech Republic ) . The conditions of PCR reaction were as described in the section Genotyping of F2 mice . Polymorphism of SNPs was tested by restriction analysis after PCR reaction using following restriction enzymes ( New England BioLabs , Ipswich , MA ) : HpyAV for rs48212577 ( 14 , 13 µl of PCR product , 2 U ( 1 µl ) of HpyAV , 1 . 7 µl of 10x NEB buffer 4 [200 mM Tris-acetate , 500 mM Potassium Acetate , 100 mM Magnesium Acetate , 10 mM Dithiothreitol , pH 7 . 9] , 0 . 17 µl of 10 mg/ml BSA ( bovine serum albumin ) , 37°C , o/n ) ; HinfI for rs4229232 ( 14 . 8 µl of PCR product , 5 U ( 0 , 5 µl ) of HinfI , 1 . 7 µl of 10x NEB buffer 4 , 37°C , o/n ) ; BsmFI for rs50154157 ( 14 , 13 µl of PCR product , 2 U ( 1 µl ) of BsmFI , 1 . 7 µl of 10x NEB buffer 4 , 0 . 17 µl of 10 mg/ml BSA , 65°C , o/n ) , and Tsp509I for rs50776991 ( 14 , 8 µl of PCR product , 2 U ( 0 , 5 µl ) of Tsp509I , 1 . 7 µl of 10x NEB buffer 1 [100 mM Bis-Tris-propane-HCl , 100 mM MgCl2 , 10 mM Dithiothreitol , pH 7 . 0] , 65°C , o/n ) . The products were electrophoresed in 3% agarose gel containing 80% of MetaPhor® Agarose ( Cambrex Bio Science Rockland , Inc . , Rockland , ME ) and 20% of UltraPure™ Agarose ( Invitrogen , Carlsbad , CA ) for 15 min to 2 h at 150 V . For the strain pattern analyses , differences in survival after T . b . brucei infection were compared between the RC strains CcS-5 , CcS-11 , CcS-16 and CcS-20 and the parental strains BALB/c and STS by Kaplan-Meier estimator using the PROC LIFETEST procedure of the SAS 9 . 1 statistical package for Windows ( SAS Institute , Inc . , Cary , NC ) . The differences between strains BALB/c , STS and CcS-11 in splenomegaly , hepatomegaly and body weight change were evaluated by the analysis of variance ( ANOVA ) and Newman-Keuls multiple comparison using the program Statistica for Windows 8 . 0 ( StatSoft , Inc . , USA ) . Strain and age were fixed factors and individual experiments were considered as a random parameter . The differences in parameters between strains were evaluated using the Newman-Keuls multiple comparison test at 95% significance . Differences between strains BALB/c , STS and CcS-11 in chemokine and cytokine levels were calculated by Mann Whitney U test . Linkage of microsatellite markers with survival after T . b . brucei infection in F2 hybrids was examined by analysis of variance ( ANOVA , PROC GLM statement of the SAS 8 . 2 for Windows ( SAS Institute , Inc . , Cary , NC ) ) . Log10 transformation was performed in order to obtain normal distribution . The effect of each marker , sex and experiment on mouse survival was tested . Each individual marker and its interactions with other markers and sex or experiment were subjected to ANOVA . A backward elimination procedure [33] was used . The first round of the backward elimination procedure results in a list of significant markers and a list of interactions . This list ( the markers and interactions with P value smaller than 0 . 05 ) is the input for the second round of ANOVA and the marker ( or interaction ) bearing the highest P value ( if P>0 . 05 ) is eliminated . The backward elimination procedure was repeated till the final set of significant markers and interactions was obtained . To obtain genome-wide significance values ( corrected P ) , the observed P-values ( αT ) were adjusted according to Lander and Schork [34] using the formula: where G = 1 . 75 Morgan ( the length of the segregating part of the genome: 12 . 5% of 14 M ) ; C = 8 ( number of chromosomes segregating in cross between CcS-11 and BALB/c , respectively ) ; ρ = 1 . 5 for F2 hybrids; h ( T ) = the observed statistic ( F ratio ) .
We have compared survival of strains BALB/c , STS/A , CcS-5 , CcS-11 , CcS-16 and CcS-20 after infection with T . b . brucei . Parental strains BALB/c and STS did not differ in survival . RC strains CcS-5 , CcS-16 , and CcS-20 did not significantly differ in survival from the background parental strain BALB/c . CcS-11 mice exhibit shorter survival than BALB/c mice after challenge with T . b . brucei infection ( P = 0 . 0032 females , P = 0 . 000093 both sexes ) ( Figure 1 A , B ) . Some BALB/c mice survived up to 16 days , whereas none of the CcS-11 mice lived longer than 10 days . Strain CcS-11 was therefore selected for further analysis . We have compared splenomegaly , hepatomegaly , changes of body weight ( Figure 2 ) , and differences in cytokine and chemokine levels ( Figure 3 ) in females of background strain BALB/c , donor strain STS and RC strain CcS-11 . Non-infected mice do not differ in spleen to body weight ratio ( Figure 2A ) and in changes of body weight ( Figure 2C ) , whereas liver to body weight was higher in BALB/c than in both STS ( P<0 . 0000001 ) and CcS-11 ( P<0 . 0000001 ) ( Figure 2B ) . Infection led to a significant enlargement of spleens ( BALB/c: P = 0 . 000001; STS: P = 0 . 000004; CcS-11: P = 0 . 000001 ) and livers ( BALB/c: P = 0 . 000001; STS: P = 0 . 0007; CcS-11: P = 0 . 000001 ) in all tested strains and to decrease of body weight ( BALB/c: P = 0 . 00068; STS: P = 0 . 000044; CcS-11: P = 0 . 00037 ) in comparison with non-infected mice . BALB/c exhibited higher splenomegaly than STS ( P<0 . 0000001 ) and CcS-11 ( P<0 . 0000001 ) and also higher hepatomegaly than both STS ( P<0 . 0000001 ) and CcS-11 ( P<0 . 0000001 ) . Differences in changes in body weight during the infection were observed between BALB/c and STS ( P = 0 . 0080 ) . Serum levels of CCL7/MCP-3 , CCL2/MCP-1 , CCL3/MIP-1α , CCL4/MIP-1β , CCL5/RANTES , GM-CSF and TNF-α were measured at day 2 and 10 p . i . and compared with cytokines and chemokines serum levels of non-infected control mice . We did not observe any differences in GM-CSF levels between infected and non-infected mice . At day 2 p . i . all tested strains had increased levels of CCL7/MCP-3 in comparison with controls and in STS was also observed increased level of CCL5/RANTES . At day 10 p . i . all three tested strains exhibited increase of CCL7/MCP-3 , CCL2/MCP-1 , CCL3/MIP-1α , CCL4/MIP-1β , CCL5/RANTES , and TNF-α ( Table S2 , Figure 3 , Figure S1 ) . In infected mice , strain differences from BALB/c were observed in serum levels of CCL2/MCP-1 , CCL3/MIP-1α and CCL7/MCP-3 ( Figure 3 ) . STS mice had lower serum level of CCL2/MCP-1 day 2 p . i . ( P = 0 . 032 ) ( Figure 3A ) and higher level of CCL3/MIP-1α day 10 p . i . ( P = 0 . 028 ) ( Figure 3B ) than BALB/c . STS mice had lower serum level of CCL7/MCP-3 than BALB/c day 2 p . i . ( P = 0 . 019 ) , whereas CcS-11 had lower serum level of this chemokine than the background parental strain BALB/c day 10 p . i . ( P = 0 . 013 ) ( Figure 3C ) . We examined survival after T . b . brucei infection in 169 F2 hybrids between the strains BALB/c and CcS-11 . The strain CcS-11 differs from BALB/c in the genetic material at 8 chromosomes that were received from STS [32] . These differential STS-derived segments were genotyped in the F2 hybrid mice using 14 microsatellite markers . Statistical analysis of linkage revealed four genetic loci that influence survival after T . b . brucei infection . Two of these loci have individual effects ( Table 1 ) ; the other two operate in mutual non-additive interaction ( Table 2 ) . The effects of all loci were more expressed in females than in males . Two loci , Tbbr1 ( Trypanosoma brucei brucei response 1 ) linked to D3Mit45 ( corrected P value = 0 . 0494 females; corr . P = 0 . 267 both sexes ) and Tbbr2 linked to D12Mit37 ( corrected P value = 0 . 0224 females; corr . P value = 0 . 0583 both sexes ) have main effects on survival that are not dependent on or influenced by interaction with other genes ( main effects ) ( Table 1 , Figure 4 A , B , C , D ) . These loci have in CcS-11 an opposite effect on the studied trait . The homozygosity for the STS allele of Tbbr1 ( SS ) determines about 4 days longer survival than homozygosity of the BALB/c allele ( CC ) , whereas homozygosity for the STS allele of Tbbr2 ( SS ) is associated with about three days shorter survival than the homozygosity of the BALB/c allele ( CC ) . We have also observed a suggestive linkage of survival to D8Mit85 ( corrected P value = 0 . 0542 females; corr . P = 0 . 0994 both sexes ) , heterozygotes had the shorter survival ( Table 1 ) . Tbbr3 linked to D7Mit282 influences survival in interaction with Tbbr4 linked to D19Mit51 ( corrected P = 0 . 0332 females; corr . P = 0 . 0430 both sexes ) . F2 mice with homozygous BALB/c ( CC ) alleles at Tbbr3 and STS ( SS ) alleles at Tbbr4 or homozygous for STS allele at Tbbr3 and homozygous for BALB/c alleles in Tbbr4 have the shorter survival in comparison with other combinations of Tbbr3 and Tbbr4 STS and BALB/c alleles ( Table 2 , Figure 4 E , F ) . A suggestive linkage was detected in females in interaction of D8Mit85 and D19Mit60 ( corrected P = 0 . 0555 ) , shorter survival has been observed in mice heterozygous both in D8Mit85 and D19Mit60 ( Table 2 ) . Tbbr2 maps in CcS-11 to a rather short STS-derived region on proximal part of chromosome 12 , with previously estimated length of 6 cM [32] , [35] . In order to map this locus more precisely , we genotyped this region with 8 microsatellite markers and 4 SNPs . This led to precision mapping of Tbbr2 to a region with a maximal length of 2 . 15 Mb that contains only 26 genes ( Figure 5 ) .
CcS-11 differs in susceptibility to trypanosomiasis from both parental strains . The background strain BALB/c is susceptible to T . b . brucei . This is in agreement with findings of other research groups [15] , [16] . Donor strain STS does not differ in survival from the background strain BALB/c , however the strain CcS-11 that contains a set of approximately 12 . 5% genes of the donor strain STS and 87 . 5% genes of the background strain BALB/c and it has shorter survival after infection than either parent . The elements in the BALB/c genome that work in interaction with STS disease response loci can be identified in linkage tests as gene-gene interactions . For example , in the interaction of Tbbr3 and Tbbr4 , the survival of mice with homozygous BALB/c alleles at both loci , or homozygous STS alleles at both loci is longer than of mice that are homozygous for BALB/c allele at one locus and homozygous for the STS allele at the second ( Table 2A ) . The fine mapping and molecular identification of Tbbr4 will reveal one of BALB/c elements that can modify the effect of STS genes . The RC strains are especially suitable to detect such interactions [36] . The observations of progeny having a phenotype , which is beyond the range of the phenotype of its parents are not rare in traits controlled by multiple genes . Some F2 hybrids derived in cross between trypanotolerant African N'Dama ( Bos taurus ) and trypanosusceptible Kenya Boran ( Bos indicus ) cattle differed from both parents and contained less T . congolense parasites than any of them [37] . Similarly , mouse RC strain OcB-9 differs from both parental strains O20 and B10 . O20 in response to alloantigens [38] , several RC strains exhibit in some parameters higher susceptibility to Leishmania major than both parental strains BALB/c and STS [39] , and analysis of gene expression from livers in chromosome substitution strains ( background strain C57BL/6 , donor strain A/J ) revealed that only 438 out of 4209 expression QTLs were inside the parental range [40] . These observations are due to multiple gene-gene interactions of QTLs , which in new combinations of these genes in RC strains , F2 hybrids or in chromosomal substitution strains can lead to appearance of new phenotypes that exceed their range in parental strains . Also , with traits controlled by multiple loci , the parental strains often contain susceptible alleles at some of them and resistant on others , and some progeny may receive predominantly susceptible alleles from both parents . We have compared in strains BALB/c , STS and CcS-11 splenomegaly , hepatomegaly , changes of body weight ( Figure 2 ) , and cytokine and chemokine levels ( Figure 3 ) . However , none of these measurements explains differences in survival between BALB/c and CcS-11 . BALB/c and CcS-11 also do not differ in parasitemia day 10 p . i . ( data not shown ) . Thus , the identification of Tbbr1-Tbbr4 genes is needed to provide information about the mechanisms controlling differences in survival between these strains . We have detected four loci that in the strain CcS-11 control survival after T . b . brucei infection and mapped them with a precision of 1 cM–25 cM ( Tables 1 , 2 , Figure 5 ) . Usually , a standard inbred-strain mapping experiment using F2 hybrids will map a QTL onto a 20- to 40-cM interval [41] . Using advanced intercross lines [20] , [22] the susceptibility loci Tir1 and Tir3c to T . congolense were mapped with a 95% confidence interval to 1 . 3 and 2 . 2 cM , respectively . In the RC strains the donor-derived segments of medium length ( 5–25 cM ) comprise 54% of donor genome [42] . However , RC strains can carry on some chromosomes very short segments of donor strain origin . This feature of the RCS system allowed us previously to narrow the location of Lmr9 ( Leishmania major response 9 ) on chromosome 4 to a short segment of 1 . 9 cM without any additional crosses [43] . The short length of this segment , which controls levels of serum IgE in L . major infected mice , enabled us to map a human homolog of this locus on human chromosome 8 and show that it controls susceptibility to atopy [44] . Our data show sex influence on survival as after correction for the genome-wide testing significance of the Tbbr loci was detected only in females or in the whole tested group . This observation can be related to the influential role of sex hormones in control of parasitic infections by their ability to modulate different components of both the innate and adaptive immune responses [45] , [46] . Greenblatt and Rosenstreich [47] analyzed resistance of the 10 inbred mouse strains and two sets of F1 hybrids to infection with T . b . rhodesiense . C3H/HeN , C3H/HeJ , CBA/J , BALB/c and CBA/CaJ were highly susceptible , with mean survival times of less than 22 days , and did not exhibit differences in survival between males and females , whereas in more resistant strains CBA/N , A . CA , C57BL/6J , C57BL/KsJ , C57BL/10SnJ , ( BALB/c x C57BL/6 ) F1 and ( C57BL/6× BALB/c ) F1 female mice were more resistant than males . These data support the finding of different genetic regulation of susceptibility to T . brucei in males and females in certain genetic combinations . Genes controlling infections that appear to be sex dependent have been observed also with other pathogens . For example , Rmp4 ( resistance to mouse pox 4 ) controls susceptibility to ectromelia virus in female mice only [48] and Hrl ( herpes resistance locus ) exhibits higher influence on susceptibility to Herpes simplex virus in male than in female mice [49] . Sex specific QTLs influence also susceptibility to Theiler's murine encephalomyelitis virus-induced demyelination: loci Tmved7 and 8 affect male mice only , whereas locus Tmved9 controls susceptibility only in females . Locus Tmved6 operates both in females and males , but it has an opposite effect on disease susceptibility in males and females [50] . Lmr20 influenced IgE level in L . major infected females , but not in males [35] . QTLs Cnes1 and Cnes2 were associated with high pulmonary Cryptococcus neoformans burden in females , whereas Cnes3 was associated with fungal pulmonary burden in male mice [51] . QTL on chromosome 17 controls susceptibility to pulmonary infection with Chlamydia pneumoniae , but has much stronger effect in males , whereas QTL on chromosome 5 controls susceptibility only in female mice [52] . In humans , for example the IL9 genetic polymorphism ( rs2069885 ) has an opposite effect on the risk of severe respiratory syncytial virus bronchiolitis in boys and girls [53] . In the present study , we were able to precision map Tbbr2 to 2 . 15 Mb . This segment contains 26 genes , 12 of them are either predicted genes or cDNA sequences ( Table 3 ) . Public databases ( http://www . ncbi . nlm . nih . gov; http://www . informatics . jax . org and http://biogps . gnf . org/#goto=welcome ) show that some of these genes are in non-infected mice expressed in tissues such as liver , spleen , and brain ( Table 3 ) . These organs are in infected mice affected by parasite [6] , [7] . There is no obvious candidate gene and there are only indirect indications about the possible role of some of these genes , such as Dnmt3a ( DNA methyltransferase 3a ) [54] , [55] , Pomc ( pro-opiomelanocortin-alpha ) [56] , [57] , Adcy3 ( adenylate cyclase 3 ) [58] , and Ncoa1 ( nuclear receptor coactivator 1 ) [59] in immune response against Trypanosoma . Tbbr1 is localized in the distal part of chromosome 3 . Potential candidate genes in this locus are Ptgfr ( prostaglandin F receptor ) [MGI:97796] and Ptger3 ( prostaglandin E receptor 3 ( subtype EP3 ) ) [MGI:97795] , as prostaglandins play a suppressive role in infection with African trypanosomes [60] . Tbbr3 on chromosome 7 and Tbbr4 on chromosome 19 map near to the genes Cd19 [MGI:88319] and Cd5 [MGI:88340] , respectively , that code markers of B lymphocytes . CD19 is a B-lineage antigen , present on both B-1 and B-2 cells [61] . It was shown that in murine experimental T . brucei trypanosomiasis , B-cells were crucial for periodic peak parasitemia clearance and survival of host [16] . CD5+ subpopulation of B-1 cell has been found to be stimulated by different Trypanosoma species: T . cruzi [62] , T . b . evansi [63] , and T . congolense [64] . These B-cells were the main source of antibodies reactive with non-parasite antigens in T . congolense-infected cattle [64] . However , genes that are presently not considered as possible candidates might cause the effects of some or all Tbbr loci . Moreover , not only genes , but also noncoding RNAs in Tbbr loci region may influence the outcome of infection [65] . Some genes , for example Slc11a1 ( solute carrier family 11 ( proton-coupled divalent metal ion transporters ) , member 1 ) or Lyst ( lysosomal trafficking regulator ) /beige have been found to control susceptibility to several pathogens ( reviewed in ref [29] ) . Tbbr2 might be potentially involved also in control of Leishmania major , as it overlaps with locus Lmr22 ( Leishmania major response 22 ) , which in interaction with Lmr5 controls serum IL-4 in L . major infected mice [35] , whereas Tbbr3 on chromosome 7 maps near to Ity7 ( immunity to S . typhimurium 7 ) [66] . Control of susceptibility to T . congolense is exercised by loci on chromosomes 17 , 5 and 1 [19] , [22] , whereas susceptibility to T . cruzi is determined by loci on chromosomes 17 and 5 [23] . Influence of loci on chromosomes 17 and 5 could not be tested in the present cross , as CcS-11 does not carry STS-derived segments on these chromosomes [32] . STS-derived region present on chromosome 1 of CcS-11 overlaps with Tir3c [22] , however we did not detect influence of this segment on susceptibility to T . b . brucei . This might be caused either by differences in regulation of immunity against the sub-genus T . ( Nannomonas ) congolense and the subgenus T . ( Trypanozoon ) brucei , or because the Tir3c , which was detected in a cross between strains C57BL/6J and BALB/c [19] and C57BL/6J and A/J [22] is not polymorphic between strains BALB/c and STS tested in this paper . Therefore the possible effects of Tbbr loci in infection with other Trypanosoma species have yet to be established . In summary , this study represents the first definition of genetic loci controlling susceptibility to T . b . brucei infection . One of them , Tbbr2 is precisely mapped to the segment that contains only 26 genes , which will facilitate the identification of the candidate gene . T . brucei subspecies cause sleeping sickness in humans and affect also all livestock , with particularly severe effects in horses and dogs [1] . Thus , the definition of genes controlling anti-parasite responses might also permit a better understanding of pathways and genetic diversity underlying the disease phenotypes in humans and domestic animals .
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Trypanosoma brucei are extracellular protozoa transmitted to mammalian host by the tsetse fly . They developed several mechanisms that subvert host's immune defenses . Therefore analysis of genes affecting host's resistance to infection can reveal critical aspects of host-parasite interactions . Trypanosoma brucei brucei infects many animal species including livestock , with particularly severe effects in horses and dogs . Mouse strains differ greatly in susceptibility to T . b . brucei . However , genes controlling susceptibility to this parasite have not been mapped . We analyzed the genetic control of survival after T . b . brucei infection using CcS/Dem recombinant congenic ( RC ) strains , each of which contains a different random set of 12 . 5% genes of their donor parental strain STS/A on the BALB/c genetic background . The RC strain CcS-11 is even more susceptible to parasites than BALB/c or STS/A . In F2 hybrids between BALB/c and CcS-11 we detected and mapped four loci , Tbbr1-4 ( Trypanosoma brucei brucei response 1–4 ) , that control survival after T . b . brucei infection . Tbbr1 ( chromosome 3 ) and Tbbr2 ( chromosome 12 ) have independent effects , Tbbr3 ( chromosome 7 ) and Tbbr4 ( chromosome 19 ) were detected by their mutual inter-genic interaction . Tbbr2 was precision mapped to a segment of 2 . 15 Mb that contains 26 genes .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"animal",
"genetics",
"genetics",
"biology",
"microbiology",
"host-pathogen",
"interaction",
"genetics",
"of",
"disease",
"genetics",
"and",
"genomics"
] |
2011
|
Genetic Control of Resistance to Trypanosoma brucei brucei Infection in Mice
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Ethiopia is assumed to have the highest burden of podoconiosis globally , but the geographical distribution and environmental limits and correlates are yet to be fully investigated . In this paper we use data from a nationwide survey to address these issues . Our analyses are based on data arising from the integrated mapping of podoconiosis and lymphatic filariasis ( LF ) conducted in 2013 , supplemented by data from an earlier mapping of LF in western Ethiopia in 2008–2010 . The integrated mapping used woreda ( district ) health offices’ reports of podoconiosis and LF to guide selection of survey sites . A suite of environmental and climatic data and boosted regression tree ( BRT ) modelling was used to investigate environmental limits and predict the probability of podoconiosis occurrence . Data were available for 141 , 238 individuals from 1 , 442 communities in 775 districts from all nine regional states and two city administrations of Ethiopia . In 41 . 9% of surveyed districts no cases of podoconiosis were identified , with all districts in Affar , Dire Dawa , Somali and Gambella regional states lacking the disease . The disease was most common , with lymphoedema positivity rate exceeding 5% , in the central highlands of Ethiopia , in Amhara , Oromia and Southern Nations , Nationalities and Peoples regional states . BRT modelling indicated that the probability of podoconiosis occurrence increased with increasing altitude , precipitation and silt fraction of soil and decreased with population density and clay content . Based on the BRT model , we estimate that in 2010 , 34 . 9 ( 95% confidence interval [CI]: 20 . 2–51 . 7 ) million people ( i . e . 43 . 8%; 95% CI: 25 . 3–64 . 8% of Ethiopia’s national population ) lived in areas environmentally suitable for the occurrence of podoconiosis . Podoconiosis is more widespread in Ethiopia than previously estimated , but occurs in distinct geographical regions that are tied to identifiable environmental factors . The resultant maps can be used to guide programme planning and implementation and estimate disease burden in Ethiopia . This work provides a framework with which the geographical limits of podoconiosis could be delineated at a continental scale .
Podoconiosis is a form of elephantiasis that predominantly affects barefoot subsistence farmers in areas with red volcanic soil . It is characterized by bilateral swelling of the lower legs with mossy and nodular changes to the skin , and causes considerable disability . The aetiology is not fully understood; however , the current evidence suggests that mineral particles from irritant volcanic soils have a role , with some families having an additional genetic susceptibility to the condition [1 , 2] . In the last five years , there has been increased recognition of the disease and its importance . The World Health Organization ( WHO ) included podoconiosis in the list of neglected tropical diseases ( NTDs ) in 2011 [3] . The greatest burden of podoconiosis globally is assumed to occur in Ethiopia , and in 2013 Ethiopia included podoconiosis in its national NTD master plan [4] . Control of the disease is focused on early and consistent indoor and outdoor shoe wearing and regular foot hygiene for prevention , as well as simple lymphoedema management including foot hygiene , bandaging , massage , shoe and sock wearing and , in extreme cases , minor surgery for morbidity management [2 , 5] . To guide the implementation of these measures it is essential to have a detailed understanding of the geographical distribution of podoconiosis . The first attempt to map the distribution of podoconiosis was based on school and market surveys conducted by Price in 1974 [6 , 7] . Although this work provides an important contribution , it is limited by the inclusion of non-representative populations because it was based on market-based sampling and counted all lymphoedema cases without excluding other potential causes . Moreover , Ethiopia has undergone economic and social transformation since the 1970s , and these economic changes will have affected shoe wearing habits , foot hygiene and housing conditions , which , in turn , may influence the risk of developing podoconiosis [8] . The more recently conducted studies [9–13] have typically been conducted in areas known to be endemic for the disease and at local scales [14] . In order to guide the Ethiopia NTD master plan , we conducted the first nationwide integrated mapping of podoconiosis and lymphatic filariasis ( LF ) between June and October 2013 . Previous work described the methodology of the integrated mapping [15] , and investigated the epidemiology and individual and household risk factors [8] . Building on this work , the aim of the present paper is to ( i ) describe the geographical distribution of podoconiosis across Ethiopia , ( ii ) identify environmental factors associated with the occurrence and prevalence of podoconiosis , ( iii ) define the spatial limits of disease occurrence , and ( iv ) estimate the population living in areas at risk from the disease .
Ethical approval for the study was obtained from the Institutional Review Board of the Medical Faculty , Addis Ababa University , the Research Governance and Ethics Committee of Brighton & Sussex Medical School ( BSMS ) , and ethics committees at the Ethiopian Public Health Institute ( EPHI ) and Liverpool School of Tropical Medicine . Individual written informed consent was obtained from each participant ≥18 years of age . For those individuals <18 years old , consent was obtained from their parents or guardian and the participant themselves provided informed assent . Confirmed W . bancrofti infection was treated using albendazole ( 400 mg ) and ivermectin ( 200 μg/kg body weight or as indicated by a dose-pole ) according to WHO recommendations . For those with lymphoedema , health education was given about morbidity management . Ethiopia is located in the Horn of Africa . The total population in 2013 is estimated to be 86 . 6 million [16 , 17] , with the majority of the population living in rural areas . Ethiopia has a federal system of administration with nine regional states and two city administration councils ( Fig 1A ) [18] . The country has three broad ecologic zones , based on topography: the “kola” or hot lowlands , the “weyna dega” or midland and the “dega” or the cool temperate highlands[19] . Altitudinal variation in temperature gives rise to a variety of vegetation types and suitability of land for agriculture [16] . The data originated from two sources: the nationwide integrated LF and podoconiosis mapping in 2013 and a LF mapping survey in western Ethiopia , 2008–2010 . The details of each survey are provided elsewhere [8 , 15 , 20] . In brief , the 2013 survey was conducted in 659 districts ( woredas ) and included 1 , 315 villages . During the survey , individuals underwent a rapid-format antigen test for diagnosis of LF ( immunochromatographic card test [ICT] ) and clinical history and physical examination for podoconiosis . Further details are given elsewhere [15] . The 2008–2010 survey included 116 districts located in five regional states in western Ethiopia , conducted by a team from Addis Ababa University . Thirty-seven of the 116 districts were found to be endemic for LF . Cases of podoconiosis were extracted from this data set , based on expert opinion . Presence of lymphoedema cases in districts not endemic for LF , without sign or symptoms of other potential causes were considered podoconiosis cases ( see Supporting Information S1 ) . All 37 districts endemic for LF were excluded from data extraction to avoid misclassification of cases , while podoconiosis data were extracted from the remaining 79 [20] . Combined , the two surveys contributed 1 , 442 clusters from 775 districts of Ethiopia . The aggregation of the data was conducted by combining the point data in each administrative unit and calculating the prevalence at district level: total number in district with disease divided by total number examined in the district . The elevation data at 90 m resolution were derived from a gridded digital elevation model produced by the Shuttle Radar Topography Mission ( SRTM ) [21] , and these data were processed to calculate slope in degrees . The mean atmospheric temperature and annual mean precipitation at 30-arcsecond ( approx . 1 km ) resolution were downloaded from the WorldClim database for the period 1950–2000 [22] . A suite of raster surfaces containing values of Enhanced Vegetation Index ( EVI ) were obtained from the African Soil Information System ( AfSIS ) project [23] . Soil data including silt , clay and sand content , dominant soil type and soil-pH at 1 km2 resolution were downloaded from the ISRIC-World Soil Information project[24] . A gridded map of soil texture included in the Harmonized Soil Map of the World at 1 km2 resolution was obtained from the Africa Soil Information Service ( AfSIS ) , which is developing continent-wide digital soil maps for sub-Saharan African[24] . Straight line distance to water bodies was calculated using the data layers of water bodies produced by the SRTM at 250 m resolution[21] . Land cover type , according to the United Nations ( UN ) land cover classification system , was extracted from the qualitative global land cover map , produced at 300 m resolution from data collected by the environmental satellite ( ENVISAT ) mission’s Medium Resolution Imaging Spectrometer ( MERIS ) sensor[25] . Gridded maps of both population density and rural-urban classification for 2010 were obtained from the WorldPop project [26 , 27] and the Global Rural-Urban Mapping project ( GRUMP ) , respectively[28 , 29] . Finally , Aridity Index data were extracted from the Global-Aridity datasets ( CGIARCSI ) [30 , 31] . Survey and covariate data were linked in ArcGIS 10 . 1 ( Environmental Systems Research Institute Inc . [ESRI] Inc . , Redlands CA , USA ) based on the WGS-1984 Web Mercator projection at 1 km2 resolution . Bilinear interpolation was applied to resample numeric ( continuous ) raster data sets , whereas nearest neighbor interpolation was used with ordinal raster layers . Input grids were either extended or clipped to match the geographic extent of a land mask template of Ethiopia , and eventually aligned to it . The data were entered using a Microsoft Excel 2007 ( Microsoft Corporation , Redmond , WA ) spreadsheet and exported into STATA 11 . 0 for analysis ( Stata Corporation , College Station , TX , USA ) . Point prevalence maps were developed in ArcGIS 10 ( ESRI , Redlands , CA ) and covariate data extracted for each data point . Multicollinearity between the covariates was initially explored using cross-correlations and where correlation coefficients were >0 . 7 only non-linearly related covariates were included in the analysis ( S1 Text ) . Boosted Regression Tree ( BRT ) modelling[32 , 33] was used to identify the environmental factors associated with the occurrence of podoconiosis in Ethiopia . This approach has been effectively used in global mapping of dengue , LF , leishmaniasis and malaria vector mosquitos [34–37] and has superior predictive accuracy compared to other distribution models[38] . In brief , BRT modelling combines regression or decision trees and boosting in a number of sequential steps [32 , 33] . First , the threshold of each input variable that results in either the presence or the absence of podoconiosis is identified , allowing for both continuous and categorical variables and different scales of measurement amongst predictors [32] . Second , boosting is a machine-learning method that increases a model’s accuracy iteratively , based on the idea that it is easier to find and average many rough ‘rules of thumb’ , than to find a single , highly accurate prediction rule . Boosted Regression Tree utilizes data on both presence and absence of podoconiosis . Presence was defined as an area with at least one case in the two surveys and absence as an area with no cases in either survey . A selection of 16 environmental and climate covariates were included in a single BRT model in order to explore the relative importance of each covariate in explaining the occurrence of podoconiosis in Ethiopia . Four covariates ( land cover , soil type , soil texture , urban rural classification ) were excluded that showed little explanatory power ( <1% of regression trees used the covariate ) on the occurrence of podoconiosis . The retained covariates were used to build the final model included annual precipitation , elevation , population density , enhanced vegetation index , terrain slope , distance to water bodies , silt fraction and clay fraction . In order to obtain a measurement of uncertainty for the generated model , we fitted an ensemble of 120 BRT submodels to predict sets of different risk maps ( each at 1km x 1km resolution ) and these were subsequently combined to produce a single mean ensemble map and the relative importance of predictor variables was quantified . These contributions are scaled to sum 100 , with a higher number indicating a greater effect on the response . Marginal effect curves were plotted to visualize dependencies between the probability of podoconiosis occurrence and each of the covariates . To assess the association of covariates and high prevalence podoconiosis , the prevalence estimates were plotted against each environmental variable . This will help to identify the areas with very high prevalence and to prioritize interventions . BRT modelling and model visualization was carried out in R version 3 . 1 . 1 [39] using the packages raster [40]and dismo[41] . The resulting predictive map depicts environmental suitability for the occurrence of podoconiosis . In order to convert this continuous map into a binary map outlining the limits of podoconiosis occurrence , a threshold value of suitability was determined , above which the occurrence was assumed to be possible . Using the receiver operating characteristic ( ROC ) curve , a threshold value of environmental suitability was chosen such that sensitivity , specificity and proportion correctly classified ( PCC ) values were maximized . Finally , we estimated the number of individuals at risk by overlaying the binary raster dataset displaying the potential suitability for podoconiosis occurrence on a gridded population density map[26 , 27] and calculating the population in cells considered to be within the limits of podoconiosis occurrence . The 95% CI of the population at risk were calculated based on binary maps of the lower ( 2 . 5% ) and upper ( 97 . 5% ) bounds of the predicted probability of occurrence . The performance of each sub-model was evaluated using different statistics , including: proportion correctly classified [PCC] , sensitivity , specificity , Kappa [κ] and area under the receiver operator characteristics curve ( AUC ) . The mean and confidence intervals for each statistic were used to evaluate the predictive performance of the ensemble BRT model . In addition to ensemble approach to validation , an external validation was performed using data from 96 independent surveys conducted between 1969 and 2012 [6 , 7 , 9–12 , 42–44] which we previously identified through structured searches of the published and unpublished literature [14] . The AUC was used to assess the discriminatory performance of the predictive model , comparing the observed and predicted occurrence of podoconiosis at each historical survey . AUC values of <0 . 7 indicate poor discriminatory performance , 0 . 7–0 . 8 acceptable , 0 . 8–0 . 9 excellent and >0 . 9 outstanding discriminatory performance [45] .
Fig 2 shows the marginal effect of each covariate on the predicted suitability of occurrence for podoconiosis , averaging across the effects of all other variables , and its relative contribution to the final BRT model . Major predictors of the occurrence of podoconiosis were annual precipitation ( accounting for 30 . 7% of the variation explained by the model ) , elevation ( 22 . 6% ) , EVI ( 15 . 4% ) and population density ( 12 . 7% ) . Slope only contributed 8 . 2% to the predicted occurrence . Annual precipitation causes an increase in probability of occurrence starting from precipitation values of around 1 , 000 millimeters ( mm ) per year . High suitability for podoconiosis is also positively associated with elevation , increasing between 1 , 000–2 , 000 m asl and then sharply declining after 2 , 000 m asl . EVI is linearly correlated to the risk of podoconiosis occurrence up to 0 . 5 and declines sharply thereafter . Population density is negatively correlated with the probability of podoconiosis occurrence , with population density greater than 10 , 000 population/ km2 causing no effect on the probability of occurrence of podoconiosis . Although silt fraction and clay fraction contributed little to the final BRT model , the occurrence of podoconiosis was found to be associated with decreasing clay fraction and increasing silt fraction . Previous studies have indicated a relationship between the prevalence of podoconiosis and climate and environmental covariates ( including rainfall , altitude , temperature and soil type ) , and have characterized high prevalence areas using certain environmental variables [46] . In order to assess this relationship in Ethiopia , Fig 3 depicts the relationship between the environmental variables and the prevalence of podoconiosis . Thus , the distribution of podoconiosis is clearly bounded within an altitude range of 1 , 000–2 , 800 m asl EVI > 0 . 2 and annual precipitation >1 , 000 mm . Fig 4A presents the map of environmental suitability for podoconiosis and suggests that suitability is greatest in the central highlands of Amhara , Oromia and SNNP regional states . Absence of podoconiosis is predicted in Affar , Gambella and Somali regional states . A suitability cut-off of 0 . 49 with a sensitivity of 0 . 77 and specificity 0 . 86 provided the best discrimination between presence and absence records in the training data , and therefore this threshold value was used to reclassify the predictive risk map into a binary map outlining the potential environmental limits of occurrence ( Fig 5 ) . Uncertainty was calculated as the range of the 95% confidence interval in predicted probability of occurrence for each pixel ( Fig 4B ) indicating high uncertainty in the eastern part of Somali regional state . Cross-validation in the BRT ensemble model indicated high predictive performance of the BRT ensemble model with an AUC value of 0 . 84 ( 95% confidence interval ( CI ) : 0 . 84–0 . 85; standard deviation ( sd ) : 0 . 016 ) . External validation against historical data showed an excellent performance of the final fitted model to classify at-risk areas , with an AUC value of 0 . 89 ( CI 95%: 0 . 81–0 . 97 ) . The national population living in areas environmentally suitable for podoconiosis is estimated to be over 34 . 9 ( 95% CI: 20 . 2–51 . 7 ) million , which corresponds to 43 . 8% of Ethiopia’s population in 2010 . The largest portions of the population at risk were found in SNNP ( 68 . 1% ) Oromia ( . 48 . 0% ) and Amhara ( 49 . 6% ) ( Table 3 ) . We conducted a sensitivity analysis to determine the effect of the optimal suitability threshold ( 0 . 496 ) on the estimates of at-risk population . For that , we applied both a lower ( 0 . 3 ) and a higher ( 0 . 6 ) cut-off to dichotomize the final BRT model , and estimated the population living in suitable areas for podoconiosis based on these extreme thresholds . The total estimated population at risk would be 44 . 6 million ( 95%CI: 27 . 8–59 . 4 ) and 29 . 9 million ( 95%CI: 16 . 7–46 . 8 ) for the 0 . 3 and 0 . 6 cut-offs respectively .
The geographical distribution and burden of podoconiosis in Ethiopia is formidable and represents an important challenge to program planners and policy makers . Success in tackling this national problem is , in part , contingent on strengthening the evidence base on which control planning decisions and their impacts are evaluated . It is hoped that this mapping of contemporary distribution of podoconiosis will help to advance that goal . Empirical evidence has shown that podoconiosis management is effective in the early stages of the disease and improves clinical measures and the quality of life of patients [5] . If this management is found to be effective and cost-effective using more robust assessment , the next step will be scaling up interventions in all endemic districts . Prioritizing those districts with high prevalence would be a cost-effective approach . Scaling up prevention of podoconiosis through consistent shoe wearing is also vital . Studies in southern Ethiopia have identified cultural , financial and logistic barriers to shoe wearing [65 , 66] , and have enabled to develop a community messaging intervention to enhance prevention of podoconiosis . This intervention requires testing and adaptation to other endemic districts , possibly in combination with the hygiene promotion package of the 16-package Health Extension Program . In conclusion , our results provide a detailed description of the geographical distribution and environmental limits of podoconiosis in Ethiopia . This will enable optimal allocation of the limited resources available for podoconiosis control , permit evaluation of the impact of interventions in the future , and guide mapping of other potentially endemic countries and contribute to the global mapping of podoconiosis .
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Podoconiosis is a neglected tropical disease that results in swelling of the lower legs and feet . It is common among barefoot individuals with prolonged contact with irritant soils of volcanic origin . The disease causes significant social and economic burden . The disease can be prevented by consistent shoe wearing and regular foot hygiene . A pre-requisite for implementation of prevention and morbidity management is information on where the disease is endemic and the identification of priority areas . We undertook nationwide mapping of podoconiosis in Ethiopia covering 1442 communities in 775 districts all over Ethiopia . During the survey , individuals underwent a rapid-format antigen test for diagnosis of lymphatic filariasis and clinical history and physical examination for podoconiosis . A suite of environmental and climatic data and a method called boosted regression tree modelling was used to predict the occurrence of podoconiosis . Our survey results indicated that podoconiosis is more widespread in Ethiopia than previously estimated . The modelling indicated that the probability of podoconiosis occurrence increased with increasing altitude , precipitation and silt fraction of soil and decreased with more clay content and population density . The map showed that in 2010 , 34 . 9 million people lived in areas environmentally suitable for the occurrence of podoconiosis in Ethiopia .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Mapping and Modelling the Geographical Distribution and Environmental Limits of Podoconiosis in Ethiopia
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Whole Genome Shotgun ( WGS ) metagenomics is increasingly used to study the structure and functions of complex microbial ecosystems , both from the taxonomic and functional point of view . Gene inventories of otherwise uncultured microbial communities make the direct functional profiling of microbial communities possible . The concept of community aggregated trait has been adapted from environmental and plant functional ecology to the framework of microbial ecology . Community aggregated traits are quantified from WGS data by computing the abundance of relevant marker genes . They can be used to study key processes at the ecosystem level and correlate environmental factors and ecosystem functions . In this paper we propose a novel model based approach to infer combinations of aggregated traits characterizing specific ecosystemic metabolic processes . We formulate a model of these Combined Aggregated Functional Traits ( CAFTs ) accounting for a hierarchical structure of genes , which are associated on microbial genomes , further linked at the ecosystem level by complex co-occurrences or interactions . The model is completed with constraints specifically designed to exploit available genomic information , in order to favor biologically relevant CAFTs . The CAFTs structure , as well as their intensity in the ecosystem , is obtained by solving a constrained Non-negative Matrix Factorization ( NMF ) problem . We developed a multicriteria selection procedure for the number of CAFTs . We illustrated our method on the modelling of ecosystemic functional traits of fiber degradation by the human gut microbiota . We used 1408 samples of gene abundances from several high-throughput sequencing projects and found that four CAFTs only were needed to represent the fiber degradation potential . This data reduction highlighted biologically consistent functional patterns while providing a high quality preservation of the original data . Our method is generic and can be applied to other metabolic processes in the gut or in other ecosystems .
Whole Genome Shotgun ( WGS ) metagenomics is increasingly used to study the structure and functions of complex microbial ecosystems . Thanks to a huge research effort , these high-throughput approaches are constantly improving and have already provided valuable information . Recent achievements [1–3] for analyzing metagenomic reads have focused on genome or species reconstruction . They constitute an alternative to amplicon sequencing approaches such as 16S rDNA and provide potentially more robust tools for community structure assessment . As a complement to these taxonomy oriented approaches , WGS metagenomics allows the direct functional profiling of microbial communities through gene inventories of otherwise uncultured microbial communities . The functional annotation of these genes , as well as the evaluation of their abundances requires the mapping of metagenomic reads on one or several functional databases which may include annotated , de novo assembled gene catalogs . It is a challenging task for which bioinformatic tools are still being developed ( see [4] for a very recent review ) . As early as 2005 , Tringe et al . [5] demonstrated that functional binning could discriminate environments and determine the functional potential of microbial communities . Since then , several other groups used functional data to shed light on the processes taking place in the ecosystem , even in the absence of additional expression data ( see e . g . [6 , 7] ) , and developed frameworks to functionally characterize and compare ecosystems [4 , 8] . Investigating complex microbial communities from a purely functional point of view provides an interesting insight because their taxonomic diversity may be very high and varying in time while , owing to environmental selection pressure , their ecosystemic functions are often ubiquitous and much more stable in time . The functional profiling of microbial communities has motivated the adaptation of concepts borrowed from environmental and plant functional ecology to the framework of microbial ecology , such as community aggregated traits [9 , 10] , which can be quantified through abundance computation of specific genes or predefined pathways from WGS data . These taxon-free approaches allow the study of key processes at the ecosystem level and correlate environmental factors and ecosystem functions [11] . Choosing relevant descriptors for ecosystem functions raises several issues . Using arbitrarily predefined pathways helps simplifying the systems description by creating combined descriptors , however it may not reflect all the information in the data . On the other hand , unsupervised statistical analyses such as e . g . principal component analysis ( PCA ) are quite often used to extract low-dimensional description of these high-dimensional datasets . However , they do not exploit all the richness of available information about biological processes at the individual or collective level , and may lead to irrelevant or hardly interpretable results . To overcome these issues , we propose a novel model based approach , mixing satistical data analysis and system biology . We focus on specific ecosystemic metabolic processes , described by a possibly high number of genes or functional markers abundances . Our aim is to infer a limited number of combined functional traits characterizing these processes , as well as their intensity in the ecosystem . Our approach relies on Non-negative Matrix Factorization ( NMF ) , a popular machine learning technique in data and image analysis . NMF , together with PCA belongs to a wide family of data analysis methods designed to solve blind source separation problems . These questions are widely encountered and cover all situations where a mixture of several signals originating from different unknown sources is observed ( here gene abundances in samples ) and one wishes to separate them and identify the sources ( the combined functional traits ) and the mixture coefficients . NMF was previously used for genomic data mining in the context of microarray data analysis [12 , 13] . More recently , it was introduced in the context of metagenomics for reads binning [14] , or analyzing datasets from various ecosystems . In particular Jiang and co-authors used this approach as a “soft” clustering tool in different ecosystems . They studied pathway abundances in diverse environmental ecosystems [15] , compared habitats in marine ecosystems using protein families profiles [16] and human body sites using phylogenetic and functional data [17] . In [18] the idea of coupling source separation analyses with prior knowledge was developed in the context of bipartite network reconstruction , using prior knowledge on the network structure , and applied to the reconstruction of regulatory signals from microarray data . The originality of our work lies in the design of a constrained NMF approach where the constraints aim at selecting biologically relevant combined functional traits ( the sources ) to describe processes at the ecosystem level . The constraints are derived from available prior knowledge in a Bayesian perspective . Moreover , we propose a careful multicriteria selection procedure to select the relevant number of combined functional traits . As a proof of concept , we applied our approach to the modelling of ecosystemic functional traits of fiber degradation by the human gut microbiota based on 1408 samples of gene abundances .
Quantitative metagenomics allows the study of metabolic processes at the ecosystem level , by producing metagenes abundances with functional annotation . Taking advantage of this information , we consider a metabolic process occurring in an ecosystem . We assume that this process is described by a list of biochemical reactions , each associated to what we call a functional marker . A functional marker is a group of genes , or modular elements in genes , able to control the production of enzymes involved in a reaction . Typical functional markers are Kegg Orthologies ( KO ) . The abundance of a functional marker is defined as the sum of the abundances of all the metagenes identified as belonging to the group . Our first modelling hypothesis is the existence of patterns underlying functional markers abundances in the ecosystem . Indeed , within a microbial ecosystem , genes can be looked at in a hierarchical fashion . They are first associated on microbial genomes , which are further grouped at the ecosystem level into subcommunities of microorganisms . These subcommunities involve hundreds of different bacterial species linked by complex co-occurrences or interactions . Moreover they are influenced by environmental factors ( e . g . nutrients or temperature ) . The set of functional marker abundances within each of these subcommunities form a characteristic pattern in the ecosystem . This hypothesis is illustrated in the simplistic example of metabolic process in Fig 1 . Here , the observed functional marker abundances originate from three subcommunities , resulting in three patterns . The ecological interpretation of the first pattern ( green box ) could be a trophic chain in which metabolite X is produced by bacterial species of type 1 and used by bacterial species of type 3 as well as an association between bacterial species of type 1 and 2 for other ecological reasons which cannot be elucidated with the observation of this particular metabolic process only . Therefore functional marker abundances in samples should reflect this structure . Our second modelling hypothesis is motivated by the fact that even if in many microbial communities microbial species composition varies over time and samples , metabolic processes at the community level are ubiquitous and much more stable in time . Consequently , we assume that the patterns mentioned above are shared by all samples of the ecosystem , in variable proportions because they were selected by specific environmental constraints . For instance , for the gut ecosystem these include anaerobic conditions , temperature or digestion residue composition coming from the host diet . As illustrated on Fig 2 , when considering the abundances of functional markers in several samples , each of these patterns can be characterized by a line vector of functional marker frequencies , which we define to be a Combined Aggregated Functional Trait ( CAFT ) , in the meaning that it represents a combination of quantifiable characteristics ( functional markers ) of aggregated microbial communities . Following our hypotheses , functional marker abundances in each sample are modelled as mixtures of underlying CAFTs . For each sample i = 1 , … , n and marker j = 1 , … , r , the observed frequency aij of marker j in sample i is the sum of the contributions of all CAFTs . This leads to a i j ≃ ∑ l = 1 k w i l h l j ( 1 ) where hl = ( hl1 , … , hlr ) is the characterization in terms of r markers frequencies of the lth CAFT and wi = ( wi1 , … , wik ) is the vector of the intensities or weights of the k CAFTs in sample i . Note that each hl is defined up to a multiplication by a positive constant , provided that its weights in the samples are scaled accordingly . The ℓ1-scaled version hl/ ( ∑j hlj ) can be interpreted as the vector of relative frequencies of functional markers within the subcommunity characterized by trait l . In the rest of the paper , we denote the ( n × r ) data matrix by A , the ( k × r ) matrix whose lines are the hl by H and the ( n × k ) matrix whose lines are the wi by W . H will be called the trait matrix and W the weight matrix . By definition the entries of the weight and trait matrices W and H are assumed to be non-negative , we denote this condition by W ≥ 0 , H ≥ 0 . ( 2 ) We now show how prior knowledge on microbial genomics and metabolism can be exploited in order to select biologically relevant CAFTs . A first important consequence of our modeling framework is that genomic information can be translated into constraints on CAFTs structure . Indeed , by construction , the CAFT hl characterizes a gene pattern associated in each sample i to a subcommunity Cil . Therefore , in each of these subcommunities , the total abundance of any functional marker j is proportional to hlj . Thus , denoting nxj the number of functional markers j in the genome of a microorganism x in the ecosystem , it follows that ∑ x ∈ C i l n x j = w i l h l j . ( 3 ) From Eq ( 3 ) we observe that any constraint on the structure of the genomes ( the nxj ) satisfied by all x in the ecosystem induces a constraint on the CAFTs ( the hlj ) . As an example , let us assume that two markers j and j′ are such that for each microorganism in the ecosystem , if j′ is not present in its genome , then neither is j . This implies that for each microorganism x , there is a non-negative constant Rx , j , j′ satisfying n x j = R x , j , j ′ n x j ′ , ( 4 ) where we arbitrarily set Rx , j , j′ = 0 when nxj = nxj′ = 0 . Then as a consequence of Eq ( 3 ) we have for each CAFT l h l j ≤ δ h l j ′ , ( 5 ) where δ = supx ∈ Ecosystem Rx , j , j′ . In the absence of any other information , exploiting genomic information only could lead to a large number of constraints . For instance , constraining the ratio of abundances in pairs of markers as above requires assessing the existence of constants such that Eq ( 4 ) holds for r ( r − 1 ) ordered pairs , for each genome x . This is why we propose to combine genomic information with metabolic information , when possible , in order to select a limited but relevant set of constraints on the CAFTs . In order to do this , we formulate two additional assumptions . First , we assume that a subset of reactions in the metabolic process under consideration are well known , so that a list of metabolites that are consumed or produced by each reaction can be extracted . Second , we assume that metabolites are partitioned in two classes . The first one corresponds to metabolites that are known to be exported out of the cell , from experimental evidence or published data . The second one gathers metabolites that are actually known to stay within microbial cells , as well as metabolites for which this is strongly suspected . For convenience , these two classes will be called the extracellular and intracellular metabolites , although this may be misleading in some cases . Following similar steps as in eqs ( 3–5 ) , a subset M1 of the class of intracellular metabolites can be derived from genomic information on the microorganisms present in the ecosystem , as well as a positive constant δ1 , m for each m in M1 , such that in each CAFT hl the marker frequencies associated to the production or consumption of m should satisfy ∑ j producing m h l j ≤ δ 1 , m ∑ j' consuming m h l j ′ ( 6 ) In the same way , there is a subset M2 of intracellular metabolites such that for each m in M2 , each CAFT hl should satisfy ∑ j' consuming m h l j ′ ≤ δ 2 , m ∑ j producing m h l j ( 7 ) The intuitive idea behind such constraints is the following . Assuming that functional markers are relevant proxies of metabolic functions , it could be expected that if a genuinely intracellular metabolite is produced in the subcommunity characterized by a given CAFT it has to be used . This inspires constraint ( 6 ) , since it imposes that the number of markers associated to reactions that produce a given intracellular metabolite is zero if the number of markers associated to reactions that consume the same intracellular metabolite is zero . Conversely , it could be expected that a metabolite cannot be used if it is not produced , which is enforced by constraint ( 7 ) . This justifies the absence of constraints imposed on functional markers associated to reactions consuming or producing for the class of extracellular metabolites as the latter can actually be imported or exported outside bacterial cells in the ecosystem . However , the set of metabolites and reactions under consideration is defined in the context of a specific metabolic process , given the available knowledge . Thus it may be that in this process , some intracellular metabolites accumulate in the cells . It may also be that the selected reactions ignore metabolic routes , because they are unknown or correspond to other metabolic processes , that could produce or use the metabolites , and finally it may also happen that we were wrong in assuming that some poorly studied metabolites are intracellular . Consequently , depending on metabolites , either none , one or both constraints ( 6 ) and ( 7 ) may be satisfied . In the case where both constraints apply , the frequencies of markers associated to production and consumption are both either zero or positive with consumption over production marker frequency ratio lying in [1/δ1 , m , δ2 , m] . The theoretical construction of the two sets M1 , M2 and the positive constants δ1 , m and δ2 , m is detailed in the Materials and Methods section , as well as their practical approximation from known bacterial genomes . The set of constraints ( 6 ) and ( 7 ) can be expressed in matrix notation as F Δ H T ≤ 0 ( 8 ) where Δ is the set of parameters Δ = {δ1 , m , m ∈ M1} ∪ {δ2 , m , m ∈ M2} and FΔ is a coefficient matrix depending on Δ . Note that , although they include metabolic information and take the familiar form of linear inequality constraints ( 6 ) and ( 7 ) are constraints on gene frequencies in subcommunities , they are not constraints on metabolic fluxes . The constrained NMF algorithm as well as an unconstrained version were implemented on a toy model in order to illustrate the improvement in terms of CAFT analysis brought by the constraints . We considered a microbial ecosystem in which a metabolic process characterized by a simple 5 reactions graph takes place , each reaction being associated to a functional marker . The metabolic process was assumed to be structured in two CAFTs . The first CAFT characterizes a subcommunity in the ecosystem in which all the functional markers are present in equal proportion , while the second CAFT characterizes a subcommunity in which only three functional markers are present , in equal proportion . The metabolic graph and the true CAFTs are displayed in Fig 3A . Thus , the true CAFT composition matrix was taken equal to H ¯ = 1 1 1 1 1 1 0 0 1 1 We built a matrix A for 80 samples assumed to be mixtures of CAFT 1 and CAFT 2 . We first generated a matrix W ‾ of abundances of CAFT 1 and CAFT 2 for the 80 samples , with entries randomly drawn from a uniform distribution , and computed the corresponding marker count matrix B = W ‾ H ‾ . Noisy counts were generated from B according to the formula a i j = b i j ( 1 + 0 . 2 ϵ i j ) where the ϵij are independent , centered Gaussian variables with unit variance . In order to exemplify the construction of the constraints , we assumed that a set of three highly prevalent bacteria in this ecosystem was known . Bacteria are represented by the vectors x1 , x2 and x3 of functional markers counts in their genomes x 1 = ( 1 , 1 , 1 , 0 , 0 ) x 2 = ( 1 , 0 , 0 , 1 , 0 ) x 3 = ( 0 , 0 , 0 , 0 , 1 ) We assumed that based on published data on the physiology of these bacteria and possibly other bacteria in the ecosystem that are less prevalent , we knew that metabolites U , Z and T were extracellular . For instance , these data may come from in vitro culture experiments . We also assumed that the metabolites V , X and Y were not detected in the extracellular medium in these type of experiments , and considered them as intracellular by default . We used these representative genomes , together with the metabolic graph of Fig 3A and applied the rules described in the Materials and Methods section to build a set of constraints on the CAFTs . Considering metabolite V , it is known to be produced by the reaction associated to marker a and consumed by the reactions associated to b and d . The inspection of the three genomes led to n 1 a = 1 = 1 × ( n 1 b + n 1 d ) in x 1 , since n 1 b + n 1 d = 1 n 2 a = 1 = 1 × ( n 2 b + n 2 d ) in x 2 , since n 2 b + n 2 d = 1 n 3 a = 0 = 0 × ( n 3 b + n 3 d ) in x 3 , since n 3 b + n 3 d = 0 Therefore Eq ( 11 ) is satisfied on the three reference genomes . Thus , V belongs to M ˜ 1 with δ1V = max ( 1 , 1 , 0 ) = 1 . As described in the Materials and Methods section , we compensated for calibrating δ1V on a limited number of genomes instead of all the genomes in the ecosystem by applying a multiplicative factor of 2 , which seems sensible here since the highly prevalent genome have at most one copy of each functional marker in their genome . We obtained the following constraints h l 1 ≤ 2 ( h l 2 + h l 4 ) Similarly , X belongs to M ˜ 1 with hl2 ≤ 2hl3 . However , Y is not in M ˜ 1 as Eq ( 11 ) is not satisfied for genome x2 , here 33% of the reference genomes . In the same way , V and X belong to M ˜ 2 with hl2+hl4 ≤ 2hl1 and hl3 ≤ 2hl2 , and Y is discarded . We compared the CAFTs reconstructed using the constrained ( Fig 3B ) and unconstrained ( Fig 3C ) NMF inference . The NMF inference with constraints recovers the true CAFTs , while the unconstrained inference fails to provide relevant biological results . Besides , both constrained and unconstrained NMF provide a similar reconstruction of the data , as shown in Fig 3D . Indeed , from a mathematical point of view , the reconstructed CAFTs with and without constraints span the same linear space . Therefore , while both constrained and unconstrained NMF provide usable results for biological sample analysis , only the constrained version is relevant for CAFT inference .
We presented a new NMF based approach to investigate metabolic processes in microbial ecosystems using quantitative metagenomic data . As mentioned in the introduction , NMF techniques were previously used as a “soft” clustering tool to compare samples from various ecosystems , ranging from marine environment to human body ( [15 , 16] , [17] ) . However , our work focused on different aspects of NMF . Rather than clustering biological samples , we aimed at extracting biologically relevant features associated to a metabolic process , based on ecological modelling . Therefore our main interest , from the mathematical point of view , was the inference of a biologically interpretable trait matrix H rather than sample analysis through the weight matrix W . We proposed to model the functional diversity of an habitat-specific microbial ecosystem as a mixture of contributions from subcommunities , each characterized by a profile of functional marker frequencies . These profiles were named Combined Aggregated Functional Traits in reference to [10] , since they are measured at the community level from a random sample of microorganisms , regardless of their taxonomic identities . More precisely they result from both a combination of functional markers to form coherent and operative metabolic pathways , and an aggregation of individuals to form a subcommunity characterized by this trait . Our main contribution was the design of a novel constrained NMF model for CAFT inference . Standard NMF approaches were popularised for biological data analysis mainly because usual dimension reduction techniques such as truncated Singular Value Decomposition ( SVD ) , or the closely related Principal Componant Analysis ( PCA ) often lead to negative coefficients , which do not offer straightforward interpretations . However , while SVD or PCA provide a unique and well defined reduced dataset , optimal in the ℓ2-error sense , NMF is an intrinsically ill-posed problem with multiple solutions . Imposing constraints reflecting biological knowledge greatly helps to select a relevant solution . This was illustrated through the toy example in Fig 3 , where both constrained and unconstrained NMF provided solutions leading to the same reconstructed data . However , only the constraints allow the solution that makes biological sense to be picked . Our approach is in essence Bayesian , and aims at taking advantage of prior information , when available , to explore the data . We proposed a method that exploits knowledge about the genomic structure and metabolism to build relevant constraints . Genes frequencies are not randomly distributed in the metagenome and result from metabolic association encoded in microbial genomes . Thus , constraints resulting from this metabolic structuration scale up at the community level and should be accounted for in the determination of CAFT . We designed local constraints , only involving adjacent functional markers in the graph representation of the metabolic process . Hence the constraints are not strongly restrictive and still leave a considerable freedom in assembling reactions to build the CAFT matrix , without ever forcing hard reconstruction of pathways . Our method is flexible since it allows imposing constraints only on parts of the metabolic process where biological knowledge is available and considered reliable . Moreover , it could be easily adapted to include other types of prior informations than the one we considered . For instance it should be possible , in contexts where more prior information is available , to design constraints on the weight matrix W , as proposed in [18] for microarray data analysis . We considered a new approach for selecting the number of CAFTs , based on biological reproducibility instead of the numerical stability usually considered in literature . We proposed a criterion which evaluates the concordance between CAFTs computed on independent data sets , and combined it with classical procedures . We implemented our approach on metagenes abundances from 1408 samples of the human gut microbiota , in order to determine CAFTs associated to fiber degradation in the distal gut . Fiber degradation was chosen because it is a major function of the gut microbial ecosystem . It involves the anaerobic fermentation of simple sugar which has been largely documented in the literature , allowing the design of constraints related to this step . The constraints were built by selecting 190 reference microbial genomes from prevalent species of the human gut microbiome , for which marker genes of anaerobic fermentation were annotated . This involved manual curation and relied on both state of the art knowledge of metabolism and accuracy of available annotations . We proposed an approach to account for missing information . In particular , genes were missing in pathways where they were expected to be present , which could be explained by annotation errors , or by the presence of alternative pathways undocumented in the literature . It could also come from unmodelled pathways , not related to fermentation , but sharing some common steps . Then , constraining missing genes markers would be an error . Our approach carefully took into account annotation , modelling and knowledge based errors by adjusting the constraints . We found that our ecological model of CAFTs was consistent with the data , and that the metagenomic potential for fiber degradation by the human gut microbiota could be interpreted as a mixture of 4 CAFTs , shared by all samples in variable proportions . The CAFT detailed in the Results section is the one including the genes involved in methane production , which can be detected in 50% of a healthy population through a breath test . As a matter of fact , none of the three other CAFTs showed a high frequency of this set of genes responsible for the conversion of H2 and CO2 to methane . Two of them are prevalent among individuals , characterized by their richness in terms of GH and fermentation pathways . They differ slightly in GH repertoire and downstream fermentation genes , which may be linked to dietary habits . Finally a fourth profile , less prevalent among samples , might be more represented in diseased individuals . This data reduction highlighted biologically consistent functional patterns while providing a high quality preservation of the original data . Provided that prior knowledge is available , the generic framework we designed can be applied to other metabolic processes in the gut or in other ecosystems . By acknowledging fundamental data structuration our approach enables the inference of meaningful functional traits .
The columns of matrices A and W were ℓ1-normalized; the normalized matrices correspond to the repartition of the functional markers ( resp . the CAFTs ) abundancies among the samples . The distance matrix dA ( resp . dW ) of A ( resp . W ) defined as the 1408 × 1408 matrix whose element ( i , j ) is the euclidean distance between rows i and j of A ( resp . W ) was computed . For each biological sample i , the Pearson correlation between the distance of i to the other samples in A and in W was evaluated: C i = cor d i , j A j ≠ i , d i , j W j ≠ i and the histogram of ( Ci ) i = 1 , … , 1408 was displayed .
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Microbial communities are highly complex ecosystems , harboring a high taxonomic diversity , rapidly varying in time . Focusing on the functional traits of microorganisms instead of species offers an interesting and complementary insight since quite often , owing to environmental selection pressure , traits are less variable in time and shared by many species . By providing gene inventories of otherwise uncultured microbial communities , Whole Genome Shotgun ( WGS ) metagenomics made the functional profiling of microbial communities possible , since community aggregated traits can be quantified by the abundance of relevant marker genes . Considering a global metabolic process in an ecosystem , we propose a method that exploits gene abundance data to determine combinations of functional traits characterizing this process . When applied to fiber degradation in the human gut microbiota , the method shows that only four complex functional traits are needed to characterize this process . The approach is generic and could be applied to other processes or ecosystems . It allows summarizing complex datasets by a limited number of biologically interpretable functional patterns .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"ecology",
"and",
"environmental",
"sciences",
"metagenomics",
"functional",
"genomics",
"metabolites",
"microbial",
"ecosystems",
"ecology",
"ecosystems",
"metabolic",
"processes",
"genetics",
"biology",
"and",
"life",
"sciences",
"genomics",
"ecosystem",
"functioning",
"metabolism",
"fermentation"
] |
2016
|
Inferring Aggregated Functional Traits from Metagenomic Data Using Constrained Non-negative Matrix Factorization: Application to Fiber Degradation in the Human Gut Microbiota
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Post-mitotic cell separation is one of the most prominent events in the life cycle of eukaryotic cells , but the molecular underpinning of this fundamental biological process is far from being concluded and fully characterized . We use budding yeast Saccharomyces cerevisiae as a model and demonstrate AMN1 as a major gene underlying post-mitotic cell separation in a natural yeast strain , YL1C . Specifically , we define a novel 11-residue domain by which Amn1 binds to Ace2 . Moreover , we demonstrate that Amn1 induces proteolysis of Ace2 through the ubiquitin proteasome system and in turn , down-regulates Ace2’s downstream target genes involved in hydrolysis of the primary septum , thus leading to inhibition of cell separation and clumping of haploid yeast cells . Using ChIP assays and site-specific mutation experiments , we show that Ste12 and the a1-α12 heterodimer are two direct regulators of AMN1 . Specifically , a1-α2 , a diploid-specific heterodimer , prevents Ste12 from inactivating AMN1 through binding to its promoter . This demonstrates how the Amn1-governed cell separation is highly cell type dependent . Finally , we show that AMN1368D from YL1C is a dominant allele in most strains of S . cerevisiae and evolutionarily conserved in both genic structure and phenotypic effect in two closely related yeast species , K . lactis and C . glabrata .
The switch between effective and inhibited separation of mother and daughter cells in eukaryotic mitosis represents a fundamental process for understanding the evolution of organizational and functional complexity of organisms , and also has significant medical and industrial value [1–3] . It has been well documented that division of the cytoplasm in Saccharomyces cerevisiae is comprised of a series of coordinated events including assembly and contraction of the contractile actomyosin ring in mitosis , formation of the primary and secondary septa and finally separation of mother and daughter cells [1] . The molecular machinery and regulatory networks that underlie this process has been significantly advanced in recent studies in the simple eukaryotic model yeast S . cerevisae . In particular , the RAM ( regulation of Ace2 and morphogenesis ) network , a pathway regulated by the MEN ( Mitotic Exit Network ) , has been proposed to be responsible for nuclear importation of the transcription factor Ace2 , which is needed for septum cleavage and post-mitotic cell separation [4–7] . Nuclear importation of Ace2 drives a sharp increase in the transcription of genes involved in septum cleavage , including CTS1 and SCW11 , and ultimately leads to separation of mother and daughter cells [4 , 5] . So far , six proteins have been identified as key nodes in the RAM network: Sog2 , Tao3 , Hym1 , Kic1 , Mob2 and Cbk1 . Cells lacking Ace2 or any of these six core components show a phenotypic defect of indistinguishable post-mitotic cell separation and cell clumping [6 , 7] . Failure of daughter cells to separate from their mothers after mitosis , showing a snowflake phenotype under the micropscope , is recognized as the early evolution from unicellular to multicellular populations and the transition can evolve quickly over the course of multiple rounds of selection for the snowflake phenotype [8–10] . In our earlier work , we dissected phenotypic variation in cell clumping in a segregating population created by crossing two phenotypically divergent strains ( YL1C with a strong clumpy phenotype and YH1A with effective cell separation ) , into four major cell clumping Quantitative Trait Loci ( QTLs ) [11] . These major QTLs together explained 45% of the trait phenotypic variation . We resolved the major QTL explaining 25% of the clumping phenotypic variation into the QTL gene AMN1 . We further identified the V368D substitution in Amn1 as the causative variation of the QTL gene through site specific mutation and allele replacement experiments [11] . Amn1 was previously found to be required to turn off the mitotic exit network ( MEN ) , a pathway that promotes spindle breakdown , degradation of mitotic cyclins , cytokinesis , and post-mitotic cell separation , through obstructing the binding of Tem1 to Cdc15 [12 , 13] . This paper presents a novel mechanism of Amn1-mediated cell separation inhibition after mitosis in YL1C . We show here for the first time that Amn1 can post-translationally control the degradation of Ace2 through the ubiquitin proteasome system ( UPS ) . This establishes that Amn1 modulates post-mitotic cell separation through down-regulating Ace2 and its downstream genes . The data not only advances Amn1 as an antagonist of MEN [12] , but also provides a new insight into how the RAM mediates post-mitotic cell separation [4–7] . Moreoever , we demonstrate that the Amn1-governed post-mitotic cell separation is cell-type dependent . The clumping phenotype governed by Amn1 is highly dependent on the ploidy level in natural S . cerevisiae cells , while the functional of Amn1368D from YL1C in controlling post-mitotic cell separation , is evolutionarily conserved in both genic structure and phenotypic effect .
Firstly , the clumping cells of the S . cerevisiae strain YL1C became separated when AMN1 was deleted ( Fig 1A ) as we previously observed [11] . To explore the underlying mechanism by which AMN1 causes cell clumping , we conducted an RNA-seq assay and identified 43 significantly differentially expressed genes between YL1C cells showing a strong clumpy phenotype and YL1C with AMN1 deleted ( Fig 1B ) . Of these 43 genes , 18 were up-regulated when AMN1 was deleted , including DSE1 , DSE2 , DSE3 , DSE4 , EGT2 , SCW11 and CTS1 with known roles in post-mitotic cell separation , acting directly to degrade the primary septum at the bud neck [14–16] . From these 7 known genes , we chose the 4 most up-regulated genes , DSE1 , DSE2 , SCW11 and CTS1 , and confirmed the results of the RNA-seq assay by using RT-qPCR ( S1 Fig ) . We then stained YL1C cells with calcofluor white ( CFW ) , a fluorescent dye specifically staining chitin , the major component of the septum , as previously suggested [17] , and confirmed that the YL1C cells remained attached with the undegraded primary septum at the bud neck . In contrast , when AMN1 was deleted , the bud scars were deeply stained by CFW , indicating complete mother-daughter cell separation ( Fig 1C ) . These results indicate that AMN1 inhibits cell separation after mitosis and induces cell clumping as seen in the YL1C strain . Yeast cell clumps can also be formed through an aggregate cellular behavior genetically controlled by the FLO family of genes that regulate interactions between cell wall glycoproteins [18–20] . We invesigated the influence of FLO genes on the clumping of YL1C cells . FLO1 and FLO8 deletion showed a comparable clumping phenotype to that of YL1C ( S2 Fig ) , suggesting that clumping phenotype of YL1C cells was largely attributable rather to defective post-mitotic cell separation governed by Amn1 than to the cell wall glycoproteins encoded by the FLO gene family . Ace2 is the major transcription factor of DSE1 , DSE2 , DSE3 , DSE4 , EGT2 , SCW11 and CTS1 , its mutation or deletion may sharply decrease the RNA levels of these target genes [4–6 , 14–16] . Based on our observation that deletion of AMN1 occurred in parallel with down-regulation of these Ace2 target genes ( Fig 1B ) , and the fact that deletion of ACE2 restored cell clumping phenotype in Δamn1 mutant cells ( Fig 1A ) , we hypothesized that AMN1 inhibited post-mitotic cell separation in the YL1C strain through inactivating Ace2 . To test the hypothesis , we firstly profiled the RNA level of ACE2 , and did not find any significant change in the RNA level ( Fig 2A upper ) , but the protein level of the gene measured by the western blotting assay was substantially up-regulated in the YL1C strain with AMN1 deleted when compared to that in the YL1C strain ( Fig 2A lower ) . The protein level of Ace2 was also found to vary over the cell cycle and to be negatively correlated with the protein level of Amn1 in the YL1C strain in a cell synchronization analysis by using nocodazole . However , the protein level of Ace2 did not show a marked change across the cell cycle when the cells carried an Amn1368V variant ( Fig 2B and 2C ) . We induced expression of AMN1 under the PGAL10 drive in YL1C using galactose , and profiled the protein levels of Ace2 and Amn1 . The results showed that the protein level of Ace2 decreased as the protein level of Amn1 increased . At the 6-hour time point , Amn1 protein expression reached its highest level and the Ace2 protein was not detectable ( Fig 2D ) . However , the RNA expression of ACE2 did not show any change , while the RNA level of AMN1 progressively increased ( S3 Fig ) . Moreover , when the 368D was replaced by the Val residue , the protein level of Ace2 was no longer dependent on the Amn1368V level ( Fig 2D ) . The negative correlation in protein level between Ace2 and of Amn1 in both cell synchronization assay and galactose pulse-chase assay strongly support the down-regulation of Ace2 by Amn1 . We also tested the nuclear accumulation of Ace2 , which was necessary to induce transcription of its target genes [4 , 5] . We marked Ace2 with GFP and found that Ace2 was absent in daughter cell nuclei of synchronized YL1C cells . In contrast , when AMN1 was deleted , Ace2 efficiently accumulated in daughter cell nuclei ( Fig 2E ) . Using site-directed mutagenesis , we constructed two YL1C strains with a continuously activated Ace2 , carrying either of two sets of multiple substitutions , either S122D , S137D , T575A , S701A and S714A ( referred to as Ace2-AAA-2D ) , or F127V , T575A , S701A and S714A ( referred to as Ace2-AAA-F127V ) . Both were previously reported to locate in the nucleus in all cells regardless of cell cycle position and to continuously transcribe Ace2’s target genes [5 , 21] . Nevertheless , post-mitotic cell separation was inhibited in both genetically modified strains , while the RNA levels of Ace2 target genes did not significantly change in the two strains compared to YL1C ( S4A Fig ) . Ace2 protein levels remained extremely low in the wild-type YL1C cells and its engineered strains , but were boosted when AMN1 was deleted ( S4B Fig ) . The data supports that Amn1 mediated degradation of Ace2 overrides the well-established view that regulation of Ace2 function is through phosphoration and protein localization during the process of cell separation after mitosis [4 , 5] . To further explore downregulation of Ace2 by Amn1 , we firstly performed ribosome profiling to test whether Amn1 affects the translational efficiency of Ace2 . It shows no marked change in the level of ACE2 mRNAs occupied by the ribosomes in YL1C compared with the corresponding Δamn1 strain ( S5 Fig ) . The downregulation of Ace2 by Amn1 is therefore unlikely due to altered translational efficiency . To examine the apparent impact of Amn1 on the stability of Ace2 , we performed a GAL promoter shut-off chase experiment in which PGAL10-ACE2 was transformed into YL1C so as to drive ACE2 expression . The engineered cells were initially grown in galactose medium to induce ACE2 expression , and then transferred to glucose medium to switch off ACE2 transcription . The protein level of Ace2 was then measured every 15 minutes using western blotting . Ace2 was extremely unstable and vulnerable to degradation in the YL1C strain ( Fig 3A left panel ) , while no marked decrease in the protein level was observed in the AMN1 deleted strain ( Fig 3A right panel ) . When the proteolytic activity of the 26S proteasome was blocked using MG132 , then Ace2 levels no longer markedly decreased ( Fig 3B ) . Additionally , YL1C cells treated with MG132 had a stable endogenous Ace2 protein level ( Fig 3C ) , while the endogenous Ace2 protein was markedly boosted when the ubiquitin coding gene UBI4 was deleted ( Fig 3D ) . These results indicated that the down-regulation of Ace2 by Amn1 can be explained by the ubiquitin-conjugated protein degradation machinery . Amn1 was previously predicted to be a member of the F-box protein family in S . cerevisiae , and proposed to be a potential ubiquitin ligase E3 in a bioinformatic analysis [22] . However , the putative F-box in Amn1 is atypical since the motif is separated by a 56-amino acid insertion . However , there is no experimental evidence so far for whether Amn1 catalyzes ubiquitination of Ace2 directly and facilitates its degradation . To tackle this open question , we firstly constructed two genetic modified Amn1 proteins without a normal function as an E3 ligase , one with deletion of only the F-box ( referred to as AMN1-Δ ( 496–552 ) & ( 721–789 ) ) and the other with deletion of both the F-box and the 56 amino acid insertion ( referred to as AMN1-Δ ( 496–789 ) ) . YL1C cells with either modified Amn1 protein showed effective cell separation and substantial up-regulation of the Ace2 protein in vivo ( Fig 3E ) . This suggested that the atypical F-box in Amn1 was functional and controlled the stability of Ace2 . Furthermore , we carried out co-immunoprecipitation assays and showed that Amn1 physically binds to Skp1 and Cdc53 , two key components of SCF complex ( Fig 3F left panel ) [23] . We also performed a size-exclusion chromatography experiment , and found that the three proteins , Amn1-Flag , Skp1-Myc and Cdc53-Ha , could be co-eluted at #16-#21 , corresponding to the apparent size of ~440kDa . Additionally , we found that the three proteins overlapped again at fraction #27-#29 , corresponding to the apparent size of ~ 158kDa . We thus speculated that the fraction #27-#29 contained the complexes of Amn1-Cdc53 , Cdc53- Skp1 , and Amn1-Skp1 ( Fig 3F right panel ) . We then examined the stability of Ace2 in cdc53 and skp1 mutants using the GAL promoter shut off assay . And found Ace2 was greatly stabilized by mutations in both CDC53 and SKP1 ( Fig 3G ) . Moreover , we enriched the Ace2 protein complex using immunoprecipitation and profiled Amn1 and Ace2 . Amn1 in YL1C was clearly seen to physically interact with Ace2 ( Fig 3H ) . However , when the 368D in Amn1 was substituted by Val , the Amn1368V level was detected but its physical interaction with Ace2 was substantially weakened . A higher molecular ladder of ubiquitinated Ace2 was also detected in YL1C ( Fig 3H and 3I ) , while ubiquitinated Ace2 was almost completely eliminated ( Fig 3H and 3I ) when AMN1 was deleted or substituted with AMN1368V . An in vitro protein ubiquitination assay also showed that Ace2 could be labelled with exogenous human ubiquitin in the presence of Amn1368D , but not in the absence of Amn1 , and the ubiquitination was substantially weakened when Amn1368D was replaced by Amn1368V ( Fig 3J ) . These observations support Amn1’s role in mediating Ace2 proteolysis through the UPS , and the essentiality of the 368D residue to enable Amn1 to adequately bind and hence to ubiquitinate Ace2 . ACE2 has a paralog SWI5 which arose from whole genome duplication [24] . Swi5 has an almost identical DNA-binding domain to Ace2 , but regulates a different set of genes in vivo [16] . However , we did not see any significant change in RNA or protein levels of SWI5 in YL1C with or without AMN1 , clearly indicating that Swi5 , unlike Ace2 , is not regulated by AMN1 ( Fig 4A ) . We aligned protein sequences of Ace2 and Swi5 , compared their functional domains and identified 3 aligned regions ( A , B and C ) , with region A subdivided into A1 , A2 , A3 and A4 regions . We then constructed a series of Ace2-Swi5 chimeras , and identified a novel 11-residue domain ‘ELRDLDIPLVP’ as the site for Amn1 to bind to and directly degrade Ace2 ( see Supplementary Materials and Methods , S6 Fig ) . We replaced this domain with the corresponding Swi5 derived ‘EINDLNLPLGP’ in YL1C , creating a modified Ace2* ( Fig 4B ) . YL1C cells carrying the Ace2* separated effectively and a western blotting assay indicates that the modified Ace2* was expressed at markedly higher levels than the wild-typed Ace2 in vivo ( Fig 4C and 4D ) . In addition , the RNA levels of four Ace2 target genes were clearly up-regulated ( Fig 4D ) . An Ace2 protein with the 11 residue domain mutated may therefore escape from the negative control by Amn1 . Moreover , we did not observe any detectable signal of interaction of Ace2* with Amn1 , nor its ubiquitination , further supporting the role of the identified 11-residue domain in the post-translational regulation of Ace2 ( Fig 4E ) . We then constructed a series of strains bearing single amino acid substitutions ( Ace2R71N , Ace2D74N and Ace2V78G ) , but none of these led to effective cell separation , suggesting that the whole 11-residue domain be essential for its phenotypic effect ( S6C Fig ) . The above analyses were established in haploid cells . We found that inhibition of post-mitotic cell separation was released and the cells separated effectively in diploids ( Fig 5A ) . Additionally , overexpression of AMN1 led to strong inhibition of post-mitotic cell separation in the diploid cells ( Fig 5A ) . We then checked the protein level of Amn1 and Ace2 in diploid cells using western blotting and found Amn1 protein level was much lower , agreeing with its RNA level ( Fig 5A and 5B ) . Conversely , the protein level of Ace2 was markedly boosted in diploids compared to haploid cells ( Fig 5B ) . Overexpression of AMN1 in the diploid cells depressed the Ace2 protein level , as expected ( Fig 5B ) . Using RNA-seq , we surveyed the RNA levels of the 18 genes that were up-regulated when AMN1 was deleted in haploid cells . Of the 18 , 15 showed an inflated expression in MATa/α diploid cells , but their RNA levels were repressed when the cells had AMN1 over-expressed ( Fig 5C ) . These results were confirmed by RT-qPCR assays ( Fig 5A ) . The data supports Amn1 as the key inhibitor of post-mitotic cell separation in diploid as well as in haploid cells , but it must be noted that its endogenous expression is not activated in diploid cells in nature as to be explained below . Diploid S . cerevisiae cells differ from the corresponding haploids in two ways . First , haploid cells and diploid cells created from merging of the haploid cells may be identical at every gene in the genome except at the MAT locus . Second , they differ in gene dosage [25] . To compare the diploids and haploids in exactly the same genetic background , we created diploids with a homozygous genotype MATa/a or MATα/α at the MAT locus . We observed that the cell clumping phenotype as well as the expression patterns of AMN1 and its downstream regulated genes in these diploids were comparable to those in the haploids ( Fig 5A ) . These observations effectively excluded the possibility that differentiation in the post-mitotic cell separation between natural haploid and diploid cells was due to the difference in gene dosage . We next explored the difference in post-mitotic cell separation behavior between diploid cells with either homozygous or heterozygous genotypes at the MAT locus . First , we noted that the MATa/α diploid cells of S . cerevisiae encoded a heterodimer , a1-α2 , which played a role as a transcriptional repressor of haploid-specific genes [26] and might contribute to ploidy specific phenotypes observed between natural haploid and diploid yeasts [26–28] . To establish association of the a1-α2 heterodimer with the Amn1-regulated post mitotic cell separation , we notified that the binding sites of the heterodimer in the upstream promoter region of AMN1 predicted from publicly available large scale ChIP data [26 , 29] . The two predicted binding sites were also shared by another regulator , Ste12 ( Fig 5D ) . At the two a1-α2 binding sites , we constructed single-site and double-site deletion mutants of the MATa/α diploid cells , and observed that , the MATa/α diploid cells in which both binding sites were deleted displayed inhibited mother-daughter separation after mitosis , similar to that observed in the corresponding a/a or α/α diploid cells ( Fig 5A ) . At the same time , the expression of AMN1 was markedly boosted , while the expression of Ace2’s downstream genes , DSE1 , DSE2 , SCW11 and CTS1 , was reduced ( Fig 5A ) . We thus hypothesized that the post-mitotic cell separation of the natural MATa/α diploid cells was caused by the a1-α2 heterodimer blocking the binding of Ste12 to the AMN1 promoter . We carried out a ChIP assay to test if Ste12 binds to the AMN1 promoter in YL1C haploids and diploids . The assay showed that the fold change of enrichment was highly significant in the haploid cells , with the enrichment peak observed in a region from -600 to -700 bp spanning the upstream region of AMN1 , while no enrichment was observed in the diploid cells ( Fig 5D ) . Consistently , the RNA level of AMN1 was reduced when STE12 was deleted in haploid cells , and in turn , the depressed expression of Amn1 has led to a remarkable boost in expression of DSE1 , DSE2 , SCW11 and CTS1 ( Fig 5E ) . These results show that Ste12 and the a1-α2 heterodimer share common binding sites in the promoter of AMN1 . The a1-α2 heterodimer down-regulates Amn1 and make the diploid cells effectively separate after mitosis . Although Ace2 was a well-documented transcription factor of AMN1 in S288C or W303 yeasts , Ace2 was absent in haploid YL1C [16] . Thus , the question rises how AMN1 is regulated in YL1C ? We showed that the RNA level of AMN1 in haploid cells with ACE2 deleted was not reduced to the background level ( i . e . the level when AMN1’s promoter was completely removed ) until STE12 was additionally deleted . In contrast , a single deletion of ACE2 was sufficient to reduce the RNA level of AMN1 to the background in the diploid cells ( Fig 6A ) . All these together show that transcription regulation of AMN1 in YL1C cells differs between the two types ( haploid and diploid ) of cells . In summary , the heterodimer is absent in haploids and Ste12 activates AMN1 . Expression of AMN1 silences the protein expression of Ace2 through its N-terminal 11-residue domain as illustrated in Fig 4 and thus inhibits expression of Ace2 target genes . Down-regulation of the genes involved in septum cleavage consequently leads to post-mitotic cell separation inhibition , causing cells to be clumped in haploids . On the other hand , in MATa/α diploid strains , the presence of the a1-α2 heterodimer prevented Ste12 from efficiently binding to the AMN1 promoter , which in turn , inactivates the transcription of AMN1 . This leads to effective post-mitotic cell separation because Ace2 is then released from negative regulation by Amn1 . These observations explain the cell type dependency of the Amn1 regulated cell separation after mitosis ( Fig 6B ) . All the conclusions above were drawn from experiments with natural , cell clumped strain YL1C , which carrying an AMN1368D . To explore the conservation of Amn1368D among yeast strains of Saccharomyces cerevisiae , we collected and aligned the Amn1 protein sequences for all 46 sequenced yeast strains from the SGD database ( www . yeastgenome . org ) . Of the 46 strains , 34 shared the 368D in Amn1 , while the remaining 12 , most of which have a common S288C background , ( e . g . S288C , W303 , BY4741 , BY4742 , JK9-3d and X2180-1A ) , carry the 368V ( S2 Table ) . These data prompt us that AMN1368D is a dominant allele , while AMN1368V probably sourced from the human-selection for a lab-used strain which was less clumpy and thereby greatly easily handled . Amn1 was reported as an inhibitor of the mitotic exit network ( MEN ) , through binding to Tem1 , and in doing so , inhibiting Tem1 binding to Cdc15 [12 , 13] . However , these findings were established from the haploid strain W303 carrying Amn1368V . To investigate whether MEN would also be inhibited in the YL1C haploid strain carrying Amn1368D , we performed a co-immunoprecipitation assay and observed effective binding of Amn1368D to Tem1 ( Fig 7A ) . When AMN1 was deleted in YL1C , the interaction between Tem1 and Cdc15 was strengthened; however , once either AMN1368D or AMN1368V was overexpressed in the YL1C Δamn1 cells , the interaction between Cdc15 and Tem1 was markedly weakened ( Fig 7B ) . These observations show that Amn1368D has the same function as Amn1368V in suppressing the interaction of Tem1 with Cdc15 , suggesting that both proteins can inhibit MEN . We also compared Amn1368D and Amn1368V for their role in regulating post-mitotic cell separation in both S288C and W303 strains . The comparison shows that overexpressed AMN1368D may still result in post-mitotic cell separation inhibition , whereas overexpression of AMN1368V does not lead to the same phenotype in these two strains ( Fig 7C ) . AMN1 was predicted as an orthologous gene among at least 10 yeast species from the orthologous groups database ( OrthoDB , http://orthodb . org/orthodb7 ) , including S . cerevisiae , C . glabrata and K . lactis [30] . Notably , the conserved regions of the Amn1 orthologous proteins cover the functional 368D residue ( S7A Fig ) . We tested functional conservation of the orthologous proteins in two yeast species related to S . cerevisiae , diverging ~100 million years ago [31] . First , C . glabrata , a highly opportunistic pathogen is usually found in the urogenital tract or bloodstream and especially prevalent in HIV positive or elderly populations [32] . Second , K . lactis , a Kluyveromyces yeast is commonly used in basic research and industry [33] . We aligned Amn1Kl ( from K . lactis ) and Amn1Cg ( from C . glabrata ) with Amn1Sc ( from S . cerevisiae ) using clustal W and found 24 . 8% and 37 . 6% amino acid sequence identity with Amn1Sc respectively ( S7D Fig ) . When we replaced the coding region of the AMN1 allele in situ in YL1C with the coding region of either AMN1Cg or AMN1Kl , the clumping phenotype was restored in Δamn1 mutant cells ( S7B Fig ) . The RNA levels of the four major downstream genes responsible for septum degradation , DSE1 , DSE2 , SCW11 and CTS1 , were consistently repressed in the two gene replacement strains ( S7C Fig ) . These results indicate that AMN1368D , but not AMN1368V is more likely the conserved allele .
The evolution of multicellularity from unicellularity in eukaryotes has attracted wide interest in both basic research and its translational value in medicine and industry . However , the underlying molecular mechanism that governs this important transition in cellular behavior is far from well established . In model organism budding yeast , the defective post-mitotic mother and daughter cell separation has been recognized as a key step in the evolution of multicellularity in the recent literature . In these works , clumped yeast cells from inhibited mother-daughter cell separation during mitosis were attributed to various mutants of ace2 , particularly a truncated ace2 mutant [8–10] , or the inhibition of Ace2’s nuclear importation in budding yeast [4 , 5] . Here , we report a novel Amn1 governed post-mitotic cell separation and demonstrate that Amn1 induces proteolysis of Ace2 through the ubiquitin proteasome system , and in turn , down-regulates Ace2’s downstream target genes involved in hydrolysis of the primary septum . This leads to inhibition of cell separation and clumping of haploid yeast cells . Moreover , the present study reveals that separation of mother and daughter cells after mitosis regulated by Amn1 is also highly dependent on cell type in the budding yeast S . cerevisiae , with effective separation after mitosis observed when haploid cells merge with other haploid cells of an opposite mating type to form diploids . In light of the Amn1 governed post-mitotic cell separation and our observation that AMN1 has an unusually lengthy upstream region , we demonstrate that Ste12 and the a1-α2 heterodimer share the same binding sites in the promoter of AMN1 , which leads to the transcription regulation of AMN1 in YL1C cells differs between the two types ( haploid and diploid ) of cells . The present study clarified that divergence in the regulation of post-mitotic cell separation between haploids and diploids is not attributable to gene dosage , but actually to the presence or absence of the a1-α2 heterodimer . Furthermore , up to 65 other regulators that conditionally activate AMN1 were proposed to target the upstream region of AMN1 , which may change the transcription regulation of AMN1 [34] . Among them , Phd1 in diploid yeast cells may bind to the AMN1 promoter when the cells are starved of nitrogen [35] . These observations indicate that AMN1 could be activated conditionally by common regulators , leading to inhibition of post-mitotic cell separation . The functionality of AMN1 in regulating post-mitotic cell separation is highly conserved among S . cerevisiae and its two closely related yeast species , K . lactis and C . glabrata . Amn1 has not only preserved a high level of amino acid sequence similarity among yeast species , but also conservation of the functional domain of the C terminus can be extended to at least 13 other species , including Homo sapiens , Xenopus tropicalis and Danio rerio [36] . therefore , Amn1 may also be able to mediate turnover of substrates through the UPS in higher species , and regulate other biological processes in addition to post-mitotic cell separation .
All yeast strains and plasmids used in this research are listed in S3 and S4 Tables , and the methods for genetic modification of yeast strains and plasmid construction are detailed in supplementary material and methods . Yeast cells were cultured in 50 ml YPD liquid medium until OD600 = 1 . 0 was reached . Cells were then harvested by centrifugation and the cell pellet was washed with ice-cold water . Total RNA was extracted with the Qiagen RNeasy mini kit and then RNA-seq libraries were constructed using the Illumina TruSeq kit . RNA was quantified using the NanoDrop and RNA integrity was assessed using the Agilent BioAnalyzer 2100 . Using the Illumina HiSeq 2000 , about 5 million 125 bp paired end reads were collected per sample . Reads were mapped to the S288C genome for expression analysis using tophat [37] . Gene expression was measured as reads mapped per kilobase of exon per million reads mapped ( RPKM ) . A list of differentially expressed genes was obtained by using cufflinks and cuffdiff [37] . Total RNA for RT-qPCR was isolated using the hot phenol protocol [38] followed by purification with RNase-free DNase ( Promega , USA ) , and finally subjected to first-strand cDNA synthesis using SuperScript III Reverse transcriptase ( Invitrogen , USA ) . 1μl of the single-strand cDNA in 10-fold dilution was used as the template for real-time quantitative PCR ( RT-qPCR ) using SYBR-green ( Toyobo , Japan ) and ACT1 as the internal control . Every tested strain was independently cultured three times to gain three independent biologically replicated samples and each sample was assayed in triplicate in the qPCR assay . AMN1Kl ( XM_451967 . 1 ) was cloned from genomic DNA of K . lactis ( NRRL Y-1140 ) using primers ATGTCTTGCGTCTCCAGTATTAG and TCAGGTCGCTTCGTTGAC , whilst AMN1Cg ( XM_447123 . 1 ) was cloned from genomic DNA of C . glabrata ( CBS 138 ) using primers ATGGTATTGCCTGATTCCAAC and TTATTCATTTTCTAATTGATTAAC . We used UAR3 pop-in/pop-out method to perform AMN1 ortholog replacement experiments . The AMN1 ORF in YL1C was firstly deleted using the URA3 marker , and then the URA3 marker was replaced with AMN1KL and AMN1CG separately . Proteins were extracted using the protocol described by Kushnirov [39] . Briefly , 107 yeast cells were harvested by centrifugation and the pellet was washed with H2O before incubation in 200μl 0 . 2M NaOH for 5 minutes at room temperature . Cells were centrifuged again and the pellet was incubated in 50μl SDS-loading buffer ( 120mM Tris-Cl , 10% glycerol , 4% SDS , 8% β-mercaptoethanol , 0 . 005% bromophenol blue ) , and boiled for 5 minutes . Cells were centrifuged and 10μl supernatant was loaded and separated in SDS-PAGE . Western blotting was performed using the standard laboratory procedures [40] . Cell-cycle was synchronized by using of α–factor or nocodazole , as described previously [41] . Briefly , mating type of YL1C cells which were to be arrested with α–factor was switched to MATa using plasmid pTetra , and their BAR1 gene was deleted using natMX4 so that the strain could respond to a low density of α–factor . The logarithmic phase cells ( OD600 = 0 . 5 ) were grown in YPD with added α–factor ( 50 ng/ml ) for 1 . 5 hours . The G1-arrested cells were subsequently released after repeatedly washing with pre-warmed ddH2O at 30°C . The cells were then incubated in pre-warmed YPD medium with Pronase E ( 0 . 1 mg/ml ) . Similarly , the nocodazole-arrested assay was performed by adding 200 μl of nocodazole ( 1 . 5M ) into 20 ml of logarithmic phase cells ( OD600 = 0 . 5 ) . The cells were grown for another 2 . 5 hours to be arrested in G2/M phase and then washed twice using pre-warmed YPD . The arrested cells were released in 40 ml fresh YPD medium . Finally , 2 ml samples of the cells treated either with α–factor or with nocodazole were harvested every 10 minutes over the next two hours for further analysis . Yeast cell cultures in a volume of 50 ml were grown in YPD to an optical density of OD600 = 1 . 0 . The cells were then harvested by centrifugation and the cell pellet was washed with ice-cold water . The cells were pelleted and re-suspended in 1000 μl of lysis buffer ( 50 mM HEPES-KOH at pH 7 . 5 , 140 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% Na-deoxycholate , 1 tablet of the complete inhibitor cocktail supplied by Roche ) and lysed with acid-washed glass beads for 15 minutes in a vortex on full output . Cell lysate was centrifuged ( 15 min at 13 , 000 rpm ) at 4°C to remove cell debris . The gel filtration column ( Superdex 200; Amersham Biosciences ) was washed and equilibrated using cold PBS ( 4°C ) . Lysates were passed over the gel filtration column with a flow rate of 0 . 5 ml/min . Samples were collected every 0 . 5 ml per tube and analyzed by western blot . Co-immuno-precipitation assay was performed for detecting physical interaction between Ace2 and Amn1 . Cells carrying pGU-Myc-Ace2 were grown in SC-U medium overnight . Cell cultures were diluted to an OD600 of 0 . 2 using 20ml YPR and grown to stationary phase . Cells were then harvested by centrifugation . The cell pellets were resuspended in fresh 50ml YPR and continuously cultured until OD600 reached an approximate value of 0 . 8 . Galactose was immediately added and MG132 ( 20 μM in final solution ) was added two hours later into the cell culture . The cells were grown for another hour and then harvested by centrifugation . WCEs ( whole cell extracts ) were extracted using glass beads in IP lysis buffer ( 50 mM HEPES-KOH at pH 7 . 5 , 140 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 1 tablet of the complete inhibitor cocktail supplied by Roche ) and quantified using NanoDrop . 5mg of WCEs was used as input and 50 mg ( 1000 μl ) of WCEs were then added with magnetic dynabead protein G ( Invitrogen , USA ) , which was pre-incubated with the monoclonal mouse anti-Myc ( Transgene , China ) antibody for 30 min at room temperature . The mixture was rotated for 1 hour at room temperature . The dynabeads conjugating protein complex was harvested by magnetic force and washed three times with IP lysis buffer . The beads were resuspended in SDS-loading buffer ( 120mM Tris-Cl , 10% glycerol , 4% SDS , 8% β-mercaptoethanol , 0 . 005% bromophenol blue ) and boiled for 5 minutes . All samples were followed by western blotting , using anti-Myc antibody ( Transgene , China ) and anti-Flag ( Sigma , USA ) . To detect the physical interactions of Cdc53/Amn1 , Skp1/Amn1 , Amn1/Tem1 and Cdc15/Tem1 , yeast cells were grown in YPD until OD600 of 1 . 0 . The following steps were performed as above . in vitro ubiquitination assays were performed as previously described [42] . Briefly , YL1C cells expressing AMN1368D , AMN1368V or Δamn1 mutant cells were harvested when the OD600 reached 1 . 0 . Cell pellets were resuspended in reaction buffer ( 50 mM Tris-HCl , pH 7 . 5 , 5 mM MgCl2 , 1 mM DTT , 2 mM ATP ) . Protein was extracted by the standard glass beads method and concentration of the extracts was measured and normalized using the Bradford protein assay . Equal amounts of extracts from YL1C cells expressing AMN1368D , AMN1368V or Damn1 mutant cells were added with 2 μg N-terminal histidine tagged human ubiquitin , and the cell extracts were incubated with Myc-Ace2 ( purified from YL1C cells with AMN1 deleted ) for 1 hour at 30°C . Myc-Ace2 were recovered using 20 μl of dynabeads protein G ( Invitrogen , USA ) that was pre-incubated with the monoclonal mouse anti-Myc ( Transgene , China ) antibody for 30 min at room temperature . After three washes with the lysis washing buffer ( 50 mM HEPES-KOH at pH 7 . 5 , 140 mM NaCl , 1 mM EDTA , 1% Triton X-100 ) , ubiquitylated proteins were detected using antibodies against the His epitope . Ste12 tagged with 6 tandem repeats of c-myc epitope was used in ChIP assays for both the haploid YL1C and the corresponding diploid cells . The ChIP assay was implemented according to the classic protocol [43] . In detail , cell cultures in a volume of 50 ml were grown in YPD to an optical density of OD600 = 1 . 0 , and incubated with 1% FA for 15 minutes at room temperature to enable protein-DNA cross-linking . After addition of 125 mM glycine and incubation for a further 5 minutes at room temperature , the cells were harvested and washed three times with ice-cold 1× TBS at pH 7 . 5 ( 20 mM Tris-Cl at pH 7 . 5 , 150 mM NaCl ) . The cells were pelleted and re-suspended in 1000 μl of lysis buffer ( 50 mM HEPES-KOH at pH 7 . 5 , 140 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% Na-deoxycholate , 1 tablet of the complete inhibitor cocktail supplied by Roche ) and lysed with acid-washed glass beads for 15 minutes in a vortex on full output . After removing the cell debris by centrifugation at 12000 rpm for 5 minutes at 4°C , the chromatin in the supernatant was sheared to a length of 200 bp to 500 bp using Covaris S220 ( Duty Factor = 25% , Intensity Peak Incident Power = 400W , Cycles per Burst = 200 , Processing Time = 20 minutes , Volume = 1ml in TC16 tubes ) . Immuno-precipitation was performed with 2 . 5 mg ( 1000 μl ) cell extract in 20 μl magnetic dynabeads protein G ( Invitrogen , USA ) , which was pre-incubated with the monoclonal mouse anti-Myc ( Sigma , 9E10 , USA ) antibody for 2 hours at room temperature . The precipitates were washed in order with lysis buffer , lysis buffer with 360 mM NaCl , washing buffer ( 10 mM Tris-Cl at pH 8 . 0 , 250 mM LiCl , 0 . 5% NP-40 , 0 . 5% Na-deoxycholate , 1 mM EDTA ) , and 1× TE at pH 7 . 5 using the magnetic device supplied by Toyobo ( Japan ) . The precipitated DNA was eluted by heating in TES for 30 minutes at 65°C and de-cross linked by heating at 65°C overnight , and digesting with proteinase K ( Merck , USA ) for 1 hour at 37°C . DNA in the precipitate was purified using the PCR Purification Kit ( QIAGEN , USA ) .
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Separation of mother and daughter cells after mitosis in eukaryotes enacts various functional and/or developmental needs and has significant medical and industrial implications . How this cellular behaviour is regulated is far from being concluded . We report here a novel Amn1 mediated post-mitotic cell separation in a budding yeast strain , YL1C and demonstrate that the post-mitotic cell separation can be regulated through a ubiquitin-conjugated protein degradation of Ace2 by Amn1 . The Amn1-governed switch of cell separation is evolutionarily conserved and highly cell type dependent . These findings provide insights into the molecular mechanism of how post-mitotic cell separation is regulated in budding yeast , and data for translating into medical and industrial applications .
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2018
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Amn1 governs post-mitotic cell separation in Saccharomyces cerevisiae
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In order to proceed through their life cycle , Leishmania parasites switch between sandflies and mammals . The flagellated promastigote cells transmitted by the insect vector are phagocytized by macrophages within the mammalian host and convert into the amastigote stage , which possesses a rudimentary flagellum only . During an earlier proteomic study of the stage differentiation of the parasite we identified a component of the outer dynein arm docking complex , a structure of the flagellar axoneme . The 70 kDa subunit of the outer dynein arm docking complex consists of three subunits altogether and is essential for the assembly of the outer dynein arm onto the doublet microtubule of the flagella . According to the nomenclature of the well-studied Chlamydomonas reinhardtii complex we named the Leishmania protein LdDC2 . This study features a characterization of the protein over the life cycle of the parasite . It is synthesized exclusively in the promastigote stage and localizes to the flagellum . Gene replacement mutants of lddc2 show reduced growth rates and diminished flagellar length . Additionally , the normally spindle-shaped promastigote parasites reveal a more spherical cell shape giving them an amastigote-like appearance . The mutants lose their motility and wiggle in place . Ultrastructural analyses reveal that the outer dynein arm is missing . Furthermore , expression of the amastigote-specific A2 gene family was detected in the deletion mutants in the absence of a stage conversion stimulus . In vitro infectivity is slightly increased in the mutant cell line compared to wild-type Leishmania donovani parasites . Our results indicate that the correct assembly of the flagellum has a great influence on the investigated characteristics of Leishmania parasites . The lack of a single flagellar protein causes an aberrant morphology , impaired growth and altered infectiousness of the parasite .
Protozoan parasites of the genus Leishmania cause a variety of diseases in humans collectively termed as leishmaniasis . The pathologies range from self-healing cutaneous lesions ( Leishmania major ) to fatal visceral involvement ( Leishmania donovani ) . Two million new infections are estimated to occur annually , with an estimated 12 million people presently infected in over 85 endemic countries worldwide [1] . The parasite is transmitted to mammalian hosts as the infective flagellated promastigote form from the gut of its insect vector , female phlebotomine flies . Promastigotes are phagocytized by macrophages wherein they develop into tamastigote form which is able to survive and proliferate inside the fully acidified phagolysosomes of their host cells [2] . The developmental stage differentiation is mainly induced by changes in pH and temperature and each stage is highly adapted for extra- or intracellular survival in the specific environment encountered in insect and vertebrate host [3] . One aspect of the transformation from promastigote to amastigote parasites is the regulation of organelle and overall cell size . The promastigotes are spindle-shaped cells with a long flagellum protruding from the flagellar pocket , an invagination of the cytoplasmic membrane at the anterior end of the cell . By contrast , the amastigotes display a more spherical form with an overall reduced cellular volume and only a rudimentary flagellum that does not protrude from the flagellar pocket . The flagellum is involved in various processes such as cell motility but also attachment to host surfaces and intracellular signaling [4] , [5] . As in most motile eukaryotic flagella , a canonical “9+2” microtubule axoneme drives the flagellar movement of Leishmania parasites . It consists of nine outer doublet microtubules ( A- and B-tubule ) surrounding a pair of centrally located singlet microtubules . Radial spokes extend inward from each outer doublet towards the central pair . ATP-dependent dynein motor proteins attached to each doublet translocate along the adjacent doublet to generate the sliding force that underlies flagellar movement . Cilia and flagella of eukaryotic cells contain three different classes of dyneins: cytoplasmic ones as well as the inner and outer dynein arms of the axoneme . L . mexicana contains two cytoplasmic dynein-2 heavy chain genes ( LmxDHC2 . 1+2 . 2 ) and a single dynein-1 heavy chain gene ( LmxDHC1 ) . Disruption of LmxDHC2 . 2 results in an amastigote-like phenotype and immotility of the parasite . Nevertheless , protein expression is still as in the promastigote stage . Further studies indicate the absence of the paraflagellar rod proteins PFR1 and PFR2 and that the LmxDHC2 . 2 is required for correct flagellar assembly [6] . Every dynein binds to a structurally unique binding site mediating a high specificity that is essential for the flagellar movement . The unicellular green algae Chlamydomonas reinhardtii serves as a model organism for studying the composition and function of flagella . Their outer dynein arms are very well characterized [7] , [8] . These dyneins produce 80% of the flagellar force and bind to specific sites of the A-tubule of the outer microtubule doublet [9] . The globular heads possess a binding site for the B-tubule , and they are spread along the whole length of the axoneme with a regular distance of 24 nm . The outer dynein arms consist of several polypeptide chains: three heavy chains ( HCα , β and γ ) , two intermediary chains ( IC78 and IC69 ) and multiple light chains ( LC1-8 ) [10] . In 1994 , Takada and Kamiya could identify a protein complex responsible for the association of the outer dynein arm to the microtubule , the outer dynein arm docking complex ( ODA-DC ) [11] . Subsequent studies showed that this complex consists of three proteins present in equimolar amounts and in a 1∶1 stoichiometry with the outer dynein arm polypeptide chains [12]–[14] . The subunits DC1 [13] and DC2 [14] have coiled-coil domains and are wound around each other in an α-helical manner . The third subunit DC3 , member of an EF hand superfamily of Ca2+ binding proteins , is also essential for the composition of the outer dynein arm and the ODA-DC [12] . The flagella of leishmania parasites reveal , apart from the above described axonemal structure , an additional peculiar characteristic feature: the paraflagellar rod ( PFR ) . This is a unique network of cytoskeletal filaments which extends along the whole axonene within the flagella of kinetoplastids , euglenoids and dinoflagellates [15] . It was shown in L . mexicana and T . brucei that this structure is essential for the cellular movement [16] , [17] . However , nothing is known about its function so far . Here , we report the characterization of LdDC2 , a protein of the outer dynein arm docking complex ( ODA-DC ) from Leishmania donovani , a structure important for the integrity of the flagellar axoneme . The protein was identified during an earlier performed proteome analysis of L . donovani stage differentiation [18] . It is expressed exclusively during the promastigote stage of the parasite and localizes primarily to the flagellum . Deletion mutants display an altered morphology , impaired growth and show slightly increased in vitro infectivity .
L . donovani 1SR strain , a gift from D . Zilberstein ( Department of Biology , Technicon , Israel Institute of Technology , Haifa , Israel ) , was used for all experiments . Promastigotes ( day 0 ) frozen directly after passage trough BALB/c mice were thawed and cultivated at 25°C in M199 medium supplemented with 25% fetal calf serum and 20 µg/mL gentamycin . In vitro differentiation to amastigotes was achieved as described previously [19] . Briefly , promastigotes ( day 0 ) were heat-shocked at 37°C for 24 h ( day 1 ) and then cultivated for up to 5 days at 37°C in mildly acidic medium ( pH 5 . 5 , day 2–5 ) . Cell densities were determined using a CASY 1-Cell Counter & Analyser ( Schaerfe Systems ) . Peritoneal exudate cells ( PECs ) from 4–6 weeks old female C57black/6 mice were used for infection assays . Mice were treated with 5% thioglycolate in PBS given intraperitoneal four days prior to experiment . On day 4 mice were sacrificed and PECs were prepared by rinsing the peritoneum with 10 mL of sterile PBS . PECs were washed once and seeded at a density of 106 cells per well in a 12-well plate on coverslips in RPMI-medium supplemented with 10% fetal calf serum , 5 mM glutamine and 50 µg/mL gentamycin . After incubation under 5% CO2 at 37°C for 24 hours , PECs were incubated with L . donovani parasites at a parasite to PEC ratio of 10∶1 for 48 hours . Non-engulfed parasites were washed away three times with warm RPMI and cells on coverslips were stained with Giemsa and used for microscopic studies . To assess infection rates , the quantities of overall PECs versus infected cells were determined . At least 400 cells in three independent experiments were assessed . All counts were done with coded samples to prevent bias . Animal care and experimentation were performed in accordance with the German Federal Animal Protection Laws , in particular §§ 4 , 7 and 10a , in the animal facility of the Bernhard Nocht Institute . Genomic DNA from L . donovani logarithmic promastigotes was prepared using the Puregene DNA Purification System ( Gentra Systems ) according to the manufacturer's recommendations . Two primers were designed based on the sequence of the L . major gene 5852119 ( hypothetical protein CAB55364 ) : sense primer CAB-S27 ( 5′-GAGACATATGTCAGTGGTGGCTGCCAA-3′ ) ; antisense primer CAB-AS27 ( 5′-GAGAGGATCCCTATTTGGCCTTCTGAG -3′ ) . CAB-S27 and CAB-AS27 were used to PCR-amplify L . donovani genomic DNA ( 95°C for 1 min , 50°C for 1 min , 72°C for 2 min; 30 cycles using the Perkin Elmer DNA Thermal Cycler 480 ) . The amplified product ( 1857 bp ) was gel-purified and cloned into the pCR 2 . 1-TOPO vector . The gene was sequenced using the Big Dye Terminator PCR cycle sequencing kit as per the manufacturer's instructions ( Applied Biosystems ) . RNA from L . donovani promastigotes and in vitro differentiated amastigote cells was isolated by subjecting the parasites to repeated cycles of freezing and thawing in TRIzol . For Northern blotting , agarose gels were loaded with 20 µg of total RNA . After transfer to a nylon membrane , the blots were sequentially hybridized with radio-labeled lddc2 and ß-tubulin probes . Hybridizations were performed in 0 . 5 M Na2HPO4 , 7% SDS , and 1 mM EDTA ( pH 7 . 2 ) at 70°C . Blots were washed in 40 mM Na2HPO4 and 1% SDS ( pH 7 . 2 ) at 70°C . The PCR-amplified DNA fragment coding for lddc2 full-length protein was cloned into the prokaryotic expression plasmid pJC45 , a derivative of pJC40 [20] , using the restriction enzymes NdeI and BamHI . Following transformation in E . coli BL21 ( DE3 ) [pAPlacIQ] the protein was expressed following standard procedures . Recombinant protein was isolated using Ni-NTA resin according to the manufacturer's recommendations ( Qiagen , Hilden , Germany ) . 200 µg of recombinant LdDC2 was injected subcutaneously into a chicken . The first injection was done in combination with complete Freund's adjuvant , the following two booster injections were done in combination with incomplete Freund's adjuvant at two-week-intervals . Antibodies were purified from eggs using increasing concentrations of polyethyleneglycol 6000 [21] . 10% SDS-PAGE was performed under reducing conditions . Samples from promastigotes and in vitro derived amastigotes were obtained by lysing the cells directly in hot SDS sample buffer ( 95°C , 125 mM Tris-HCl pH 6 . 8 , 20% glycerine , 20% SDS , 20 mM DTT , 0 . 001% bromophenolblue ) . Western Blot analyses were carried out using the semidry blotting technique with electrophoresis buffer ( 0 . 25 M Tris , 0 . 5 M glycine , 1% SDS ) as blotting buffer . Polyclonal chicken antibodies ( LdDC2 1∶500 ) or monoclonal mouse antibodies ( Anti-β tubulin clone Tub 2 . 1 ( Sigma ) and an alkaline phosphatase conjugated anti-chicken IgM or anti-mouse IgG ( Sigma ) , as secondary antibody , were used to detect the protein with the 5-bromo-4-chloro-3-indolyl-phosphate ( BCIP ) /nitro blue tetrazolium ( NBT ) color developmental substrate ( Promega ) . Primers CAB-5′UTR ( S38 ) E/S ( 5′-GAGAATTCATTTAAATCCAAGCAAAGGCGAATACATAT-3′ ) ; CAB-5′UTR ( AS37 ) B/K ( 5′-GAGGATCCGGTACCGACCAAGTCCACCAATGTACG-3′ ) and CAB-3′UTR ( S31 ) B ( 5′-GAGGATCCGCGACAGCATGCCAGCAACACGG-3′ ) and CAB-3′UTR ( AS37 ) H/S ( 5′-GAAAGCTTATTTAAATTCTGCGTAGCCTGTGTGTGG-3′ ) were used to PCR amplify the 5′UTR and 3′UTR of CAB55364 from genomic L . major DNA . The plasmid pUC19 was used as a cloning vector . Δlddc2:neo and Δlddc2:pac were constructed by ligating the 5′UTR-PCR-fragment into the EcoRI and BamHI restriction sites followed by ligation of the 3′UTR-PCR-fragment into the BamHI and HindIII restriction sites of the pUC19 vector . The selection markers neomycinphosphotransferase ( neo ) and puromycinacetyltransferase ( pac ) were ligated via integrated restriction sites for KpnI ( at the end of 5′UTR fragment ) and BamHI ( at the beginning of 3′UTR fragment ) . Before transfection , the integration constructs were separated from the vector backbone by digestion with the enzyme SwaI . The Leishmania-specific expression vector pX63pol ( kindly provided by Dr . Martin Wiese , Strathclyde Institute of Pharmacy and Biomedical Science , Glasgow , Scotland ) was used to express lddc2 in L . donovani Δlddc2n/p promastigotes . The two primers CAB-S27 and CAB-AS27 were used to PCR-amplify the coding region of lddc2 . The product was digested with NdeI and BamHI , the 5′overlapping ends were filled in by using Klenow polymerase to create blunt ends . The vector was digested with EcoRV and ligated with the prepared insert . The correct orientation and sequence was re-confirmed by nucleotide sequencing . Plasmid-DNA was purified by using the Nucleobond AX PC2000 Maxiprep-Kit ( Macherey & Nagel ) . For episomal expression 100 µg of DNA was used per transfection; 5 µg of DNA was used for integration via homologous recombination . Parasites were transfected by means of electroporation . Cells were harvested during late log phase of growth , washed twice in ice-cold PBS , once in prechilled electroporation buffer ( 21 mM HEPES pH 7 . 5 , 137 mM NaCl , 5 mM KCl , 0 . 7 mM Na2HPO4 , 6 mM glucose ) and suspended at a density of 1×108 cells/mL in electroporation buffer . Chilled DNA was mixed with 0 . 4 mL of the cell suspension , which was immediately used for electroporation using a Bio-Rad Gene Pulser . Electrotransfection was carried out in a 4 mm electroporation cuvette at 3 . 750 V/cm and 25 microfarads . After electroporation , cells were kept on ice for 10 min before being transferred into 10 mL of antibiotic-free medium . After 24 h , the transfectants were selected with either 50 µg/mL G418 ( neomycin ) , 25 µg/mL puromycin B or 7 . 5 µg/mL of bleomycin . L . donovani promastigotes were added to poly- ( L-lysine ) covered glass slides and air dried . Cells were fixed with 3 . 7% formaldehyde in M199 for 15 min , washed three times in PBS , and permeabilized in PBS/0 . 2% Triton-×100 , and washed three three times in PBS . Subsequently , cells were incubated for 30 min in PBS containing 10% FCS . After blocking , cells were incubated with anti-LdDC2 ( 1∶500 ) , anti-β-tubulin ( 1∶500 ) or anti-PFR2 ( 1∶4 ) antibodies ( provided by Martin Wiese ) , diluted in PBS/10% FCS , following three washes in PBS . Slides were incubated with Cy™2-conjugated anti-chicken IgG antibodies , Cy™2-conjugated anti-mouse IgG antibodies or Cy™3-conjugated anti-mouse IgG antibodies ( Dianova ) , diluted 1∶1000 in PBS/10%FCS and washed another three times in PBS . After incubation with Hoechst 33258 ( Molecular Probes , 1∶2000 in PBS ) , cells were mounted in mounting medium ( Dako Cytomation ) and examined with a Zeiss Axioskop 2 plus immunofluorescence microscope and using the OpenLab software package ( Improvision ) . Scanning electron microscopy ( SEM ) was performed on L . donovani promastigotes that were harvested by centrifugation ( 10 min , 720×g , 4°C ) , washed twice with PBS and fixed with 2% glutaraldehyde in 0 . 1 M sodium cacodylate buffer ( pH 7 . 2 ) and postfixed with 1% OsO4 . Cells were dehydrated in increasing ethanol concentrations ( 30–100% ) , subjected to critical point drying , coated with gold , and viewed with a Philips SEM 500 electron microscope . For transmission electron microscopy ( TEM ) , cells were treated as described above and dehydrated with graded ethanol solutions and propylene oxide . Parasites were embedded in an epoxy resin ( Epon ) . Ultrathin sections ( 70 nm ) were cut ( Ultra Cut E; Reichert/Leica , NuBlock , Germany ) and counterstained with uranyl acetate and lead citrate . Sections were examined with a FEI TECNAI SPIRIT transmission electron microscope at an acceleration voltage of 80kV . Phase contrast microscopy and flagellar length determination were performed on a Zeiss Axioskop 2 plus microscope . Parasites were stained with Giemsa and analyzed microscopically . The flagellar length was measured from the cell body to the tip of the flagellum using the ImageJ software .
In the course of a proteome analysis of the in vitro stage differentiation of L . donovani , the hypothetical protein CAB55364 was found to be expressed in an amastigote-specific manner [18] . Primers deduced from the coding region of the orthologous L . major gene ( accession no . 5852119 ) were used to amplify the corresponding DNA by PCR from L . donovani genomic DNA . The product obtained comprised 1857 bp and showed 96% sequence identity to its L . major homologous . Southern blot analysis indicated that the investigated gene is single-copy per haploid genome ( data not shown ) . It encodes a hypothetical protein of 618 amino acid residues , a calculated Mr of 70 , 000 and a pI value of 5 . 1 . The protein is a putative homologous of the 70 kDa subunit of the outer dynein arm docking complex of Chlamydomonas reinhardtii ( CrDC2 ) [14] . This protein complex consists of three subunits and is essential for the assembly of the outer dynein arm onto the doublet microtubule of C . reinhardtii flagella . C . reinhardtii , an unicellular , biflagellate green algae of the order Volvocales , serves as a model organism for studying eukaryotic cilia and flagella . Figure 1 shows a comparison of the amino acid sequences of the identified L . donovani protein with CrDC2 and four additional DC2 proteins from other organisms . CrDC2 has a high α-helical content and comprises three regions with a high probability to form coiled-coil structures [14] . This is a structural motif in which α-helices are coiled together like the strands of a rope creating a so called superhelix . CrDC2 is thought to interact with the other two subunits of the complex via this structure . The PAIRCOIL program ( http://paircoil . lcs . mit . edu/cgi . bin/paircoil ) indicates that the L . donovani homologous also has several regions predicted to form coiled-coil structures . These are between amino acids 114–157 , 184–228 , 386–415 and 586–618 ( Fig . 1 ) . In addition , the protein contains a calcium binding EF hand motif between amino acid 576–588 and three potential MAP kinase SP phosphorylation motifs in the C-terminal part of the protein with potential phosphorylation sites at residues S493 , S515 and S532 . Because of the demonstrated function of DC2 in C . reinhardtii , a flagellar localization of the protein in L . donovani was predicted . It was shown earlier that the transport of proteins into the flagella of trypanosomatid organisms is mediated through specific signal sequences [22]–[24] . However , we could not find any of the described motifs within the sequence of the investigated protein . Supplementary to CrDC2 , homologous in other kinetoplastid species such as Trypanosoma brucei ( 45% identity ) and T . cruzi ( 42% identity ) were found . Additional homologous could be identified in Micromonas sp . ( 23–29% identity ) , Ciona intestinalis ( 26% identity ) , Paramecium tetraurelia ( 26% identity ) , Tetrahymena sp . ( 20–23% identity ) , Giardia lamblia ( 23% identity ) , Drosophila sp . ( 22–24% identity ) and also in humans ( 20–22% identity ) . Figure 1 shows an alignment of the L . donovani protein to the C . reinhardtii , the Ciona intestinalis , one Drosophila and one human homologous . All of the proteins are similar in their predicted size and the identities extend throughout the whole sequences with the L . donovani protein showing the highest similarity ( 26% ) to CrDC2 . No homologous proteins could be found in organisms lacking motile flagella or cilia such as yeast or Arabidopsis . In Caenorhabditis elegans only a protein that shares 20% homology over the first 300 amino acids can be identified ( GeneBank accession no . CAA50183 ) . This molecule , which is termed IF-2 ( MUA-6 , cytoplasmic intermediate filament ) , is localized in the hypodermis . It is required for hypodermal integrity and the attachment of muscles to the body wall [25] . So far , there is no hint that a CrDC2 homologous protein exist in C . elegans . Based on these phylogenetic data , we propose that the identified protein is the 70 kDa subunit of the ODA-DC of L . donovani and we named it LdDC2 accordingly . In order to investigate the expression pattern of LdDC2 Northern blot analysis with RNA isolated from day 0 to day 5 of the in vitro stage differentiation of L . donovani was performed . The complete coding region of LdDC2 was used as a probe . This analysis revealed a ca . 4 kb transcript which showed a decreasing intensity in the course of differentiation ( Fig . 2A ) . Protein amounts were examined with the help of a Western blot analysis of cellular extracts from all days of stage differentiation . For this , the full-length LdDC2 protein was synthesized in E . coli as an N-terminally His-tagged protein . Matrix-assisted laser desorption ionization time of flight mass spectrometric analysis of the product after digestion with trypsin confirmed the identity of rLdDC2 ( data not shown ) . A polyclonal chicken antiserum generated against rLdDC2 detected a band with an estimated size of about 73 kDa that was exclusive to the promastigote stage ( day 0 ) of the parasite ( Fig . 2B ) . The subcellular localization of the protein was determined by indirect immunofluorescence microscopy . The LdDC2 antibodies stained the flagella of promastigote parasites ( Fig . 3 ) . We could also detect signals within the cytoplasm of the cells . Since this staining was also observed in amastigotes ( data not shown ) , it may be due to a cross- reactivity of the antibodies that is specific for immunofluorescence . In order to further characterize the function of LdDC2 in L . donovani a null mutant of the gene was generated . Due to the lack of sequence information of the L . donovani genome , primers deduced from the untranslated regions of the L . major dc2-gene were used to amplify the respective 5′ - and 3′ -UTRs of L . donovani . The generated PCR products showed 95% ( 3′ UTR ) and 97% ( 5′ UTR ) identity to the L . major sequences . These products were employed to assemble transfection vectors to induce homologues recombination events in L . donovani . After successful ligation of the selection markers neomycinphosphotransferase and puromycinacetyltransferase , the two constructs Δlddc2:neo and Δlddc2:pac were used to transfect L . donovani promastigotes . Drug resistant parasites were cloned and the selected cells checked for the presence of lddc2 . Figure 4 shows the PCR results for two independent Δlddc2n/p null mutant clones . No specific lddc2 fragment ( 1800 bp ) could be generated ( Fig . 4 ) . The two additional DNA fragments of 750 and 2300 bp are unspecific side products produced by cross reactions of the primers with other regions of the L . donovani DNA . Both clones ( Δlddc2n/p-1 and Δlddc2n/p-2 ) were used for further experiments . In order to test whether the successful replacement of both alleles of the lddc2 gene is accompanied by the loss of the corresponding protein , a Western Blot analysis was performed . The LdDC2 antiserum did not detect the protein in the lysates from the two null mutants Δlddc2n/p-1 and Δlddc2n/p-2 ( Fig . 5A ) . Immunofluorescence assays ( IFAs ) showed the same results ( Fig . 5B ) . Only parasites transfected with the control plasmid pX63pol showed the typical staining of the flagellum ( Fig . 5B ) whereas the null mutants did not exhibit any staining of the flagella . They only display an unspecific cytoplasmic staining probably due to cross reactions of the antibodies ( Fig . 5B ) . A reconstitution of the null mutants through episomal expression of lddc2 ( ΔLdDC2n/p-1 + LdDC2:pX63pol ) restored the expression of the protein within the cells as shown by Western blot analysis ( Fig . 5A ) and IFAs ( Fig . 5B ) . A striking consequence of the lddc2 gene replacement is an altered cell shape and flagellar length . To document the aberrant morphology , IFAs with a β-tubulin specific antibody were performed . The microscopic images of this analysis are shown in Figure 6A . The two mutants Δlddc2n/p-1 and Δlddc2n/p-2 exhibit a rounded cell shape and a drastically reduced flagellum . By contrast , WT and reconstituted mutants show the normal promastigote spindle shaped form with long flagella ( Fig . 6A ) . The average mean flagellar length of WT promastigotes ( n = 119 ) was 11 . 6±2 . 4 µm whereas the mutant parasite lines displayed a mean flagellar length of only 3 . 7±1 . 4 µm ( Δlddc2n/p-1 , n = 137 ) and 2 . 9±1 . 0 µm ( Δlddc2n/p-2 , n = 140 ) . Transgenic expression of lddc2 ( Δlddc2n/p-1+LdDC2:pX63pol , n = 119 ) restores flagellar length ( 9 . 5±3 . 4 µm , Fig . 7A ) . In addition , we performed IFAs with an antibody directed against the flagellar protein PFR2 ( Fig . 6B ) . Once more , the reduced flagellar length in the null mutants can be clearly observed . Scanning electron microscopic analysis of WT parasites and null mutants confirmed the observed phenotype ( Fig . 6C ) . Using transmission electron microscopy on cross-sections of chemically fixed promastigote cells , the flagellar ultrastructure was examined . Interestingly , the flagellar ultrastructure of the null mutants was changed . As shown in Figure 8 , the outer dynein arm is present in all WT cells analysed . However , it is missing in the two mutants Δlddc2n/p-1 and Δlddc2n/p-2 . The absence was observed in all analyzed flagellar cross section ( 15 per cell line ) apart from two sections of mutant Δlddc2n/p-1 . A striking difference between WT cells and mutants was observed concerning the motility . While the wild-type L . donovani promastigotes show directed movement across the microscopic field of vision , the mutants , while wiggling in place , are unable to translocate for any distance ( Video S1 , S2 , S3 , supporting information ) . Another distinctive feature of Lddc2 null mutants was a strongly reduced cellular growth ( Fig . 7B ) . Doubling times for the null mutants were ∼80 h , roughly eight times longer than those of WT or of the reconstituted null mutant ( 8–12 h doubling time ) . A population of single-allele gene replacement mutants ( Δlddc2+/n ) showed an intermediary phenotype with a doubling time of approximately 20 h . As both null mutant clones display more resemblance to amastigote than promastigote parasites regarding their morphological shape and growth rates , we looked for the expression of known amastigote marker proteins . Wild type and Δlddc2n/p-1 parasites were subjected to stage conversion conditions for three days . Lysates from these in vitro differentiated cells were tested for the presence of the A2-protein family . Expression of the A2-gene family is a hallmark of the L . donovani amastigote stage [26] and is commonly used as marker for amastigote differentiation . Figure 9 shows a Western blot analysis using anti-A2 monoclonal antibodies . Due to the very different growth rates cell densities for the null mutants were lower . Nevertheless , expression of the A2 gene family can be detected even from day 0 in the null mutants , while wild type parasites do not show detectable A2 protein before day 2 . Thus , null mutants express trace levels of A2 protein in the promastigote which increase rapidly by day 1 and do not change until day 3 ( Fig . 9A ) . PEC infection assays were used to analyze the involvement of LdDC2 in infectivity of L . donovani . WT , Δlddc2n/p-1 and the reconstituted mutant parasites were incubated with mouse peritoneal exudate cells ( PEC ) for 24 hours and examined for intracellular amastigote load . Figure 10 shows the results of three independent experiments . The percentage of infected PECs for the WT parasites is 41±0 . 07% on average . The examined null mutant line caused an average percentage of 68 . 13±0 . 16% . The reconstituted mutant showed an average percentage of 50±0 . 12% infected PECs . The null mutant therefore revealed an increased infection . It is slightly higher than the one of WT parasites . The infectiousness of reconstituted mutant parasites was reduced again . At 24 h , the majority of WT parasites had not been phagocytized yet and were still seen as extracellular promastigotes attached to the host cells . Those cells were not counted . Reconstituted mutants showed a similar phenotype . By contrast , ΔLdDC2 mutants were detected mostly as intracellular amastigotes .
In the course of a proteome analysis of the in vitro stage differentiation of L . donovani a subunit of the outer dynein arm docking complex ( ODA-DC ) was identified as amastigote-specific [18] . A Western blot analysis with an antibody raised against the respective recombinant protein however showed the protein exclusively in the promastigote stage of the parasite . Due to the suspected function of the protein , an amastigote-specific expression can not be anticipated because the parasite only exhibits a rudimentary flagellum during this life cycle stage . Our previous data showed that the theoretically expected and the experimentally determined molecular weights of the identified protein differ greatly . The calculated molecular mass of LdDC2 is 70 , 000 . The protein detected in the 2D-gels of the proteome analysis displayed a molecular weight of ∼35 kDa only [18] . It is possible that the protein detected was a degradation product of LdDC2 accumulated during degeneration of the flagellum in the course of differentiation . However , one would expect to detect this degradation product in the performed Western blot analysis of the stage differentiation ( Fig . 2B ) . This is not the case . We suspected that the recognized epitopes are not functional within the degradation product formed during amastigote differentiation . The proteome analyses of the in vitro stage differentiation of L . mexicana identified the paraflagellar rod protein 2c as amastigote-specific [27] . Here , too , only a fragment of the protein was detected . Apparently , protein degradation products formed during the differentiation into amastigotes can be detected for at least five days . Northern blot analysis of the lddc2 expression showed a decreasing intensity of the transcript during stage conversion with signals no longer detectable at days 4 and 5 . The Western Blots , by contrast , showed intact LdDC2 protein only in the promastigotes . This indicates that either LdDC2 mRNA is no longer translated or that degradation is upregulated once stage conversion commences . Immunofluorescence studies displayed a flagellar localization of the protein , confirming the predicted function of LdDC2 . Additional staining could be detected in the cytoplasm of the parasites . It is not clear whether this is due to a pool of non-assembled material or if it is an unspecific side reaction of the antiserum . The ODA-DC subunit identified in this study is the 70 kDa subunit DC2 . The protein sequence showed altogether four regions with a high probability to form coiled-coil structures . The function of these structures is usually related to the formation of homo- and heterodimers [28] . For the homologous protein from C . reinhardtii it was shown that these regions are responsible for the interaction of CrDC2 with another subunit of the ODA-DC , DC1 [14] . DC1 contains similar structural motifs and is associated with DC2 [13] . The C-terminal part of CrDC2 contains a short glutamic acid rich repeat followed by a region with a high content of charged amino acids . It was postulated that the interaction with the tubulins of the outer dynein arms as well as with the intermediate chain take place via this region . LdDC2 contains a similar region , albeit shorter than in the C . reinhardtii homologous . In contrast to CrDC2 an additional EF hand motif close to the C-terminus could be identified for the L . donovani protein . This motif is also present in other trypanosomatid DC2 proteins as for example in L . braziliensis , T . brucei and T . cruzi . However , the function of this motif is unclear . The third subunit of the complex DC3 , also contains such sequence motifs . It was proposed that the protein is involved in the Ca2+ dependent regulation of the activity of the outer dynein arm [12] . While searching the L . major protein database homologous for all subunits of the ODA-DC could be found supporting the concept that the outer dynein arms in Leishmania are also anchored to the A-tubule via an ODA-DC . The composition of flagella and cilia show a remarkable conservation throughout the evolution [29] . A large number of proteins are needed for the correct assembly of a flagellum . A proteomic analysis of purified C . reinhardtii flagella identified 360 proteins with high confidence and another 292 with moderate confidence [7] . Broadhead and colleagues investigated the flagellar proteome of T . brucei and found it to be constituted of at least 331 proteins [30] . All these flagellar components must to be imported from the cytoplasm as flagella do not contain their own ribosomes . It was shown that specific signal sequences mediate the transport of proteins into the flagella of kinetoplastid organism [22]–[24] . However , dynein arms are assembled within the cytoplasm prior to transport [31] , restricting the need for signal sequences to a few proteins within those large complexes . No known flagellar import signal sequence could be identified within the LdDC2 sequence , indicating that it is transported together with other components of the ODA-DC . The lddc2 null mutants showed a variety of morphological changes as well as a reduced growth rate . Parasites lacking LdDC2 were considerably smaller , with a rounded cell shape . The flagella were shortened , and parasites were not as motile as wildtype promastigote L . donovani . The mutants are unable to translocate for any distance . Instead they wiggle around in one place . The oda1 mutant of C . reinhardtii showed a similar flagellar phenotype [14] , [32] , [33] . These cells lack the outer dynein arm and the ODA-DC . They were isolated initially because of their slow swimming phenotype with a reduced frequency and force of their flagellar beating after a chemical mutagenesis [32] . Later on it was shown that this phenotype was due to a mutation within the crdc2 gene leading to the generation of a stop-codon right after the translation initiation site [14] . The lack of LdDC2 in Leishmania causes a much stronger phenotype . The null mutation not only affects the motility of the cells , but their entire morphology including flagellar length and ultrastructure . Indirect immunofluorescence microscopy , light microscopy , and scanning electron microscopy all confirm that the lddc2 null mutant displays reduced flagellar length . To analyze this phenotype more closely transmission electron microscopy of flagellar cross-sections was performed . The flagellar ultrastructure shows that like in the oda1 mutant of C . reinhardtii the flagella lack the outer dynein arm . Apart from this the mutants possess a normal axoneme and the typical PFR structure . Immunofluorescence studies also confirmed the presence of the paraflagellar rod protein PFR2 in the null mutants . Several studies have shown that the reduction of flagellar correlated with a change in overall cell morphology in other trypanosomatid organisms . The disruption of the cytoplasmic dynein-2 heavy chain gene DHC2 . 2 in L . mexicana resulted in immotile parasites with a rounded cell body . Ultrastructural analysis revealed short flagella that lacked the paraflagellar rod and contained a disorganized axoneme [6] . In Chlamydomonas and C . elegans , cytoplasmic dynein-2 is one of the motor proteins that power the intraflagellar transport ( IFT ) [34] , a bidirectional active transport of components required for the flagellar assembly . LmxDHC2 . 2 seems to be required for maintenance of promastigote cell shape and correct assembly of the flagellum . A similar phenotype could be observed in RNAi generated knock-down mutants of IFT proteins in trypanosomes [35] . Down-regulation of IFT leads to assembly of a shorter flagellum . Cells with a shorter flagellum are smaller , with a direct correlation between flagellum length and cell size . The deletion of the ADF/cofilin gene in Leishmania likewise results in non-motile cells with reduced flagellar length and severely impaired beat frequency . The PFR is not assembled , vesicle-like structures appear throughout the flagellum and actin distribution is altered markedly [36] . It was speculated that ADF/cofilin driven actin dynamic activity is required for intracellular trafficking of flagellar proteins from the cytoplasm to the flagellar base . Deletion mutants of the MAP kinase homologue MPK3 in L . mexicana also leads to reduced flagellar length , stumpy cell bodies and vesicle and membrane fragments in the flagellar pocket [37] . The authors speculate that LmxMPK3 might be involved in the regulation of IFT . The absence of a correct PFR structure in all described mutants suggests that the IFT is severely impaired and this might be responsible for the observed phenotypes as PFR assembly seems to be mediated by IFT [22] , [38] . LdDC2 null mutants do not lack the PFR . Therefore , the observed reduction of the flagellum and the abnormal cell morphology seems to be a consequence of another mechanism . In 2003 , Wiese et . al . postulated that a MAP kinase kinase of L . mexicana ( LmxMKK ) is involved in the regulation of flagellar length in promastigote cells . The gene is promastigote-specific and a null mutant showed shortened flagella . The mutants were able to induce lesions during an infection of BALB/c mice , albeit with delay [39] . In addition , as already described , null mutants of the MAP kinase 3 of L . mexicana ( LmxMPK3 ) also possess shortened flagella . Contrary to the MKK knock-out MPK3 is not required to establish an infection in mice [37] . It is not known so far how flagellar length is regulated . Since over 80 phosphorylated proteins were identified in the flagella of Chlamydomonas [40] , [41] the involvement of protein kinases and classical signal transduction pathways is quite likely . The amino acid sequence of LdDC2 contains three potential MAP kinase phosphorylation sites in the C-terminal region , and the homologous protein in L . mexicana ( having the same phosphorylation sites ) is indeed phosphorylated . However , in vitro kinase assays using in vivo activated LmxMPK3 and LdDC2 showed that the ODA-DC subunit most likely is not a substrate for MPK3 ( Erdmann , personal communication ) . Additional phosphorylation studies will be necessary to clarify the regulatory mechanisms underlying flagellar length control . Another consequence of LdDC2 knock-out was the deregulated expression of the amastigote-specific protein family A2 . Expression can already be detected in the promastigote cells with an increase early during differentiation . If and by which mechanism ( s ) the loss of a structural protein of the flagellum influences the expression of other proteins remains to be clarified . The degeneration of the flagellum however is a central event during differentiation into the amastigote stage , and it is conceivable that the accumulation of other flagellar proteins in parasites that cannot assemble full-length flagella may cause unfolded protein stress and thus mimic the heat stress that is the key signal for stage conversion [42] . The infectivity of LdDC2 null mutants was slightly increased compared to wild type L . donovani . We could show that at 24 h after infection , most wild type parasites were attached to the outside of the host cells and only a limited percentage of the host cells showed intracellular parasites . For the LdDC2 null mutants we saw higher rates of infection and fewer extracellular parasites could be found . The interaction between Leishmania and their host cells is very complex . The two major surface molecules involved in macrophage binding are GP63 , a surface metallo protease and various phosphoglycans including LPG ( Lipophosphoglycan ) [43] , [44] . LPG molecules form a dense glycocalyx on the surface of the promastigotes , including the flagellum . Both molecules , GP63 and LPGs , are virulence factors essential for the survival of L . major in the insect vector as well as in the vertebrate host [45]–[47] . Zhang and Matlashewski showed that the A2 proteins constitute bona fide virulence factors . Antisense-mediated reduction of A2 protein synthesis in L . donovani caused a greatly reduced infectiousness in vitro and in vivo [26] , [48] . Furthermore , expression of A2 proteins in L . major which lacks these genes changed the pathology of L . major [48] . Therefore , the increased expression of the A2 protein family in the LdDC2 null mutants may account for the increased infection rates . An equivalent gene replacement in L . major should allow the use of a mouse infection model to test whether the changes observed in vitro with L . donovani are reflected in the animal host . In summary , we can conclude that the correct assembly of the flagellum has a great influence on the investigated characteristics of Leishmania parasites . The lack of only one flagellar protein leads to a completely different morphology and slows down proliferation . In addition , the parasite's ability to invade host cells is slightly enhanced . It will be interesting to see whether the lack of other structural proteins of the flagella may have a similar impact .
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Leishmania parasites are responsible for the disease leishmaniasis . They are spread through sandflies . The primary hosts are mammals , including humans . They occur in two different morphological forms . The flagellated promastigotes live in the gut of the sandfly vector . After transmission to the mammalian host they get phagocytized by macrophages and convert into the amastigote form , which is able to survive within the phagolysosome . The molecular mechanisms underlying this transformation process from promastigote to amastigote are poorly understood so far . A striking difference of the life cycle stages is a long flagellum in the promastigote compared to only a rudimentary flagellum in the mammalian stage amastigote . During an earlier study of the stage differentiation of Leishmania donovani we identified a flagellar protein , a subunit of the outer dynein arm docking complex ( ODA-DC2 ) . This protein is part of a flagellar structure called the axoneme . Here we have further characterized the protein regarding its role within the life cycle of the parasite . Mutant promastigotes lacking DC2 protein show reduced flagellar length and a more amastigote-like appearance overall . In addition , the motility is heavily retrenched and transmission electron microscopy indicated that the flagellar ultrastructure is affected . Furthermore , the mutants express amastigote-specific genes and show increased in vitro infectiousness towards macrophages . Therefore , we conclude that the correct assembly of the flagellum is vital for maintenance of the promastigote stage of the parasite .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"cell",
"biology/cell",
"growth",
"and",
"division",
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"biology/microbial",
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2010
|
Characterization of a Subunit of the Outer Dynein Arm Docking Complex Necessary for Correct Flagellar Assembly in Leishmania donovani
|
Thermodynamic measurements of ion binding to the Streptomyces lividans K+ channel were carried out using isothermal titration calorimetry , whereas atomic structures of ion-bound and ion-free conformations of the channel were characterized by x-ray crystallography . Here we use these assays to show that the ion radius dependence of selectivity stems from the channel's recognition of ion size ( i . e . , volume ) rather than charge density . Ion size recognition is a function of the channel's ability to adopt a very specific conductive structure with larger ions ( K+ , Rb+ , Cs+ , and Ba2+ ) bound and not with smaller ions ( Na+ , Mg2+ , and Ca2+ ) . The formation of the conductive structure involves selectivity filter atoms that are in direct contact with bound ions as well as protein atoms surrounding the selectivity filter up to a distance of 15 Å from the ions . We conclude that ion selectivity in a K+ channel is a property of size-matched ion binding sites created by the protein structure .
Potassium channels conduct K+ ions at nearly diffusion-limited rates , and at the same time , they prevent Na+ ions from conducting [1] . The ability to distinguish between K+ ions ( Pauling ionic radius 1 . 33 Å ) and Na+ ions ( Pauling ionic radius 0 . 95 Å ) occurs within a segment of the pore known as the selectivity filter ( Figure 1A ) . Inside the selectivity filter , K+ ions are coordinated by oxygen atoms from the protein , which replace the water molecules that normally surround a hydrated ion ( Figure 1B ) [2 , 3] . Several different operational definitions of ion selectivity or relative permeability exist in the electrophysiology literature [4 , 5] . Ion selectivity is sometimes quantified by comparing the conduction rates of different ions , which is analogous to defining the substrate specificity of an enzyme by measuring the catalytic rate with different substrates [6 , 7] . Other times , ion selectivity is defined by a measure of the degree to which one ion will conduct relative to another ( out compete ) when both ions are present in solution at the same time , akin to a substrate competition assay . These different definitions are not related in any simple manner , and their physical interpretation ( in terms of the interaction energy between an ion and the channel ) depends on specific details of the conduction mechanism . Because ion channels generally operate far from equilibrium , it seems reasonable to define ion selectivity as electrophysiologists have , by using the nonequilibrium conditions under which channels normally function . On the other hand it should be possible to characterize ion selectivity under equilibrium conditions if one had a method for quantifying the affinities of different ions for the channel . Selectivity defined in such a way would depend not on the kinetic details of the conduction mechanism , but rather on the energy difference between the ion-bound and unbound states , which should be directly related to the atomic structures of these states . In this study , we have measured ion binding to a K+ channel under equilibrium conditions using isothermal titration calorimetry ( ITC ) , and we have determined the structures of ion-bound and ion-free forms of the channel . These experiments permit us to correlate structural and thermodynamic properties of selective ion binding . Our aim is to understand which property of an ion ( i . e . , size or charge density ) is recognized by the channel and which properties of the channel create an energetically favorable match for the ion .
Figure 2A shows an ITC experiment in which a solution containing KCl is titrated into a solution containing Streptomyces lividans K+ channels ( KcsA ) in NaCl . Each downward deflection results from the heat of diluting KCl from the injection syringe solution into the reaction chamber plus the net heat of transfer of K+ ions from solution to the channel . There are several points to note about this titration . First , there is a gradual decrease in the amount of heat liberated upon successive KCl injections until a constant level is eventually reached . This pattern suggests that the channel's binding sites are becoming saturated so that the final injections represent only the heat of diluting the concentrated KCl solution into the protein solution . In control experiments in which the channel protein is not present in the reaction chamber , one observes only this constant heat of dilution . Second , the binding reaction is exothermic , which means the transfer of K+ ions from water to the channel is enthalpically favored . And third , the rate of heat release ( and thus K+ binding ) is relatively slow , as evidenced by the longer duration of the deflections in the beginning compared to the end of the titration . In Figure 2B , the heat transfer associated with each injection is plotted as a function of the ligand ( K+ ) -to-protein concentration ratio and fit to an equation that incorporates the enthalpy and affinity of a single K+ ion binding event ( see Materials and Methods ) [8] . The fit ( solid line ) corresponds to stoichiometry of binding n = 1 , dissociation constant KD = 0 . 41 mM , and enthalpy ΔH° = −1 . 4 kcal/mol . The inset contains the same data plotted in a more familiar fashion: the fraction of total heat liberated ( the transient component due to K+ binding ) as a function of the K+ concentration . A detailed description of the ion binding model , which is based on a combination of ITC and x-ray crystallographic data , is given in Materials and Methods . When the K+ titration experiment was repeated using LiCl as the background electrolyte ( Figure 2C ) instead of NaCl ( Figure 2A ) , the outcome was similar: heat is liberated upon addition of K+ . By contrast , heat is not liberated upon addition of Na+ , which has a Pauling radius that is smaller than that of K+ ( Figure 2D ) . The larger alkali metal ions Rb+ ( Pauling radius 1 . 48 Å ) and Cs+ ( Pauling radius 1 . 69 Å ) are like K+ , binding to the channel with estimates for KD and ΔH° given in Table 1 . In electrophysiological experiments , K+ channels are known to discriminate strongly against smaller alkali metal ions Li+ and Na+ but permit the larger Rb+ and Cs+ ions to conduct . We therefore observe a correlation between electrophysiological permeability and heat liberation by ITC . Crystal structures of KcsA show that its selectivity filter can exist in two distinct conformations associated with low and high concentrations of K+ ( Figure 3A ) [2 , 9] . In solutions containing less than 5 mM KCl and 150 mM NaCl , the filter adopts a nonconductive conformation , which is pinched closed ( Figure 3A , left ) . Crystallographic occupancy studies show that in this conformation , ions bind at the ends of the filter ( sites 1 and 4 ) , with up to a single K+ ion distributed over these two sites [9] . As the concentration of K+ in the crystallization solution is increased , a second ion enters , in association with a conformational change of the filter to the conductive form ( Figure 3A , right ) . In the conductive form , two K+ ions are distributed over four sites , each with approximately half occupancy [9 , 10] . The crystallographic data have thus demonstrated that the entry of a K+ ion ( into the middle of the filter , sites 2 and 3 ) is associated with a specific conformational change of the selectivity filter . Is the heat transfer associated with ion binding correlated with the filter conformational change ? To address this question , we studied a mutant channel , M96V , which crystallographically remains in the nonconductive conformation at K+ concentrations up to 300 mM K+ ( Figure 3B ) . In the ITC assay of this mutant channel , no detectable heat is liberated upon addition of K+ ( Figure 3C and 3D ) . This mutant channel in every other respect appears like the wild-type channel at low concentrations of K+; the only difference is that as the K+ concentration is raised , the channel fails to undergo the nonconductive-to-conductive conformational change ( and thus fails to bind a K+ ion at sites 2 and 3 ) . These observations suggest that the heat measured in the ITC assay on the wild-type channel is liberated when the filter undergoes its conformational change and binds a K+ ion at sites 2 and 3 ( Figure 3A ) . The coupling of an ion-binding event to a conformational change of the filter can explain why the rate of ion binding is slow in the ITC assay . The readjustment of protein atoms occurs up to a distance of 15 Å from the ion pathway ( Figure 4A ) . The atomic displacements are essentially the same whether K+ , Rb+ , or Cs+ ions bind ( Figure S1 ) . It is undoubtedly significant that the conformational change involves amino acids that are highly coupled to one another in an analysis of sequence co-evolution ( Figure 4B ) . We suspect that these amino acids have been constrained by natural selection to enable the filter to bind K+ at sites 2 and 3 , but not Na+ . The data discussed so far support the following conclusions . Alkali metal ions K+ , Rb+ , and Cs+ can bind to the internal sites ( 2 or 3 ) of the selectivity filter . Their binding is associated with a specific conformational change of the filter to a conductive form and an exchange of heat with the environment . Na+ ( and presumably Li+ ) does not bind to sites 2 or 3 , stabilize the conductive conformation , or liberate heat . An obvious distinction between the different alkali metal cations is their atomic radius . By studying the alkali metal ion series , we have documented how the ionic radius affects the ability of a +1 ion to interact with the selectivity filter . We observed a size cut-off between Na+ and K+ . Next , we investigated a series of different sized alkaline earth ( +2 ) cations . Figure 5A shows an ITC titration with BaCl2 into a KcsA solution with NaCl as the background electrolyte . The fit corresponds to KD = 0 . 17 mM and ΔH° = +5 . 3 kcal/mole ( Figure 5B , solid line ) . By contrast , no measurable heat of ion binding to KcsA is detected with either Ca2+ ( Figure 5C ) or Mg2+ ( unpublished data ) . The ionic radius of Ba2+ ( 1 . 35 Å ) is very close to that of K+ ( 1 . 33 Å ) , whereas the radii of Mg2+ ( 0 . 61 Å ) and Ca2+ ( 0 . 99 Å ) are close to that of Li+ ( 0 . 60 Å ) and Na+ ( 0 . 95 Å ) , respectively . For Ba2+ , ΔH° is positive ( unfavorable ) , but a large favorable entropy term , ΔS° , results in a free energy change ΔG° for binding that is actually slightly more favorable for Ba2+ than for K+ ( Table 1 ) . The important point , however , is that Ba2+ binds to the filter , whereas Ca2+ and Mg2+ do not . This means the size cut-off for selective binding of +2 cations occurs between Ca2+ ( radius 0 . 99 Å ) , which does not bind , and Ba2+ ( radius 1 . 35 Å ) , which binds . This is the same size cut-off that is observed in the +1 alkali metal cation series . The ability of Ba2+ to bind to the selectivity filter as measured using ITC is in good agreement with electrophysiological studies on the interaction of Ba2+ with K+ channels [11 , 12] . Does Ba2+ binding require the protein conformational change within the filter ? An earlier structure of a Ba2+ complex of KcsA could not address this question because it was determined at low resolution ( 5 Å ) [13] . We have determined a Ba2+ structure with crystals that diffract to 2 . 7 Å . A 2Fo-Fc electron density map calculated after omitting the selectivity filter and ions is shown ( Figure 6A , blue mesh ) . An x-ray anomalous signal defines unequivocally the location of Ba2+ ions in the filter at site 4 , as determined previously at lower resolution [13] , and near site 2 at the center of the filter ( Figure 6A , magenta mesh ) . Figure 6B shows in superposition the structures of the Ba2+ complex ( yellow ) , the conductive K+ complex ( blue ) , and the nonconductive ( low K+ ) KcsA structure ( red ) . At amino acid glycine 77 , whose carbonyl oxygen atom coordinates the Ba2+ ion near site 2 , the carbonyl carbon resides at a location in between the conductive and nonconductive conformations . Everywhere else , protein atoms of the Ba2+ complex adopt the conductive structure , as can be seen by the side chain rotamer of valine 76 ( Figure 6B ) and the superposition of aromatic amino acids surrounding the filter for the Ba2+ and conductive K+ structures ( Figure 6C ) . Thus , size selective binding of +2 cations ( i . e . , Ba2+ and not Ca2+ or Mg2+ ) depends on the ability of the filter to adopt the conductive conformation , just as for monovalent cations .
By studying a series of ions with different atomic radii , we observe a dependence on the ability of ions to bind to the interior of the selectivity filter . Among the alkali metal ions , K+ , Rb+ and Cs+ bind and liberate heat , whereas Na+ does not . Among the alkaline earth ions , Ba2+ binds , whereas Ca2+ and Mg2+ do not . The binding events are correlated with specific protein conformational changes , which enable a selected ion to bind to sites 2 or 3 of the selectivity filter . The thermodynamic measurements of ion binding correlate in a qualitative manner with the ability of K+ , Rb+ , and Cs+ to conduct through K+ channels and the ability of Ba2+ to enter the pore and cause blockage . In quantitative detail , however , the “permeability sequence” determined electrophysiologically is not the same as the binding affinity sequence determined in this study . The permeability of K+ for most K+ channels is slightly greater than that of Rb+ , which is greater than that of Cs+ , whereas the permeability of Na+ and Li+ is extremely small [5 , 14] . Ba2+ blocks K+ conduction but itself , conducts poorly . In the setting of these significant electrophysiological differences between the ions , the equilibrium binding affinities for K+ , Rb+ , Cs+ , and Ba2+ are fairly similar to each other ( Table 1 ) . This difference between permeability ( large variation ) and affinity ( small variation ) is not a discrepancy , because permeability and binding affinity are two different entities . Permeability is a nonequilibrium quantity reporting how well an ion conducts , which depends on the depth of energy wells and the height of energy barriers encountered by ions as they diffuse through the pore . Binding affinity is an equilibrium quantity and depends on the path-independent energy difference between the reactants ( channel + ion ) and product ( complex ) . How does the ion-binding event that is the focus of this study relate to ion conduction ? The affinity of the K+ ion for the filter ( KD ∼ 0 . 5 mM ) precludes the possibility that transport is simply the forward and backward steps of the binding reaction . The KD places an upper limit on the rate at which an ion can dissociate ( and thus be transported ) because koff = kon × KD . For example , if kon were diffusion limited and had a rate of 108 M−1s−1; then KD would limit koff ( and therefore the conduction rate ) to about 105 ions s−1 . Thus , we assume that when the filter is conducting ions at a high rate ( up to about 108 ions s−1 ) , it remains in its conductive conformation as ions flow through . This assumption is compatible with the slow rates of ion binding in the ITC assay: once binding has occurred in association with the conformational transition to the conductive form , which may be slow , K+ can then diffuse through at a high rate . The above discussion implies that the ITC experiments record an ion-binding event ( associated with a protein conformational change ) that is not a step in the conduction process , but rather one that puts the filter into its conductive conformation . Nevertheless , the specificity of this binding event ( K+ and not Na+ mediated ) appears to be deeply rooted in the ability of the filter to select the correct ion by stabilizing a specific ( conductive ) structure in response to ions that satisfy a certain criterion . What is the criterion or physical property of an ion that allows it to enter the filter and stabilize the conductive conformation ? Obviously ion size is important . Is this because the binding site ( s ) favor an ion for its size or for the strength of the electric field near its surface ? The electric field strength , which is proportional to the charge density , is of course a function of an ion's size . By studying ions with different ionic radii and different charges , we find that the ability to bind and stabilize the conductive conformation depends on the same cut-off radius for both +1 and +2 cations . If the ability of an ion to bind depended on field strength as a “primary” property , then Na+ and Ba2+ should be similar . This is not the case: K+ and Ba2+ , which have different electric field strengths at their surface but nearly the same size , bind with similar affinities . This result informs us that an ion's size , not simply through the effect of size on field strength , is an important criterion for selectivity in K+ channels . How does the K+ channel detect the size of an ion ? The crystal structures in different ionic solutions show that the selectivity filter has the potential to adopt two distinct , well-defined conformations that we call conductive and nonconductive . The conductive conformation is associated with ion-binding sites that are each formed by eight carbonyl oxygen atoms ( sites 1–3 ) surrounding a “hole” into which a K+ ion snugly fits and satisfies its preference for six to eight coordinating oxygen atoms [15] . A very slight rotation of the carbon–oxygen bond permits larger ions such as Rb+ and Cs+ to fit snugly into these same sites without perturbing the conductive conformation . The sites in the conductive conformation are not compatible with the smaller radius of Na+: given only Na+ in solution , we observe experimentally that the channel enters the nonconductive conformation rather than binding Na+ at sites 2 and 3 ( i . e . , the nonconductive channel is more stable than a Na+-bound conductive channel ) . Thus , in answering the question how does the K+ channel detect the size of an ion , we are led to conclude that the conductive conformation is associated with ion-binding sites that match the size of K+ but are too large for Na+ . The channel is thus able to compensate for the dehydration energy of K+ but not Na+ . It is the conductive conformation of the channel rather than the transition from nonconductive to conductive that confers selectivity . After all , when the nonconductive conformation is prevented by mutation , the channel still binds K+ selectively over Na+ [16] . However , the crystallographically observed transition is fortuitous , because it reveals to us the regions of the channel that are responsible for creating the binding sites as they are in the conductive conformation . The filter atoms and the surrounding protein atoms are important for creating the selective ion-binding sites ( Figure 4 ) . The above data and conclusions are in agreement with the general ideas behind the “snug fit” hypothesis of Armstrong and Hille for ion binding in a selective channel [17 , 18] . The data are not in agreement with a recent study using molecular dynamic simulations of KcsA , in which it was suggested that structural constraints imposed by the channel protein are not important [19] . The authors of the simulation study proposed that selectivity is a function of local chemical interactions provided by a relatively unstructured carbonyl oxygen selectivity filter ( described as liquid-like ) , which creates a proper electrostatic environment for K+ . Local interactions between carbonyl oxygen atoms and K+ ions are certainly important , but the data presented here would seem to suggest that the protein structure is actually very important in the creation of size-constrained ( does not mean rigid ) ion binding sites . The recent structure of a cation-selective channel , called the NaK channel , reinforces this same conclusion [20] . Its selectivity filter contains two binding sites that are chemically identical ( in terms of the coordinating oxygen atoms ) to sites 3 and 4 in a K+ channel , and yet these sites are not very selective for K+ over Na+ , apparently because the surrounding protein atoms are different and thus impose different constraints upon the sites . It is useful to place selectivity in the K+ channel in the broader context of ion selectivity by synthetic host molecules . Cram , Lehn , and Pedersen demonstrated that ion selectivity could be achieved by constraining the size of the binding site to match the size of the ion [21–23] . We suggest that the K+ channel achieves selectivity by the very same principle , using the protein structure to create carbonyl oxygen-based ion- binding sites that are appropriately sized for K+ and not for Na+ . In the K+ channel , we observe a new level of complexity added to this principle of size selectivity: the presence of K+-selective sites 2 and 3 is coupled to a specific conductive conformation of the selectivity filter . Consequently , K+—the ion that is to be conducted—stabilizes the conductive conformation . The K+-dependent conformational change represents a form of selectivity , because in the absence of K+ ( i . e . , in the presence of only Na+ , which should not be conducted ) , the filter goes into a nonconductive conformation .
KcsA was expressed and purified as described previously [2] . The A98G mutant of KcsA was used in all ITC measurements , because equilibration was reached more quickly following each ionic solution injection . This mutant is referred to as wild type in this study . KcsA was cleaved with chymotrypsin and the tetramer purified over a Superdex 200 column equilibrated with 50 mM Tris , pH 7 . 5 , 20 mM KCl , 100 mM NaCl , and 5 mM DM ( n-decyl-β-d-maltopyranoside ) . The purified protein was concentrated to 10 mg/ml , extensively dialyzed against the desired buffer , and diluted just prior to ITC experiments . Protein concentrations were determined by absorbance at 280 nm where 1 optical density ( OD ) = 0 . 4 mg/ml . KcsA is stable in these conditions and was reused after exchanging it into the appropriate buffer . Measurements of the enthalpy change ( ΔH° ) upon ion binding to KcsA were performed using a VP-ITC MicroCalorimeter ( MicroCal; http://www . microcal . com ) . Any given experiment was carried out at a constant temperature ( ±0 . 005 °C ) within the range of 21 °C to 23 °C . The sample cell ( V = 1 . 3628 mL ) was filled with a solution containing 25 mM HEPES , pH 7 . 5 , 100 mM NaCl ( except experiments done with 100 mM LiCl ) , 5 mM DM and 71–142 μM KcsA . The injection syringe was filled with a mono- or divalent salt ligand solution containing 25 mM HEPES , pH 7 . 5 , 100 mM NaCl ( or 100 mM LiCl ) , 5 mM DM and 15–100 mM XCln , where X is the desired ion . All solutions were filtered and degassed prior to use . Twenty to thirty injections of 3–10 μL of ligand solution were titrated into the KcsA protein solution while stirring at 500 rpm . The heat change of each injection was integrated over 10 min for monovalent ion solutions and 20 min for BaCl2 solutions , both of which are uncharacteristically slow for simple ion binding , suggesting a slow equilibrium between the nonconductive and conductive conformations ( see next section for model ) . Protonation during ion binding was assessed using solutions containing 25 mM ( Na-phosphate or Tris ) buffers , pH 7 . 5 instead of 25 mM HEPES , pH 7 . 5 in both the cell and syringe ( Figure S2 ) . The four-state reaction scheme shown in Figure 7 is the simplest description of the ion-binding event that is compatible with our experimental data . Equation 1 describes the probability that the channel is in the CX2 state as a function of K+ concentration , denoted as [X] . Crystal structures show two distinct conformations of the selectivity filter , nonconductive ( NX ) , and conductive ( CX2 ) ( Figure 3A ) . NX is observed at low [K+] , and CX2 is observed at high [K+] . Full K+ titrations were carried out between these conformations using crystallography , from which we concluded that K+ stabilizes the conductive conformation [9] . State N is never observed crystallographically , even at high concentrations of Na+ , where the filter adopts its nonconductive conformation , which we correspond to state NX . Since we never observe state N , we assume that K0 is very large , so the quadratic term ( [X]2 ) can be dropped and Equation 1 is approximated by Equation 2 , a rectangular hyperbola ( n = 1 binding isotherm ) with an apparent KD = ( 1 + K1 ) /K1K2 ( referred to as KD in the text ) . . The ITC titration of the wild-type channel is consistent with Equation 2 , since the data fit well to an n = 1 binding isotherm , and attempts to fit n ≠ 1 do not significantly improve χ2 . One might wish to argue that the equilibrium process N to NX could be coming in to the mechanism , but is simply not detectable as a significant deviation from n = 1 . Such an occurrence would not change the conclusions of this study . State CX is also never observed but is included , because we know from crystallography that the pore goes from its nonconductive conformation , with one ion in it at the beginning of a K+ titration , to its conductive conformation , with two ions in it at the end . Mutation M96V prevents CX2 ( filter remains in NX ) even at high [K+] ( Figure 3B ) . Crystallographic ion occupancy studies show that NX harbors an ion near one of its entryways ( referred to as binding sites 1 or 4 ) but not in the middle ( at sites 2 or 3 ) , whereas CX2 harbors an ion near one of its entryways ( sites 1 or 4 ) and in the middle ( sites 2 or 3 ) ( Figure 3A ) [9] . Thus , in the reaction scheme , the transition governed by equilibrium constant K1 describes a conformational change of the filter from NX to CX and the transition governed by K2 describes the entry of a single K+ into the middle of the conductive filter ( to occupy site 2 or 3 ) . Because only states NX and CX2 are observed experimentally , we assume that K1 favors NX and that K2 favors CX2 . According to this scheme , upon addition of K+ in our calorimetric titration the formation of the CX2 state occurs in two steps . We make no assumptions about the detailed origins of the heat generated ( i . e . , which steps contribute ) . However , it is an important observation that the mutation M96V prevents the NX to CX2 conformational transition as documented by crystallographic experiments ( Figure 3B ) , and it also prevents the exchange of heat in the ITC experiments ( Figure 3D ) . In essence , the heat serves as a signal for the nonconductive-to-conductive transition , and thus enables us to investigate how different ions react with the filter under equilibrium conditions . The data were fit in Origin to the equations provided in [8] ( fix n = 1 ) . A constant background was subtracted because the 100-mM NaCl in the cell and syringe created an environment where the heat of diluting the ions from the syringe is constant; an example of this can be seen both in Figures 2D and 3D , where ions do not bind to KcsA . This background was determined from the integration of the final injections and the minimization of χ2 for the overall fit . Ideally , to fit n , K , and H as independent variables , where n is the number of binding sites , K is the association constant , and H is the enthalpy of the reaction , the product of the association constant and the concentration of protein in the cell ( termed “c” ) should be within the range of 5–500 ( see Micro Calorimetry System ITC manual and [8] ) . If c < 5 , such as in our case where c < 0 . 3 , it is still possible to fit K and H if outside knowledge of the stoichiometry of binding is known and if the binding sites are at least nearly saturated during the titration [24] . The fit and error to the fit for K and H for Figure 2B are K = 2440 ± 150 M−1 and H = −1402 ± 49 cal/mol , indicating an excellent fit of the data to the model . At least three full titrations ( with similar errors to the fit ) of each ligand condition were collected , with their means and standard deviations of the mean reported in Table 1 . No K+ ion binding to M96V KcsA was seen in the experimental range described above with up to 35 mM KCl in the syringe ( unpublished data ) . However , it is possible that K+ would bind M96V with a lower affinity than wild type , requiring a higher [KCl] solution in the syringe . The experiments in Figure 3C and 3D were performed with a sample cell solution containing 25 mM HEPES , pH 7 . 5 , 250 mM NaCl , 5 mM DM and 142 μM KcsA , and 25 mM HEPES , pH 7 . 5 , 400 mM KCl , and 5 mM DM in the injection syringe . The different salt concentrations were necessary to compensate for the heat of diluting the 400 mM KCl in the syringe , which when injected into a cell containing 100 mM NaCl , dominated the enthalpy from ion binding to the wild-type channel . To reduce this dilution effect , the NaCl concentration in the cell was adjusted to counterbalance the KCl in the syringe until significant binding of K+ to wild-type KcsA was observed , which occurred at 250 mM NaCl . An alignment was created from 404 K+ channel sequences that were collected from the nonredundant database using PSI-BLAST ( e-score < 0 . 001 ) [25] . The statistical conservation and coupling between positions were calculated as described previously [26] . The network of co-evolving positions was determined from an analysis that seeks self-consistent clusters of positions that statistically co-vary with one another [27] . K+ channels have one self-consistent cluster , which is mapped as the co-evolving positions in Figure 4B . The KcsA-Fab complex was prepared and purified as described [2] . KcsA-Fab in Na+ was obtained by extensively dialyzing the purified complex against a buffer containing 50 mM Tris , pH 7 . 5 , 150 mM NaCl , and 5 mM DM over a period of 48 h . The Ba2+-containing KcsA-Fab complex was prepared by adding BaCl2 ( to a 5 mM final concentration ) to the KcsA-Fab in Na+ just prior to setting up the crystal trays . The KcsA ( M96V ) -Fab buffer solution contained 50 mM Tris , pH 7 . 5 , 300 mM KCl , and 5 mM DM . Crystals were grown at 20 °C by the sitting-drop method by mixing an equal volume of concentrated KcsA-Fab complex ( ∼12 mg/ml ) with a reservoir solution containing 21% PEG400 , 50 mM magnesium acetate , and 50 mM buffer ( HEPES , pH 7 . 0 for Na+ crystals or 50 mM MES , pH 6 . 2 for Ba2+ crystals ) . Crystals were cryoprotected in a single step by increasing the PEG400 concentration in the reservoir solution to 40% , followed by re-equilibration for 1–2 d . All crystals were frozen in propane and stored in liquid N2 . Data were collected at station X25 of the National Synchrotron Light Source ( Brookhaven National Laboratory , Upton , New York , United States ) and at station A1 of the Cornell High Energy Synchrotron Source ( Ithaca , New York , United States ) . The data were processed with Denzo and Scalepack [28] . The structures were solved by molecular replacement using the high K+ KcsA-Fab structure ( Protein Data Bank ( PDB ) code 1K4C ) as a search model in molrep [29] . The models were refined by manual rebuilding using the program O [30] and several cycles of minimization and B-factor refinement using CNS [31] . A random 5% of the reflections were excluded from the refinement to calculate Rfree . Anomalous Fourier difference maps were calculated for the Ba2+ structure in CNS using a model that excludes the bound ions and selectivity filter residues 74–80 .
Structure coordinates and structure factors from the KcsA in BaCl2 , KcsA in NaCl , and KcsA ( M96V ) in KCl crystals have been deposited in the Protein Data Bank ( http://www . rcsb . org/pdb ) with accession ID codes 2ITC , 2ITD , and 2NLJ .
|
The exquisite selectivity of potassium ion ( K+ ) channels in cellular membranes allows them to pass K+ ions while restricting the closely related sodium ( Na+ ) ions , and thereby maintain the electrical potential across cellular membranes . In this study , we address the fundamental question: how does the K+ channel discriminate between K+ and Na+ ions ? Past studies have relied on nonequilibrium measurements of ionic current flow . We measured heat exchange associated with ion binding to the channel under equilibrium conditions and determined crystal structures of ion-bound and ion-free forms of the channel . By studying a series of alkali metal and alkaline earth cations , we documented the effect of varying systematically the ionic charge and radius , and we discovered that the K+ channel recognizes an ion's size rather than its electric field strength . By analyzing the structures , we show that the channel's ability to recognize an ion's size is a function of protein atoms that are both near to and far away from the ion binding sites . This study opens a new window into ion selectivity in channels and also contributes to our expanding knowledge of the emerging role of long-range interactions in ligand recognition .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] |
[
"biochemistry",
"in",
"vitro",
"biophysics",
"neuroscience"
] |
2007
|
Structural and Thermodynamic Properties of Selective Ion Binding in a K+ Channel
|
Axonal transport of synaptic vesicles ( SVs ) is a KIF1A/UNC-104 mediated process critical for synapse development and maintenance yet little is known of how SV transport is regulated . Using C . elegans as an in vivo model , we identified SAM-4 as a novel conserved vesicular component regulating SV transport . Processivity , but not velocity , of SV transport was reduced in sam-4 mutants . sam-4 displayed strong genetic interactions with mutations in the cargo binding but not the motor domain of unc-104 . Gain-of-function mutations in the unc-104 motor domain , identified in this study , suppress the sam-4 defects by increasing processivity of the SV transport . Genetic analyses suggest that SAM-4 , SYD-2/liprin-α and the KIF1A/UNC-104 motor function in the same pathway to regulate SV transport . Our data support a model in which the SV protein SAM-4 regulates the processivity of SV transport .
Neurons innervate their targets at synapses distant from the soma . Most components of these synaptic specializations , including synaptic vesicles ( SVs ) , active zone proteins and mitochondria , are synthesized in the soma and then transported along axons on the microtubule cytoskeleton [1] . Transport along the axon is bidirectional with anterograde transport driven largely by kinesins and retrograde transport carried out by cytoplasmic dynein [2] . Efficient axonal transport is important in many facets of neuronal development and function . Trophic factors , membrane components , guidance receptors as well as synaptic components are all transported down the axon anterogradely , and maintenance of trophic support requires retrograde transport of signaling endosomes containing activated receptors [2] . Abnormal axonal trafficking has been observed in brain disorders including Parkinson's disease , amyotrophic lateral sclerosis , Charcot-Marie-Tooth disease and hereditary spastic paraplegia [3] , [4] , [5] , [6] . The majority of anterograde transport is performed by a large family of plus-end directed motors of the kinesin superfamily ( KIFs ) consisting of 21 genes in C . elegans [7] and 45 genes in mouse [8] . KIFs are composed of three domains: a motor “head” domain , a stalk domain and a cargo-binding domain . In plus end directed kinesins , the globular ATPase motor domain is positioned in the N-terminal region of the protein and provides the force to walk processively on microtubules at mean velocities of around 0 . 5–1 . 5 µm/second [9] . The C-terminal cargo-binding domain is typically separated from the motor by a long coiled coil stalk or “neck” domain , though the size of this domain varies considerably within the family . By contrast with the highly conserved motor domain , the cargo binding domains are variable and determine the cargo specificity of KIFs . Accessory light chain subunits and distinct adaptor proteins provide additional diversity of cargo binding to KIFs [9] . For example , KIF5 binds APP containing vesicles via its light chain [10] , mitochondria via the adaptor Milton [11] and GlrR2 containing vesicles via the adaptor GRIP [12] . Although the cargo binding specificity of numerous kinesins has been defined to some extent , the mechanisms regulating many aspects of kinesin-mediated cargo transport remain largely uncharacterized . One general theme in the mechanisms controlling axonal transport is the regulation of KIF motor activity . The activity of the motor domain of several different KIFs , including KIF1A/UNC-104 , is negatively regulated by their cargo-binding domain in the absence of cargo [13] , [14] , [15] , [16] , [17] , [18] . In addition , activation of the motor in several cases has been documented to require the binding of other factors . For example , the cargo adaptor JIP1 is not sufficient to activate Kinesin-1 , but rather requires the additional cooperative binding of the protein FEZ1 [19] . A RAN-GTPase binding protein has been shown to activate Kinesin-1 ATP activity in vitro [20] . Phosphorylation has also been demonstrated in several cases to regulate cargo binding . For example , CaMKII regulates KIF17 binding to cargo by phosphorylation and GSK3β phosphorylation regulates KIF5 [21] , [22] . In addition , the microtubule associate protein ( MAP ) doublecortin was recently demonstrated to regulate SV transport by enhancing KIF1A motor domain binding to MTs [23] . In summary , regulation of KIF motor activity is complex . One of the identified KIF1A/UNC-104 regulators is Liprin-α/SYD-2 . Liprin-α/SYD-2 belongs to a family of proteins that interact with the cytosolic domain of LAR receptor protein tyrosine phosphatases [24] , [25] . In addition to interacting with LAR , Liprin-α interacts with several presynaptic active zone proteins to regulate active zone development [26] , [27] , . Interestingly , biochemical studies also identified interactions of Liprin-α with KIF1A [32] . In vivo , Liprin-α/SYD-2 is required for SV trafficking in Drosophila [33] and regulates UNC-104 motility in C . elegans [34] . These observations demonstrate that , in addition to the intra-molecular regulation of KIF1A/UNC-104 , its activities are also regulated by other factors . Here , using the C . elegans mechanosensory system as an in vivo model , we identify SAM-4 ( Synaptic vesicle tag Abnormal in Mechanosensory neurons ) as a novel regulator of KIF1A/UNC-104 directed SV trafficking . sam-4 , encodes a conserved SV-associated protein orthologous to human LOH12CR1 [35] that is broadly expressed in neuronal tissue . SAM-4 acts in a cell autonomous manner by binding to SVs to regulate the processivity of anterograde SV transport . sam-4 null mutants show SV trafficking defects in different neuronal cell types . Genetic analyses revealed that SAM-4 acts synergistically with the KIF1A/UNC-104 PH cargo binding domain , but not the motor domain , to regulate SV trafficking and locomotory behavior . Gain-of-function mutations in the unc-104 motor domain suppress sam-4 defects indicating that SAM-4 functions upstream of the motor in regulating SV transport . SYD-2 , which regulates SV trafficking to a lesser extent than SAM-4 , exhibits similar genetic interactions with UNC-104 but no obvious interactions with SAM-4 , consistent with SYD-2 and SAM-4 acting in the same pathway . Imaging of SV cargo movements in vivo demonstrated that SAM-4 is required to maintain cargo processivity rather than motor velocity , while gain-of-function UNC-104 proteins increase cargo processivity . We propose a model in which SV-bound SAM-4 acts in parallel to the UNC-104/KIF1A cargo binding domain to regulate activity of the motor domain .
The response to gentle touch to the body in C . elegans is mediated by a set of six touch receptor neurons ( TRNs: ALML/R , AVM , PLML/R , and PVM ) ( Figure S1A and [36] ) . We use PLM neurons as a simple in vivo system to examine axonal transport of synaptic components . The two PLM soma are located on each side of the body in the tail ganglia ( Figure S1A ) . Each PLM extends a short posterior-directed and a long anterior-directed neurite , which are easy to image because they are in close apposition to the cuticle . PLMs innervate partners via gap junctions and chemical synapses [37] . The chemical synapses are formed in a large varicosity ( ∼5 µm long ) , located at the end of single collateral synaptic branch that extends ventrally from the anterior directed process into the ventral nerve cord , usually just posterior to the vulva ( Figure S1A ) . We examined PLM neurons in vivo by expressing markers using the mec-7 promoter which drives gene transcription selectively in TRNs [38] . SVs preferentially accumulate in the PLM synaptic varicosities as observed using transgenic SV markers SNB-1-GFP [39] and GFP-RAB-3 ( jsIs821 , Figure 1A and 1C ) , similar to SV accumulations revealed at the ultrastructural level [37] , [40] . When anterograde SV trafficking machinery is disrupted by lesioning the UNC-104/KIF1A motor , SV markers accumulate in the soma and proximal portions of PLM neurites rather than being transported to the synapse ( Figure S1B–D′ ) . By contrast , when the retrograde cytoplasmic dynein motor is disrupted , SV markers accumulate abnormally at the distal portions of the anterior process [41] . These observations indicate that homeostatic regulation of SV levels in mechanosensory neuron synaptic varicosities is mediated by the balance of the anterograde and retrograde transport systems . The sam-4 ( js415 ) mutant was isolated in a forward genetic screen for mutations disrupting SV accumulation in PLM synapses , using a SNB-1-GFP transgenic marker [42] . Similar defects were observed when SV localization was analyzed using GFP-RAB-3 ( Figure 1A–F ) . In sam-4 ( js415 ) , GFP-RAB-3 fluorescence was greatly reduced in PLM synaptic varicosities ( Figure 1B and D ) and increased both in the soma and the process proximal to the soma where the accumulations were largely punctate ( Figure 1B and F ) . We also found that the accumulation phenotype in PLM neurons is temperature sensitive: mutants raised at 25°C exhibit more severe defects than those raised at 15°C ( Figure S2 ) . In addition to the abnormal SV marker accumulations in PLM neurons , similar defects were also observed in other neurons including SAB neurons and ventral nerve cord neurons when using either SNB-1-GFP or GFP-RAB-3 SV markers ( Figure S3A–G ) [39] , [43] . Thus , SAM-4 appears to be essential for efficient transport of SVs in different types of neurons . The altered distribution of GFP-RAB3 that we observe in sam-4 mutants could be explained by the disruption of neuronal morphology and/or the microtubule cytoskeleton . To test if the reduced levels of SV markers in sam-4 PLM synaptic varicosities are caused by PLM anatomical defects , we examined PLM neurites using a cytosolic fluorescent marker mRFP ( jsIs973 ) and found no obvious morphological changes: PLM neurites extend normally , terminate properly in the mid-body , form the synaptic branches at the appropriate location and form synaptic varicosities in the ventral nerve cord ( Figure 1G and H ) . Since microtubules function as a common track for the anterograde transport of many synaptic components including SVs , mitochondria and active zone proteins [1] , we asked if sam-4 mutations cause microtubule cytoskeleton disruptions by examining the localization of synaptic components other than SVs . We found that the distribution of active zone proteins ( mec-7p::tagRFP-ELKS-1 , jsIs1075; Figure 1I–L ) and mitochondria ( mec-7p::tagRFP-mito , jsIs1073 ) ( Figure 1M–P ) is grossly normal in sam-4 mutants , indicating that transport of other synaptic components is largely intact . Thus , the microtubule cytoskeleton remains competent for axonal transport . To evaluate systemic effects of sam-4 mutations , we next examined locomotion behavior which has been associated with SV trafficking defects [44] . Surprisingly , sam-4 null ( see below ) mutants exhibit only mild defects in the velocity of stimulated locomotion and show little , if any , defects in posture or the trace of sinusoidal locomotion tracks ( Figure S4 ) . We also examined other behaviors of sam-4 mutants and detected no defects in mechanosensation , egg-laying , or growth rates . Furthermore , sam-4 males remain competent to mate . These observations suggest that sam-4 may encode a specialized neuronal component that promotes efficient SV transport , without being essential for the process . Positional cloning and transgenic rescue identified sam-4 as the C . elegans gene F59E12 . 11 ( Figure S5 and Materials and Methods for details ) , which encodes an evolutionarily conserved 240 amino acid protein ( Figure S5 ) with no identifiable domain structure . An additional open reading frame ( ORF ) was identified in the 5′ UTR of the sam-4 mRNA , but these sequences are not required for functions we describe for sam-4 herein ( see discussion for details ) . SAM-4 is the C . elegans ortholog of human LOH12CR1 that was identified as a candidate tumor suppressor based on frequent deletion of this region of human chromosome 12 in acute lymphoblastic leukemia [35] . To confirm the sam-4 gene identification , we expressed a 3X-FLAG-tagged derivative of the SAM-4 protein ( Figure S5C ) under its native promoter using a MosSCI strategy [45] , [46] . The single copy sam-4-3XFlag transgene completely rescued the Sam phenotypes of sam-4 mutants ( Figure 2E ) . Immunohistochemical analysis of the transgene revealed that SAM-4 is localized primarily to the nerve ring region of the head ( Figure S6A ) , indicating that sam-4 is broadly expressed in the nervous system . The sam-4 mutations we characterized are recessive and likely represent null alleles of sam-4 . The js415 allele isolated in our screen introduces a CAA>TAA nonsense lesion at Gln104 ( Figure S5A and S5B ) . tm3828 , another sam-4 allele isolated by the Japanese National Bioresource Project , deletes 149 bp of sam-4 . This deletion removes exon sequences coding for amino acids from Leu66 to Ala100 and results in a frame-shift ( Figure S5A and S5B ) . tm3828 and js415 exhibit indistinguishable GFP-RAB-3 mis-accumulation phenotypes ( Figure S2 ) . Since both mutations result in severe disruption of coding potential of sam-4 and have similar phenotypes , we conclude that both alleles represent null mutations . To characterize the role of SAM-4 protein in regulating SV transport , we assayed its function when expressed in different cell types ( Figure S5C ) . We found that sam-4 expression in PLM neurons driven by the mec-7 promoter rescued the SV accumulation defects in PLMs ( Figure S5D ) while its expression in PLM postsynaptic partners driven by the glr-1 promoter did not . These data suggest that SAM-4 functions cell-autonomously to regulate SV transport . We next used a functional sam-4-TagRFP transgene expressed in PLMs to further examine the sub-cellular localization of SAM-4 . We observed that SAM-4 preferentially accumulates in the synaptic varicosities of PLMs and small quantities of SAM-4 accumulate as puncta in the neurites ( Figure 2A and 2A′ ) , a pattern similar to the GFP-RAB-3 marker ( Figure 2B and 2B′ ) . Further examination demonstrated that these SAM-4 particles co-localize well with the RAB-3 labeled SV particles ( Figure 2C and 2C′ ) and furthermore the RAB-3 and SAM-4 particles move together ( Figure S6B , Movie 1–3 ) . In addition , SAM-4-TagRFP is retained in the cell body in unc-104 mutants as previously demonstrated for many other SV proteins including RAB-3 [39] , [47] . These observations suggest that SAM-4 may function as a component of the SV trafficking machinery . To determine if SAM-4 is a SV component , we examined SAM-4 subcellular localization using cell fractionation analysis . We lysed sam-4-3XFlag transgenic animals under detergent free conditions , cleared the lysate of large membrane organelles , cytoskeleton , and cell debris using a 15K g spin , then fractionated the extract into a membrane containing 150K g pellet and a cytosolic fraction . We observed that , like the SV protein synaptobrevin SNB-1 , SAM-4 was present in the SV membrane-containing pellet but was absent from the cytosolic fraction ( Figure 2D ) , indicating that SAM-4 is likely associated with SVs . Bioinformatic analysis predicts that SAM-4 contains a conserved myristoylation site at its amino terminus ( Figure S5B ) . We then tested if SAM-4 localizes to membranes through the myristoylation signal . Myristoylated proteins typically migrate faster than their non-myristoylated counterparts [48] . We observed a decrease in mobility of SAM-4 ( G2S ) -3XFLAG tagged protein compared to the SAM-4-3XFLAG control expressed at endogenous levels consistent with the hypothesis that this mutation disrupts SAM-4 myristoylation in vivo ( Figure 2E ) . However , fractionation of SAM-4 to the membrane compartment was not altered by the SAM-4 ( G2S ) lesion suggesting that SAM-4 associates with membranes independently of myristoylation ( Figure S6C ) . In fractionation experiments when EDTA and EGTA were omitted from the buffer , we also observed FLAG immunoreactive band 4 kD smaller than the full length SAM-4 which fractionated partially into the cytosol ( Figure S6D ) suggesting the SAM-4 N-terminus contains a site mediating interactions with an unidentified SV membrane component . We further examined functional activity of the sam-4 ( G2S ) myristoylation mutant and found that the endogenous expression level of sam-4 ( G2S ) ( jsIs1265 ) only partially rescue sam-4 ( null ) mutants ( Figure 2F ) . In addition , we observed that the SAM-4 ( G2S ) protein is not efficiently delivered to synapses and is largely retained in the soma ( Figure 2G–H′ ) . Taken together , these results argue that SAM-4 functions as a SV component to regulate SV trafficking . Axonal transport of SVs in synapses is mediated by anterograde transport ( largely the KIF1A motor system ) and retrograde transport ( the dynein motor system ) . To understand mechanisms by which SAM-4 regulates SV trafficking , we examined genetic interactions of sam-4 with mutations in both unc-104 and the dynein heavy chain gene dhc-1 . Hypomorphic mutations were used because null mutations in both genes are lethal and because point mutations in different domains of UNC-104 are available for analysis . We first tested if SAM-4 is involved in regulating the UNC-104 transport machinery by examining sam-4 unc-104 interactions . We previously isolated a hypomorphic unc-104 loss-of-function ( lf ) mutant , js901 , with a G1466V lesion in the cargo binding PH domain of UNC-104 that displays very similar phenotypes to sam-4 ( materials and methods for details ) . These mutants show decreased GFP-RAB-3 levels in PLM synaptic varicosities and increased accumulations in the proximal portion of PLM neurites ( Figure 3A–3C′ ) . Furthermore , they displayed mild locomotion defects ( Figure 4A , 4C and 4I ) while remaining grossly normal in PLM neurite morphology , growth rate and egg-laying behavior . js901 males remained competent to mate . Overall , the phenotypic defects of unc-104 ( js901 ) are mild compared to other unc-104 alleles such as the e1265 PH domain and the rh43 motor domain lesions which have severe locomotory defects and slow growth rates . If SAM-4 interacts with UNC-104 to regulate SV transport , we reasoned that sam-4 mutations would exaggerate the mild js901 defects . Indeed , we observed that SV soma accumulations are further increased in the sam-4 unc-104 ( js901 ) double mutant relative to either single mutant ( Figure 3A–3D′ ) . Additionally , we found that sam-4 unc-104 ( js901 ) double mutants exhibit very severe locomotion defects relative to either single mutant , exhibiting defects comparable to severe unc-104 mutants ( Figure 4A–4D , 4I ) . These results suggest that SAM-4 functions in concert with the UNC-104 protein to regulate the SV trafficking . It has been previously demonstrated that the UNC-104 PH domain functions independently from the motor domain [49] , [50] . The motor domain can walk on microtubules independently of the PH domain , and the PH domain can interact with vesicles independently of the motor domain . To assess the mechanistic implications of the genetic interactions between SAM-4 and UNC-104 , we examined the allele specificity of these interactions . Specifically , we first examined sam-4 interactions with a SV binding defective unc-104 allele , e1265 , which introduces a missense mutation ( D1498N ) in the PH domain and causes severe defects in SV binding [51] . Since e1265 mutants show virtually no detectable GFP-RAB-3 signal in neurites and severe locomotion defects with essentially no sinusoidal movements within the time-frame of our measurements ( Figure 4H ) , we analyzed the sam-4 and unc-104 ( e1265 ) interactions by scoring animals for pharyngeal pumping , a behavior which is also controlled by neuronal activity [52] . We found that pharyngeal pumping rates of the double mutants were significantly lower than those of e1265 animals ( Figure 4I ) . sam-4 unc-104 ( e1265 ) double mutants also had lower brood sizes and slow growth relative to either single mutants . These results suggest that SAM-4 acts synergistically with the PH domain to regulate SV trafficking . We then tested how sam-4 mutations interact with unc-104 ( lf ) mutations in the motor domain . unc-104 ( rh43 ) introduces two missense mutations in the motor domain and results in its motility defect [51] . These mutants exhibit severe locomotion defects again limiting our assay of animal movements . We applied pharyngeal pumping tests to evaluate their interaction . Surprisingly , we found that pharyngeal pumping defects introduced by the rh43 mutations are not exacerbated by the sam-4 mutation ( Figure 4J ) . Furthermore , we noticed that while the pumping defects of e1265 mutants are less severe than those of rh43 mutants , these defects of sam-4 unc-104 ( e1265 ) are more severe than those of sam-4 unc-104 ( rh43 ) ( Figure 4J ) . Thus , sam-4 exhibits allele specific synthetic interactions with PH domain lesions , but not motor domain lesions of unc-104 . Taken together , these results suggest that SAM-4 functions by improving the UNC-104 motility , and acts in parallel to the UNC-104 PH domain to regulate SV trafficking . To further explore the notion that SAM-4 enhances UNC-104 movement , we examined UNC-104 motor activity indirectly in sam-4 mutants by determining the localization of native protein . While UNC-104 protein is barely detectable in soma of wild type animals , we observed a dramatic increase of somatic UNC-104 accumulation in sam-4 mutants ( Figure 4K–4L′ ) . Since UNC-104 expression level is not affected by the sam-4 mutations ( Figure S9B ) , these data indicate that UNC-104 motility is disrupted . By contrast with unc-104 , dynein dhc-1 mutants exhibit accumulations of GFP-RAB-3 in the distal portion of the anterior PLM neurite presumably due to disruption of retrograde transport , but have largely wild type levels of GFP-RAB-3 in both the PLM soma and synaptic varicosities . We found that dhc-1 ( js319 ) ; sam-4 double mutants show vestiges of both mutant phenotypes: while GFP-RAB-3 levels are modestly increased in the distal portion of PLM neurites resembling dhc-1 phenotypes , GFP-RAB-3 levels are greatly reduced in the PLM synaptic varicosities and increased in the proximal portion and soma resembling sam-4 phenotypes ( Figure S7 ) . This combination of phenotypes is similar to that of dhc-1 ( js319 ) ; unc-104 ( js901 ) animals ( Figure S7 ) . We interpret these phenotypes of dhc-1; sam-4 double mutants as a combination of the sam-4 and dhc-1 induced defects in SV transport . Therefore , sam-4 shows no obvious genetic interactions with dhc-1 . Taken together , these genetic interactions support the model that SAM-4 regulates SV anterograde transport through UNC-104 motor domain . To directly address how SAM-4 regulates SV transport , we examined GFP-RAB-3 puncta dynamics in PLM neurites of sam-4 mutants using time-lapse imaging ( Figure 5 ) . We found that SV anterograde transport is significantly reduced in sam-4 mutants as revealed by a reduced number of moving particles , reduced run-length of particles and increased frequency of pauses ( Figure 5E–5H ) . However , the velocity of the GFP-RAB-3 transport was similar to wild type ( Figure 5F ) . Retrograde trafficking is similarly affected by sam-4 mutations ( Figure 5 ) , in agreement with previous observations that retrograde trafficking is linked to anterograde trafficking of SVs [51] , [53] . sam-4 defects were similar in severity to those of unc-104 ( js901 ) mutants . However , sam-4 unc-104 double mutants show more severe defects in GFP-RAB-3 trafficking ( Figure 5 ) , consistent with our behavioral and cell biological observations . These findings argue that SAM-4 regulates the anterograde trafficking of SVs by modulating the processivity of SV transport . In C . elegans , SYD-2 liprin-α has been shown to regulate SV transport by binding the FHA domain and stalk domain of UNC-104 [34] . With our observations on sam-4 unc-104 interactions in regulating SV transport , we next examined the relationship between syd-2 and sam-4 . We first confirmed that syd-2 ( ok217 ) null mutants show increased GFP-RAB-3 levels in the PLM soma and decreased levels in PLM synaptic varicosities ( Figure 3 ) , suggesting that anterograde SV trafficking is reduced . The GFP-RAB-3 accumulation defects in syd-2 mutants are less severe than that in sam-4 mutants ( Figure 3 ) . Nevertheless , similar to that observed in sam-4 unc-104 ( js901 ) mutants , abnormal soma and proximal neurite GFP-RAB-3 accumulations become much severe in unc-104 ( js901 ) ; syd-2 mutants relative to either single mutant ( Figure 3 ) . Furthermore , unc-104 ( js901 ) ; syd-2 mutants show more severe defects in locomotion than either single mutant ( Figure 4I ) . Similar genetic interactions to those observed in sam-4 unc-104 ( e1265 ) and sam-4 unc-104 ( rh43 ) animals were observed in unc-104 ( e1265 ) ; syd-2 and unc-104 ( rh43 ) ; syd-2 ( Figure 4J ) . Taken together , these data suggest that SYD-2 acts synergistically with UNC-104 PH domains to regulate SV trafficking in a similar manner as SAM-4 . We next examined sam-4 syd-2 interactions and observed no detectable genetic interactions between the two mutants . Double mutants display sam-4-like GFP-RAB-3 accumulation defects ( Figure 3 ) , and similar stimulated locomotion behaviors as either single mutants ( Figure 4I ) . Over-expression of sam-4 does not suppress syd-2 ( ok217 ) mutants , and syd-2 ( ju487 ) , a gain-of-function allele , has no effects on sam-4 defects ( Figure S8 ) . These results are consistent with the hypothesis that SYD-2 and SAM-4 function in the same pathway to regulate SV trafficking . To further understand how SAM-4 activity regulates SV trafficking , we conducted a genetic screen for sam-4 suppressors . Using ENU induced mutagenesis , we screened mutated progeny of sam-4 ( js415 ) ; jsIs821 for animals with increased GFP-RAB-3 signal in PLM synaptic varicosities ( Figure 6A–6D ) and isolated two suppressors from roughly 100 , 000 genomes screened . Combining traditional genetic mapping and whole genome sequencing strategies , we identified both mutations as novel unc-104 alleles ( see Materials and Methods for details ) . Interestingly , we found that the alleles introduce missense mutations in the UNC-104/KIF1A motor domain: S211A ( js1288 ) and D177A ( js1289 ) , both of which are conserved in mammalian molecular motor proteins ( Figure S9 ) . Further genetic tests showed that both alleles are semi-dominant in suppressing sam-4 defects . Additionally , we found that over-expression of wild type unc-104 ( unc-104 ( + ) ) in PLM neurons suppresses sam-4 defects ( Figure 7A–7D and 7K ) , but over-expression of sam-4 does not suppress unc-104 defects ( Figure S7 ) . Similar suppression analysis using syd-2 mutants by these unc-104 ( gf ) mutations also revealed suppression by unc-104 ( gf ) alleles ( Figure 6 ) . Taken together , these data argue that both js1288 and js1289 are gain-of-function alleles of unc-104 , and unc-104 is epistatic to sam-4 and syd-2 . To address how the unc-104 ( gf ) suppresses the SV trafficking defects of sam-4 , we characterized the two unc-104 alleles in the absence of sam-4 . In isolation , js1288 and js1289 show grossly normal mechanosensory neuron anatomy ( Figure 7F , 7G ) . We analyzed their effects on transport by examining GFP-RAB-3 distribution in vivo . We found that GFP-RAB-3 accumulations are significantly increased in the distal part of PLM neurites ( Figure 7E–7H ) in each of these unc-104 mutants but decreased in the soma ( Figure 7E″–7G″ , 7J ) , indicating that SV transport is enhanced by these two mutations . However , we did not observe GFP-RAB-3 increase in PLM synaptic varicosities ( Figure 7E′–7G′ , 7I ) . This is probably because either SV levels in PLM varicosities are already saturated in the wild type background or other mechanisms exist at pre-synapses to maintain SV homeostasis . To further understand how these mutations affect SV dynamics , we examined GFP-RAB-3 trafficking using live imaging ( Figure 8 ) . We found that both mutations result in increased run length of GFP-RAB-3 transport ( Figure 8E ) . We also noticed that js1289 results in greater flux of GFP-RAB-3 ( Figure 8F ) , while jsIs1288 reduces SV transport velocity ( Figure 8G ) . Thus , processivity of vesicle transport is increased in both gain-of-function mutants , though perhaps by distinct mechanisms . Western blot analysis of protein levels showed that neither of these two lesions alter UNC-104 protein levels in vivo ( Figure S9B ) . Hence , increasing processivity of the SV transport through the UNC-104 motor domain can partially bypass the need for SAM-4 . This is consistent with our hypothesis that SAM-4 functions through the UNC-104 motor domain to regulate SV transport .
In this study we have identified the conserved protein SAM-4 as a novel vesicular component regulating SV transport in C . elegans . SAM-4 behaves as a SV associated protein and modulates transport probably by regulating the motor domain activity of UNC-104 . This possibility is supported by our identification of two unc-104 ( gf ) motor domain mutations , which suppress sam-4 SV transport defects . Although our genetic evidence is consistent with a SAM-4 UNC-104 interaction , we have been unable to detect any evidence for physical interactions between SAM-4 and UNC-104 either in vitro by yeast two-hybrid analysis or in vivo by co-immunoprecipitation . Therefore , SAM-4 UNC-104 interactions may be mediated by other components . Our genetic data also indicate that SAM-4 acts in the same pathway as SYD-2 in regulating SV transport . We propose a model in which SV-bound SAM-4 regulates SV transport together with SYD-2 through UNC-104 , likely via its motor domain ( Figure S10 ) . It is known that SV transport is regulated , but little is known of the molecular mechanisms involved . The identification of SAM-4 as a SV-bound regulator of KIF1A/UNC-104-mediated transport defines a new pathway for modulation of axonal transport . Although SAM-4 is conserved , analysis of the protein sequence revealed only a N-terminal myristoylation motif , which appears to contribute to SAM-4 activity . The lack of identifiable protein domains in the protein make it difficult to speculate on a specific mechanism of action . We have proposed that SAM-4 modulates SV transport processivity by modulating UNC-104 motor activity because we observed strong genetic interaction between sam-4 and unc-104 cargo binding mutants but not with motor domain mutants . Furthermore , motor domain gf mutations suppress sam-4 defects arguing that increase of motor processivity can partially bypass SAM-4 activity . In addition , the genetic interactions between syd-2 and sam-4 support a processivity based mechanism of action for SAM-4 . Both worm and mammalian Liprin-α/SYD-2 interact with KIF1A/UNC-104 [32] , [34] and Liprin-α/SYD-2 is required for efficient SV trafficking in both C . elegans and Drosophila [33] . Our data argue syd-2 functions in the same pathway as sam-4 in regulating SV transport since each null mutant shows very similar interactions with both unc-104 ( lf ) and unc-104 ( gf ) lesions , but do not display obvious interactions with each other . However , SAM-4 may play a more central role in this process since the SV trafficking phenotypes in syd-2 ( null ) are less severe than those of sam-4 ( null ) . In this study , we recovered two gain-of-function mutations in the motor domain of unc-104 that increase the processivity of the motor in cargo movement assays . Kinesin mediated SV transport is an ATP driven process , which depends on motor-microtubule binding . The ATP hydrolysis catalytic core lays in the switch I region of the KIF1A/UNC-104 motor domain ( Figure S9 ) . Lesions ( for example H215Y in unc-104 ( y211 ) , see Figure S1 ) in this domain cause severe SV trafficking defects . The js1288 mutation occurs in S211A adjacent to S212 ( S215 in mammalian KIF1A ) which coordinates the gamma-phosphate of ATP in the ATP-bound crystal structure [54] . Consistent with the hypothesis that this lesion alters rates of ATP hydrolysis , we observed a lowered velocity of transport in js1288 mutants . However , the biochemical mechanism underlying the increase in processivity is unclear . The other mutation , js1289 , is a D177A substitution ( mammalian KIF1A D180 ) in loop 8 of the motor domain . Previous studies [54] showed that loop 8 is one of three microtubule binding regions in the motor domain and thus processivity in this mutant could be increased due to changes in the affinity for microtubules . In addition to suppressing SV trafficking defects in sam-4 ( null ) , both of these lesions result in increased accumulations of SVs in the distal portion of PLM neurites where no synapses have been seen at the ultrastructural level [40] . Therefore , these unc-104 ( gf ) mutations disturb the normal homeostasis of SV trafficking and thus may not necessarily represent beneficial biochemical modifications . However , the lesions argue strongly that processivity is not optimized in KIF1A and suggest the possibility that KIF1A activity could be modified , for example by pharmacological compounds , in diseases where axonal transport is compromised . It is worth noting that several lines of evidence imply sam-4 also regulates other processes in non neuronal cells . First , sam-4 neuronal phenotypes are partially maternally rescued . Some sam-4 animals segregating from the sam-4/+ mother even display wild type levels of GFP-RAB-3 at PLM synapses . Second , sam-4 is likely post-transcriptionally regulated . The sam-4 locus is highly unusual ( for nematodes ) in that it has two 5′ “non-coding” exons ( Figure S5 ) . These exons contain a small 79 amino acid ORF . A similar ORF is found in the 5′-end of sam-4 in highly divergent nematodes and the synonymous codon usage in these nematodes indicates it is being selected as coding sequence ( Figure S11 ) . The ORF is homologous to the APC13 , a small subunit of the Anaphase Promoting Complex ( APC ) which was previously described for plant parasitic nematodes [55] , but recognized in model system databases . We , and other investigators , observed lethality when performing RNAi against sam-4 even though both nonsense and deletion alleles of sam-4 are fully viable . These sam-4 RNAi lethal phenotypes are similar to those of other APC complex component genes . These include defects in meiosis in the early embryo [56] , oocyte deformation and sterility [57] and failure to segregate germline P-granules [58] . We posit the lethality phenotype associated with sam-4 RNAi is likely due to reduced expression of this upstream ORF encoding an APC13 homolog . The APC complex plays critical roles in regulating progression through the cell cycle . However , recent work has also highlighted several critical roles for APC complexes in neuronal development [59] . In particular , disruption of the APC complex alters axon growth , post-synaptic glutamate receptor levels [60] as well as the size and number of presynaptic boutons [61] . Interestingly , in regulating bouton number in Drosophila , the APC complex works in conjunction with liprin-α . Thus , the APC complex , SAM-4 and liprin-α appear linked at multiple different regulatory levels . Further investigations of the non-neuronal roles of SAM-4 , the role of the APC13 encoding upstream ORF in regulating SAM-4 expression , and the potential role of the APC complex in regulating axonal transport are clearly warranted . Although SAM-4 is evolutionarily conserved , no human disease conditions have been specifically associated with lesions in human sam4 ( LOH12CR1 ) , a gene within a region often deleted in acute lymphoblastic leukemia . Notably , worm sam-4 mutants display virtually indistinguishable phenotypes from mild unc-104/KIF1A mutants and human diseases are associated with KIF1A . Specifically , motor domain lesions ( A255V and R350G ) in human KIF1A underlie the molecular basis of the rare recessive late onset spastic paraplegia SPG30 [6] and a frameshift mutation in the PH domain underlies a form of hereditary sensory and autonomic neuropathy [5] . Further genetic and biochemical studies of SAM-4 in both invertebrates and vertebrates will be required to define the underlying biochemical mechanisms as well as physiological inputs that modulate SAM-4 action in regulating axonal transport .
C . elegans animals were maintained using standard methods [62] . All strains used except for those used for SNP mapping were derivatives of the Bristol N2 wild type background . Animals were grown at the room temperature ( 22 . 5°C ) , unless specified . Strains used are listed in Table S1 . The genotype of strains was confirmed by PCR using oligonucleotides listed in Table S2 . jsIs1238 II , jsIs1156 IV , jsIs1263 IV , jsIs1188 IV and jsIs1189 IV transgenes were integrated using MosSCI with EG4322 for integration on chromosome II and EG5003 for integration on chromosome IV [45] , [46] and confirmed to be single copy by long range PCR amplification . jsIs1073 and jsIs1075 were generated using a bombardment protocol with Cbunc-119 as the integration marker [63] . Genetic three-factor mapping narrowed the sam-4 mutation to an interval between dpy-25 and rol-6 on chromosome II . Single nucleotide polymorphism ( SNP ) mapping was used to position sam-4 with CB4856 as a reference strain and narrowed the mutation down to a 163 kb region on Chromosome II between the SNPs CE2-141 and pkP2147 . This region is covered by 5 fosmids . Using germline transformation rescue tests , we further mapped sam-4 down to the fosmids WRM0610dH02 and WRM0632aA08 . The sam-4 ( js415 ) lesion was identified by candidate gene sequencing in this region . A C>T nucleotide change was detected in the second exon of the predicted gene F59E12 . 11 . sam-4 defects are fully rescued by a transgene expressing the hypothetic F59E12 . 11 gene , which is predicted to encode a 240 amino acid protein . These data identify F59E12 . 11 as sam-4 . unc-104 ( js901 ) was isolated in a non-clonal forward screen for mutations that mislocalized RBF-1-GFP ( jsIs423 ) . L4 jsIs423 animals were mutagenized using 50 mM ethyl methanesulfonate ( EMS ) for 4 hrs and placed on E . coli seeded agar plates . F2 animals derived from these animals were screened for mislocalization of GFP from the nerve ring to cell bodies surrounding the nerve ring . js901 was mapped to chromosome II by classical genetic mapping strategy , and tested for non-complementation with unc-104 ( e1265 ) . The entire coding sequence of unc-104 was sequenced in js901 revealing a GGA to GTA that changes Gly1465 to Val . This lesion resides within the PH domain of UNC-104 . unc-104 ( js1288 ) and unc-104 ( js1289 ) were isolated in a screen for suppressors of the PLM synaptic varicosity phenotype of sam-4 . N-ethyl-N-nitrosourea ( ENU ) mutagenesis was performed using standard methodology [64] . Briefly , sam-4 ( js415 ) animals were treated with 0 . 6 mM ENU for 4 hours at the room temperature . Treated animals ( P0 ) were transferred to fresh food ( 10 L4s or young adults per 100 mm plate ) . P0 animals were removed from the plates 24–48 hours later . F1 animals were counted 2–3 days later to estimate number of mutagenized chromosomes screened . The F2 animals were screened for increased GFP-RAB-3 signal in PLM varicosities using a fluorescent dissecting microscope . The suppressors displayed tight linkage to sam-4 . Phenotypic analysis of sam-4 ( js415 ) js1288/sam-4 ( js415 ) and sam-4 ( js415 ) js1289/sam-4 ( js415 ) revealed both were semi-dominant suppressors of the Sam phenotype . Sequencing of the sam-4 coding region revealed no mutation in sam-4 gene of these isolates . js1289 was mapped to between sam-4 and rol-6 by three factor mapping , crossing lin-31 ( n301 ) sam-4 ( js415 ) js1289 rol-6 ( 187 ) ; jsIs821 to CB4856 and screening for Lin Sam non-Rol and Lin Sam Sup ( suppressor ) non-Rol recombinants 18 of 21 recombinants had recombination events between sam-4 and js1289 and 3 between js1289 and rol-6 . 100 bp paired-end whole genome sequencing of homozygous strains was conducted at Oklahoma Medical Research Foundation . The data were analyzed using Whole Genomes , a web-based alignment and analysis program , and revealed lesions in unc-104: An Asp177 to Ala ( GAC to GCC ) lesion in js1288 and a Ser211 to Ala ( TCA to GCA ) lesion in js1289 . Plasmid DNA clones were constructed using standard molecular biology techniques . Transgenic animals were imaged using epi-fluorescence on an Olympus BX60 equipped with an X-CITE120 mercury lamp ( EXFO ) using standard GFP and RFP filter sets . Images were taken with a Retiga EXi CCD camera using OpenLab software and processed using Adobe Photoshop . jsIs1238 II , jsIs1156 IV , jsIs1263 IV , jsIs1188 IV and jsIs1189 IV transgenes were integrated using MosSCI with EG4322 for integration on chromosome II and EG5003 for integration on chromosome IV [45] with modifications . These transgenic lines were confirmed to be single-copy integration events by long range PCR ( Details available online: http://thalamus . wustl . edu/nonetlab/ResourcesF/Resources . html ) . jsIs1073 and jsIs1075 lines were generated by integrating NM2057 and NM2066 using a bombardment protocol with Cbunc-119 as the integration marker [63] . Animals were assayed at the room temperature on NGM agar . L4 animals ( or as indicated ) were transferred to a bacteria-free plate to allow them clear off bacteria ( 2–3 min ) . Subsequently , these animals were transferred to another bacteria-free plate and imaged immediately for 10–20 sec . Animal movements were recorded using LG-3 frame grabber run by ScionImage software at 1 frame/sec for 40 images total . These recordings were then analyzed using wormtracker plus [66] . Only animals in the imaging field>14 consecutive frames recorded were used in the velocity analyses . L4 animals on bacterial lawns of OP50 on NGM agar were scored at the room temperature . Pharyngeal pumping rates was determined by counting contractions of the terminal bulb for 1 minute per animal . Immunohistochemistry and western blots were performed as previously described [67] , [68] . For FLAG immunohistochemistry staining , animals were grown at room temperature and fixed in methanol/acetone . For SAM-4-3XFlag fractionation , 0 . 5 mM EGTA and 0 . 5 mMEDTA were added in the fractionation buffer as indicated in Figure S6 . Mouse anti-Flag ( 1∶200 , Sigma , Cat . A8592 ) primary antibody incubations were performed overnight at 4°C . Alexa conjugated secondary antibodies ( Invitrogen ) were incubated for 2 hrs at room temperature at 1∶500 . Antibody used for western blots: anti-FLAG ( 1∶1000 ) ; anti-β-tubulin ( 1∶1000 , E7 , Developmental Studies Hybridoma Bank , Iowa city ) , anti-UNC-104 ( 1∶40 ) [51] . For young adult animals ( used in Figure 5 ) , hermaphrodites were immobilized with 3–5 mM levamisole ( Sigma-Aldrich ) in M9 buffer and mounted on a 2% agarose pad . Time-lapse images of GFP-RAB-3 were acquired and analyzed as described before [51] . The numbers of moving particles in a 15–20 µm region at a distance of 15–25 µm away from the PLM soma were used for flux calculations . For mid-L1 staged animals ( 20–24 hrs after hatch , used in Figure 8 ) , we used an anesthetic-free protocol to image GFP-RAB-3 [69] . Specifically , animals were immobilized in 0 . 5 µl of 0 . 10 microspheres ( Cat# 00876 , Polysciences , Inc . ) on 10% agarose pads . Time-lapse imaging was acquired using 100×/1 . 30 oil objective on a Axioskop ( Zeiss ) equipped with ASI piezo XYZ-motorized stage , Sutter instruments high speed electronic filter wheels and shutters , and a Hamamatsu Orca-R2 cooled CCD camera all controlled by Volocity software ( PerkinElmer Inc . ) . Time lapse images were acquired for 40 seconds at a speed of 5 frames per second with an exposure time of 200 ms . Particle dynamics were analyzed with Volocity software . Total moving particles were counted in the 35 µm region at a distance of 20 µm away from the PLM soma . To record GFP-RAB-3 co-movements with SAM-4-TagRFP , confocal images were collected with a Hamamatsu Flash 4 . 0 CMOS camera attached to a Yokogawa Spinning Disc Confocal apparatus on an Olympus IX73 inverted microscope . 0 . 33 sec Green and Red channels exposure were taken consecutively , and captured at 1 sec intervals , and image series were assembled into movies using Micro-Manager software ( available at micro-manager . org ) . P values were determined using GraphPad Prism . Multi-group data sets were analyzed by a one-way ANOVA with post-hoc Holm-Sidak's test for multiple comparisons . A t-test was used for paired data sets .
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Most cellular components of neurons are synthesized in the cell body and must be transported great distances to form synapses at the ends of axons and dendrites . Neurons use a specialized axonal transport system consisting of microtubule cytoskeletal tracks and numerous molecular motors to shuttle specific cargo to specific destinations in the cell . Disruption of this transport system has severe consequences to human health . Disruption of specific neuronal motors are linked to hereditary neurodegenerative conditions including forms of Charcot Marie Tooth disease , several types of hereditary spastic paraplegia , and certain forms of amyotrophic lateral sclerosis motor neuron disease . Despite recent progress in defining the cargo of many of kinesin family motors in neurons , little is known about how the activity of these transport systems is regulated . Here , using a simple invertebrate model we identify and characterize a novel protein that regulates the efficacy of the KIF1A motor that mediates transport of synaptic vesicles . These studies define a new pathway regulating SV transport with potential links to human neurological disease .
|
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"Abstract",
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"Discussion",
"Materials",
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"Methods"
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2014
|
The Vesicle Protein SAM-4 Regulates the Processivity of Synaptic Vesicle Transport
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The fight against cancer is hindered by its highly heterogeneous nature . Genome-wide sequencing studies have shown that individual malignancies contain many mutations that range from those commonly found in tumor genomes to rare somatic variants present only in a small fraction of lesions . Such rare somatic variants dominate the landscape of genomic mutations in cancer , yet efforts to correlate somatic mutations found in one or few individuals with functional roles have been largely unsuccessful . Traditional methods for identifying somatic variants that drive cancer are ‘gene-centric’ in that they consider only somatic variants within a particular gene and make no comparison to other similar genes in the same family that may play a similar role in cancer . In this work , we present oncodomain hotspots , a new ‘domain-centric’ method for identifying clusters of somatic mutations across entire gene families using protein domain models . Our analysis confirms that our approach creates a framework for leveraging structural and functional information encapsulated by protein domains into the analysis of somatic variants in cancer , enabling the assessment of even rare somatic variants by comparison to similar genes . Our results reveal a vast landscape of somatic variants that act at the level of domain families altering pathways known to be involved with cancer such as protein phosphorylation , signaling , gene regulation , and cell metabolism . Due to oncodomain hotspots’ unique ability to assess rare variants , we expect our method to become an important tool for the analysis of sequenced tumor genomes , complementing existing methods .
In recent years , studies analyzing sequenced tumor genomes have seen a drastic increase in their sample sizes , growing from only a handful samples to cohorts of several thousand patients . This rise in availability of sequenced tumor samples has enabled the comparative analysis of tumors originating from different tissues , revealing a diverse tissue-specific genomic landscape of mutational patterns [1–6] . Revelations of this complexity observed in sequenced tumor samples has led to new insights into cancer genomics . However , identifying which somatic variants are the “drivers” behind initiation or progression of cancer is confounded due to the high prevalence of “passenger” mutations that occur with low frequency but are thought to have no functional effect [7 , 8] . Thus , despite the increase in tumor-derived data , we are unable to understand whether the vast majority of somatic variants in tumor samples have any functional role . Towards understanding which somatic variants influence the initiation or progression of cancer , much work has been devoted to the cataloging of sequencing data in repositories like the Catalog of Somatic Variants in Cancer ( COSMIC ) [9] and to manually curated lists of genes with evidence of cancer involvement in GeneCards [10] , the Cancer Gene Census ( CGC ) [11] , the NCI Cancer Gene Index [12] , the “proto-oncogene” and “tumor suppressor” classifications in the UniProt [13] database , the Network of Cancer Genes [14] , and the TSGene database [15] . Massive ongoing sequencing projects like The Cancer Genome Atlas ( TCGA ) have discovered thousands of genes that are mutated in only a small fraction of tumors yet may still be important for cancer initiation or progression [7 , 16–18] . This has led to a rise in the availability of tools for analyzing and visualizing data [19–23] and also for identifying genes in tumor samples that are likely to harbor somatic variants that drive cancer initiation or progression [1 , 2 , 24 , 25] . Traditionally , methods for identifying important genes in tumor samples identify genes that are significantly enriched with somatic variants by clustering somatic variants by genes for statistical analysis . Clustering variants by gene regions is the natural choice since genes are units of inheritance and much is known about the function of particular genes . Not surprisingly , gene-centric studies of TCGA data have been able to recapitulate much of the knowledge about cancer genetics derived from decades of studies [1 , 2 , 6 , 24 , 25] . For instance , methods like the Cancer Mutation Prevalence Score ( CaMP Score ) in Sjöblom et al . [1] , Wood et al . [2] , and MutSigCV in Lawrence et al . [24] employ frequency-based analyses to identify regions of the genome ( i . e . , genes ) that contain more mutations than expected by chance given a background of randomly occurring passenger mutations . However , the gene-centric analysis of individual cancer data relies on the relative frequency of all variants in a gene in sequenced tumor samples and is likely to miss variants that influence cancer progression that occur with relatively low frequency in the population . Even in the early years of such gene-centric data-driven analyses of sequenced tumor genomes like the CaMP Score , it was discovered that the genomic landscapes of somatic mutations in cancer were dominated by ‘gene hills’ , or gene regions that are mutated at a low frequency . Indeed , it has been shown that even well-studied genes in cancer are mutated in only a small portion of tumor samples [18 , 26] . Thus , to identify infrequently mutated genes that play a role in cancer progression , other methods have been developed for clustering low frequency gene-mutations together with other genes with a common functional role . For example , clustering variants from genes on the same pathway [24 , 27–30] , ontological term [28 , 31] , or protein interacting partners [32 , 33] . Additionally , akin to tools for predicting deleterious variants in other diseases , machine learning methods [34–36] have been developed to determine which variants are likely to influence cancer progression . For instance , the Cancer-specific High-throughput Annotation of Somatic Mutations ( CHASM ) [34] , is a machine learning predictor trained to classify between variants known to drive cancer progression and putatively neutral variants using properties of genomic and protein sequence , predicted protein structure , and multiple sequence alignments . In recent work , Nehrt et al . [37] and Yang et al . [38] have shown the value of analyzing cancer somatic variants by clustering variants within a gene sub-region , i . e . , the protein domain . Protein domains are the functional , structural , and evolutionary units of proteins [39 , 40] , mediate approximately 75% of protein-protein interactions [41] , and mutations in different domain regions of the same gene can have functionally and phenotypically distinct effects [42] . So , protein domain level studies have shown great potential to analyze tumor variants , in particular because they overcome the inability to distinguish functionally relevant variants due to the modularity and polyfunctionality of genes . In their domain-centric studies , somatic variants from TCGA of two [37] and later twenty [38] tumor types were analyzed to identify specific domain regions within genes that are significantly mutated in somatic tumor samples . In Nehrt et al . , it was discovered that domain regions within a single gene can display heterogeneous mutation patterns that are unique between Breast Invasive Carcinoma and Colorectal Adenocarcinoma . Extrapolated to the plethora of cancer types available in the TCGA project , Yang et al . further defined these unique domain mutational patterns , highlighting patterns specific to any of these cancer types . In these previous domain-centric analyses , statistical measures were performed to identify domain families that are frequently mutated often with mutations from multiple genes with a common protein domain . In this work , we develop a novel method to identify “oncodomains” , or protein domains in which somatic variants from one or more genes encoding the domain occur more frequently at specific sites ( i . e . , oncodomain hotspots ) than expected by chance . These oncodomain hotspots correspond to specific positions within an entire family of genes , which enables our method to study even extremely rare somatic variants via inference to other genes with similar somatic variant patterns . We argue that since protein domains are the structural and functional units of proteins , protein domains are the ideal framework for comparison to other genes since they are manually curated to match the structure and known functional features of domain family members , providing an inherent functional explanation of how somatic variants can contribute to cancer . To clarify , the approaches by Nehrt et al . and Yang et al . identified domain families that were enriched with somatic mutations but they did not , however , analyze the position-specific mutational patterns between different genes that share a common protein domain as in this work . The oncodomain concept introduced here is motivated by results from our earlier studies on known disease mutations . In Peterson et al . [43–45] , we performed a domain-centric study to cluster all known disease variants into common domain regions from all human proteins . Results from these studies hinted at protein domain positions of functional relevance for the analysis of variants from the OMIM [46] and Swiss-Prot [47] databases . Specifically , known disease variants tend to cluster at specific domain sites more than expected by chance and these ‘position-based domain hotspots’ tended to be located on functional features and conserved residues , properties that were also found for variants that have been experimentally determined to be phenotypically altering in yeast [45] . Here we tested the hypothesis of whether cancer somatic variants also present similar patterns of aggregation as known disease variants . To address this question , we developed a new statistical framework in which we control for population-level frequency information and the large proportion of cancer passenger mutations . Oncodomain hotspots are derived exclusively from somatic mutations from sequenced tumor samples and represent a novel approach for assessing which somatic mutations are likely to influence the initiation or progression of cancer . Although domain-centric models have been previously developed in Nehrt et al . , Yang et al . , the oncodomain method differs in substantial ways . Firstly , these studies were region-based in that entire domain regions were assessed for cancer significance , not specific positions within the domain family . Although Yang et al . identifies mutational hotspots , these hotspots are specific to a particular gene and contain no information from other genes sharing a common protein domain . Furthermore , the hotspots in Yang et al . do not consider variants from all domain regions as they restrict their analysis to domains that are significant in their region-based model . Secondly , oncodomains are inherently family-based in that somatic variants are aggregated to the domain-level and significance of a specific family member is ascertained by referencing all members of the family . Although Nehrt et al . analyzed domain regions from all genes sharing a common domain , the regions were concatenated and treated as a single , large gene and thus no positional information was used . Thirdly , the study conducted by Yang et al . only considers somatic variants that are predicted to be “potentially damaging” via the IntOGen-mutation platform [48] and removes all other somatic variants from the analysis . The IntOGen-mutation platform is a meta-predictor that classifies variants as “potentially damaging” primarily on the observed frequency in tumor samples and the results of several variant predictors , SIFT [49] , PolyPhen-2 [50] , VEP [51] , and MutationAssessor [52] . This contrasts with oncodomain hotspots , which consider all somatic variants no matter the observed frequency and does not utilize machine learning methods to remove variants predicted to have no functional impact . Notably , filtering the data using variant predictors is problematic since it will bias the remaining variants towards conserved sites , functional features , structurally important residues , and even domain regions since this information is used in the variant predictors to assess deleteriousness . In this work , we compare the results of oncodomain hotspots to genes with evidence of cancer involvement from the Cancer Gene Census , the NCI Cancer Gene Index , the Network of Cancer Genes , TSGene , and UniProt and to mainstream methods for the classification of cancer variants from tumors . Specifically , we compared to a gene-centric method , MutSigCV , two domain-centric approaches developed by Nehrt et al . and Yang et al . , and a multi-feature machine learning predictor trained to distinguish drivers from passengers , CHASM . We demonstrate that oncodomain hotspots not only overlap well with the cancer genomics literature and the results of both gene- and domain-centric methods , but also that our method is unique in the ability to detect variants that occur with low frequency in tumor samples but have evidence of cancer involvement or are predicted to be driver mutations by CHASM . Due to the ability of oncodomain hotspots to leverage relevant structural and functional context to identify even rare somatic variants with high potential to drive cancer development , we hope for oncodomain hotspots to become an important tool for large-scale analysis of sequenced somatic tumor samples , complementing existing tools .
Somatic Variants from 5 , 848 patients from The Cancer Genome Atlas ( TCGA ) [53] were mapped to specific positions within protein domain models to identify clusters . TCGA MAF files were obtained on July 7th , 2014 for 20 cancer types: Adrenocortical Carcinoma ( ACC ) , Bladder Urothelial Carcinoma ( BLCA ) , Brain Lower Grade Glioma ( LGG ) , Breast Invasive Carcinoma ( BRCA ) , Colon Adenocarcinoma ( COAD ) , Glioblastoma Multiforme ( GBM ) , Head and Neck Squamous Cell Carcinoma ( HNSC ) , Kidney Chromophobe ( KICH ) , Kidney Renal Clear Cell Carcinoma ( KIRC ) , Liver Hepatocellular Carcinoma ( LHIC ) , Lung Adenocarcinoma ( LUAD ) , Lung Squamous Cell Carcinoma ( LUSC ) , Ovarian Serous Cystadenocarcinoma ( OV ) , Pancreatic Adenocarcinoma ( PAAD ) , Prostate Adenocarcinoma ( PRAD ) , Rectum Adenocarcinoma ( READ ) , Skin Cutaneous Melanoma ( SKCM ) , Stomach Adenocarcinoma ( STAD ) , Thyroid Carcinoma ( THCA ) , and Uterine Corpus Endometrial Carcinoma ( UCEC ) . Only validated exonic variants were used , resulting in 1 , 326 , 954 unique exonic variants across 20 cancer types . The number of patients and variants for each of the 20 cancer types studied is enumerated in Table 1 . To map protein domain models to specific positions within human proteins , a human protein database containing 54 , 372 proteins was created with 33 , 963 proteins from RefSeq [54] and 20 , 409 proteins from Swiss-Prot [55] downloaded via NCBI’s E-utilities [56] . Since redundant protein entries exist between the RefSeq and Swiss-Prot databases , we selected only one representative protein for each unique Entrez gene ID , either the longest Swiss-Prot protein , or the longest RefSeq protein if no Swiss-Prot protein was listed for the gene ID . In addition , to avoid redundancy between isoforms produced by a single gene , we used only the longest protein product for analysis . Protein domain models from CDD [57] and Pfam [58] were obtained from the Conserved Domain Database ( CDD version 2 . 25 ) . HMMer’s semi global implementation [59] was used to map these domain models from human proteins . Finally , illustrated in Fig 1 , proteins with somatic variants were aligned to specific positions within each domain model by using HMMer’s alignment with an E-Value threshold ≤ 0 . 001 where variants on gap regions of the domain model were assigned to the last position before the gap . To build the CDD protein domain set with minimal redundancy on the models , we selected only root domains ( obtained from ftp://ftp . ncbi . nlm . nih . gov/pub/mmdb/cdd/cdtrack . txt ) . The final domain sets that map to human proteins contain 4 , 377 and 4 , 118 protein families from CDD and Pfam respectively . In previous work by Peterson et al . [43 , 44] and Yue et al . [60] it was shown that variants with known cancer relevance from the OMIM and UniProt databases tend to cluster at positions within protein domains . However , the inclusion of patient frequency information is critical for the analysis of TCGA somatic variants from sequenced tumor samples and for the identification of driver mutations , but requires a new statistical framework that includes patient frequency into the analysis . Thus , in this work , we developed a mutational score to classify protein domain positions derived from individual patient data using a local false discovery rate ( FDR ) with a Zero-Inflated Poisson ( ZIP ) null distribution . We applied this methodology separately for each cancer type and for each protein domain model and defined high scoring protein domain positions as those with a q-value < 0 . 05 . The details and derivation of this statistical approach can be found in a separate work by Gauran et al . [61] but briefly , the formulation used is as follows . At the protein domain-level which often encompasses several genes , each position within the domain contains j = 0 , 1 , …jmax somatic mutations from patients with the same cancer type and we define nj as the number of domain positions with j somatic variants . We developed a local false discovery rate method using a zero-inflated Poisson distribution as the null distribution for non-significantly mutated positions . Each protein domain was considered separately to remove the influence of region-based cofactors ( replication timing , expression , etc . ) since each domain position is aligned to the same set of proteins . Our goal is to find the cutoff of j which separates non-significantly ( f0 ( j ) ) and significantly ( f1 ( j ) ) mutated positions . The observed count of mutations are from a mixture distribution , where p0h=Pr ( non-significant ) p1h=Pr ( significant ) f0 ( j ) =densityifnon-significant f1 ( j ) =densityifsignificant Where f0 is assumed to follow a Zero Inflated Poisson ( ZIP ) distribution while f1 could be any other ( discrete ) distribution . ZIP models are considered useful for the analysis of count data with a large amount of zeros because it allows for two sources of overdispersion by mixing a Poisson distribution with zero-inflation . For a given position , we assume that the number of mutations j is generated by one of the two distributions f0 ( j ) or f1 ( j ) so the probability density function of the mixture distribution is f ( j ) =p0f0 ( j ) +p1f1 ( j ) Then , we define the local FDR at t as fdr ( t ) =p0f0 ( t ) f ( t ) Which indicates that f dr ( t ) is the posterior probability that a position with j = t is non-significant . The interpretation of the local FDR value is analogous to the frequentist’s p-value wherein local FDR values less than a specified level of significance provide stronger evidence against the null hypothesis . In this work , unless noted otherwise , we use a cutoff of f dr ( t ) = 0 . 05 , which would indicate that only 5% our oncodomain hotspots are false discoveries . When comparing regions of the genome ( i . e . , genes in the CaMP score and MutSigCV ) , methods must account for “covariates” that are thought to influence the background rate of passenger mutations for that particular genomic region , such as replication timing , gene expression , chromatin state ( open/closed ) , and mutation context ( e . g . , C to G in CpG sites , G to C in GpA sites , etc . ) . When analyzing an aligned position within the same family of genes , the altered mutation rate of the aligned gene regions does not differ between aligned positions and thus does not need to be modeled . This is correct for all covariates with the exception of mutational context , which may differ between aligned positions . However , we determined that using synonymous variants to estimate the background probability of passenger mutations was inappropriate . Firstly , it is well known that many synonymous variants are drivers that re-occur in cancer and are not distributed randomly [62–64] . Secondly , the frequency of occurrence of synonymous variants is often different than that of the nonsynonymous variants , making them inappropriate to use to estimate the null model . Thus , using a randomly distributed background of equal size to the observed nonsynonymous variants was chosen . To assess the significance of overlap between oncodomain hotspot positions and positions that have known function , functional feature annotations for each protein position were obtained from UniProt on July 18th 2015 . To determine the conservation of each domain position j , we employed the AL2CO [65] algorithm for assessing entropy via the following formula: Hjh=−∑i=1 , 20p ( ai , j ) ln ( p ( ai , j ) ) Here , p ( ai , j ) is the amino acid frequency for amino acid ai at position j and Hj is the AL2CO score at position j . Positions were considered to be conserved if they were greater than or equal to the average AL2CO score plus one standard deviation . Pearson’s correlation coefficient and Fisher’s exact test with Bonferonni correction were used to assess significance of hotspot position overlap with functional features or conserved residues . To compare to other methods , significantly mutated genes were obtained using MutSigCV v1 . 4 , significantly mutated domains were obtained from the results of Nehrt et al . and Yang et al . , and the results of CHASM were obtained from the Firehose project [19] . To compare to cancer-related databases , the Gene Ontology database [66] along with the pfam2go annotations were obtained on August 21st 2015 , the NCI Cancer Gene Index was obtained on March 7th , 2016 , the Network of Cancer Genes was obtained on March 4th , 2016 , and the TSGene database was obtained on March 4th , 2016 , the Cancer Gene Census [11] on November 6th , 2015 , and the UniProt [13] “proto-oncogene” and “tumor suppressor gene” classifications were obtained on November 7th , 2015 . Gene Ontology category enrichment was performed using Fisher’s exact test with Bonferroni correction .
In this work , we define oncodomains as families of protein domains in which somatic variants from one or more genes containing the same domain form a hotspot . Oncodomain hotspots are defined as protein domain positions where somatic variants for a specific cancer type occur more frequently than expected by chance ( see Materials & Methods ) . A comparison of the number of oncodomains and oncodomain hotspots identified for different fdr ( t ) cutoffs along with the number of patients and exonic somatic variants for each cancer type is shown in Table 1 . For simplicity , we will refer to the results obtained using the fdr ( t ) cutoff of 0 . 05 for the remainder of this analysis . In this study , we identify 185 protein domain families from CDD and 673 from Pfam across 20 cancer types as oncodomains . Within these families , 2 , 126 oncodomain hotspots were identified on CDD domains and 3 , 563 hotspots were identified on Pfam domains . Overall , the quantity and location of the hotspots were found to be highly heterogeneous between cancer types . We find the number of oncodomains and oncodomain hotspots to be highly variable between cancer types ranging from only 1 or 7 hotspots in KICH and HNSC respectively , to a maximum of 1 , 742 hotspots identified in SKCM . In our dataset , TCGA cancer types had an average of 74 ( standard deviation of 89 . 8 ) oncodomains and an average of 309 ( standard deviation of 571 ) oncodomain hotspots . The frequency of hotspots across the 20 cancer types was highly heterogeneous with nearly 400 domain models being signatures for only one cancer type while 21 were common to ten or more cancer types ( S1 Fig & S1 File ) . A full list of all oncodomains and the cancer-specific oncodomain hotspots for each cancer type can be found in S2 File . We find a strong correlation between the total number of exonic somatic variants and the number of oncodomains / oncodomain hotspots ( Pearson’s Correlation 0 . 92 and 0 . 98 respectively ) . Compared to the number of exonic variants , the number of patients in each cancer type was not as strongly correlated to the number of oncodomains ( Pearson’s Correlation: 0 . 14 ) or oncodomain hotspots ( Pearson’s Correlation: 0 . 21 ) , which is to be expected since the number of somatic variants per tumor is known to be highly variable between cancer types [25] . However , the importance of including more sequenced patients for research is highlighted in S1 Table . To address this , a bootstrapping analysis was performed 100 times for the three largest TCGA sets ( LUAD , SKCM , and UCEC ) to calculate oncodomains and oncodomain hotspots using only 75% and 50% of the available patients and , separately , the available exonic somatic variants . Results for bootstrapping patients or variants both suggest that more oncodomains and oncodomain hotspots will be identified when more data become available , as expected . We also tested the effect of combining patients from all cancer types to observe whether oncodomains and oncodomain hotspots differ from the cancer-specific hotspots analysis . In this separate analysis , we observe an increase of 82 oncodomains and 1 , 469 oncodomain hotspots ( Pfam only ) when combining all data types together that were not identified when analyzing the sets individually ( S3 File ) . Results from the combined dataset also show that 247 oncodomains and 1 , 251 oncodomain hotspots that were previously identified when analyzing individual datasets are no longer significant in the combined dataset . This , however , is to be expected due to the disproportionate number of patients in each cancer type , removing much of the cancer-specific signals . Like genes , protein domains have been shown by Nehrt et al . and Yang et al . to display heterogeneity in the prevalence of somatic variants from patients with different cancer types . However , no study yet has explored the mutation patterns of domain families that appear several times throughout the human genome . In our analysis , we observed this heterogeneity in the prevalence of somatic variants between different cancer types and also between the frequencies in which members of a particular domain family are involved . For example , in S2 File , the hotspots formed on a particular oncodomain are found to be highly heterogeneous in the quantity and location for a given cancer type . Depicted in Fig 2 for the Ras-like GTPase family ( Fig 2A ) and the calcium binding domain of the Epidermal Growth Factor ( Fig 2B ) , the intensity of color at each residue represents the number of cancer types in which that residue was found to be an oncodomain hotspot across the 20 cancer types . In these structural representations , the frequency or specific location in which somatic variants occur is highly heterogeneous between cancer types , a property that would normally be ignored by traditional region-based analyses that group all positions within a gene or domain region into a single bin when testing for significance . The overlap between oncodomain hotspots and functional features for each protein residue in the UniProt database were ranked by their Fisher’s exact test p-value with Bonferroni correction and are listed in Table 2 . Overall , we found that oncodomain hotspots significantly occur on functional feature sites ( p-value: 3 . 63E-87 ) , a finding that is not true for somatic variants overall , which do not occur significantly at functional feature sites ( p-value > 0 . 05 ) . Interestingly , the specific residue of the functional feature that is mutated is heterogeneous between cancer types , as seen in the comparison between the frequency of mutated sites in Fig 3A and the residues involved with the active site in Fig 3B . Additionally , we found a significant overlap between oncodomain hotspots and conserved residues ( p-value: 1 . 45E-09 ) . However , conservation and functional feature annotation do not correlate with oncodomain hotspots ( Pearson’s correlation coefficients of 0 . 009 and 0 . 048 respectively ) , indicating that this information alone is insufficient for determining which functional or conserved residues will be important for cancer initiation or progression . For genes with a somatic variant in an oncodomain hotspot , enrichment was performed for categories of genes in the Molecular Function and Biological Process divisions of the Gene Ontology database ( S2 Table ) . For Pfam oncodomains , Gene Ontology term enrichment was performed using the pfam2go annotations ( S3 Table ) . Overall , we found that oncodomain hotspots identify more protein domains , genes , and somatic variants than other methods , many of which are rare variants . Due to the lack of a good benchmarking set , we compared the results of our method to the results of other methods for analyzing somatic tumor genomes and to databases of genes with evidence of cancer involvement . In comparison to other domain-centric methods ( Nehrt et al . and Yang et al . , Fig 4A ) , oncodomain hotspots recapitulate 80 / 157 ( 51% ) of Pfam domain models while identifying 593 novel Pfam models . At the gene-level in Fig 4B , genes with variants in an oncodomain hotspot identify 440 / 779 ( 56% ) of genes with variants significant in CHASM , 469 / 1 , 373 ( 34% ) of genes identified by region-based methods ( MutSigCV , Nehrt et al . , and Yang et al . ) , and 4 , 587 genes were unique to oncodomain hotspots . Of these 4 , 587 genes unique to oncodomain hotspots , we found 1 , 546 / 4 , 587 ( 34% ) genes to have evidence of cancer involvement from the Cancer Gene Census , the NCI Cancer Gene Index , the Network of Cancer Genes , the Uniprot “proto-oncogene” and “tumor suppressor gene” classifications , and the TSGene databases ( Fig 4C ) which were not detected by MutSigCV or CHASM . As depicted in Fig 5 , the majority of the remaining genes detected only by oncodomain hotspots ( 2 , 738 / 3 , 041; 90% ) are either members of domain families for which cancer relevance is known ( e . g . , kinases , growth factors , and immunoglobins ) or are annotated with GO terms that have known cancer relevance ( e . g . , signal transduction , metabolic process , and cell adhesion ) . Rare variants are thought to play an important role in cancer and , thus , frequency-based methods are inherently ill-suited to assess their relevance in cancer due to their low prevalence in tumor samples . However , by comparing to other genes within the same domain family , oncodomain hotspots have the ability to infer functional relevance of variants that occur infrequently in tumor samples . Indeed , variants implicated only by oncodomain hotspots occurred in an average of 1 . 1 ( variance of 0 . 34 ) tumor samples compared to variants implicated by MutSigCV that occurred in an average of 2 . 1 ( variance of 64 . 4 ) tumor samples ( t-test p-value: 3 . 5E-259 ) . On the other hand , as expected , oncodomain hotspots implicate many of the frequently occurring variants that would be identified by other methods since the variants in oncodomain hotspots that were also identified by MutSigCV occur in an average of 2 . 2 ( variance of 59 . 3 ) tumor samples .
Distinguishing between drivers and passengers in sequenced tumor samples is a challenging task in cancer biology . However , traditional methods that rely solely on frequency of somatic variants for identifying driver variants are limited due to the lack of sequenced patients , even with the thousands of patients that have been sequenced in TCGA . As noted in Sjöblom et al . and Wood et al . , the genomic landscapes of somatic mutations are dominated by “gene hills” , or infrequently mutated genes that do not reach statistical significance but may still be relevant in cancer . Thus , new methods are needed in order to functionally characterize these rare variants and their importance in cancer . As shown in previous studies , Nehrt et al . and Yang et al . , domain-centric analyses have the potential to identify somatic mutational patterns unique to specific cancer types that would normally be overlooked by gene-centric analyses that consider only whole proteins and not the modular regions within . Such approaches can help improve our understanding of the molecular perturbations leading to cancer initiation and progression and enable the identification of new targets for cancer-specific drug research . However , these approaches consider only variation between domain regions within a single gene and , as such , ignore similar , often rare variants in other members of the same protein family that may play a similar role in cancer or may also affect drug treatments . In this study , by leveraging the knowledge of conserved regions of proteins that can occur several times throughout the genome ( i . e . , protein domains ) , we are able to infer functional and structural relevance of rare somatic variants by comparing them to similar variants in other genes sharing a common protein domain . This novel concept also allows us to observe heterogeneity in mutation prevalence between members of a protein family—patterns which can be unique for particular cancer types . In this work , we identify “oncodomain hotspots” , or positions within protein domain regions that harbor more somatic variants than expected by chance by aligning similar domain regions from multiple genes across all patients for a given cancer type ( Fig 1 ) . Overall , we found the location and intensity of oncodomain hotspots to be highly heterogeneous between cancer types . For example , as enumerated in S2 File , we found that position five on the Ras-like GTPase ( Fig 2A ) was the most frequently occurring hotspot on cd00882 , appearing in 10 cancer types ( BLCA , BRCA , COAD , LUAD , OV , PAAD , READ , SKCM , STAD , and UCEC ) and represents a portion of the GTP/M2+ binding site . However , this hotspot was not found in THCA , where oncodomains identified , instead , a hotspot on position 307 . Similarly , in LIHC , oncodomain did not identify position five or 307 as hotspots but we reported a hotspot at seven other positions , two of which can only be found in LIHC . Thus , some hotspot patterns are common in several cancers while others are unique to a specific cancer type . In the Ras-like GTPase alone , we find one hotspot unique to COAD , two hotspots unique to LIHC , five hotspots unique to LUAD , six hotspots unique to SKCM , three hotspots unique to STAD , and 20 hotspots unique to UCEC . Interestingly , while we observe a stark heterogeneity between the location and intensity of oncodomain hotspots between cancer types , our results show a significant overlap for oncodomain hotspot location with conserved residues and functional feature sites . Thus , although oncodomain hotspots are heterogeneous , they tend to occur at different positions that are highly conserved residues or at different positions that perform similar functions as seen in Fig 3 where hotspots tend to occur spatially around the active site of the catalytic domain of protein kinases . Overall , oncodomain hotspots identify many more domains ( Fig 4A ) than other domain-centric methods like Nehrt et al . and Yang et al . and more genes ( Fig 4B ) than gene-centric methods like MutSigCV or CHASM . Although not identified by other methods , 1 , 546 / 4 , 629 ( 34% ) of genes identified only by oncodomain hotspots have evidence of cancer involvement from the Cancer Gene Census , the NCI Cancer Gene Index , the Network of Cancer Genes , the Uniprot “proto-oncogene” and “tumor suppressor gene” classifications , and the TSGene manually curated databases ( Fig 4C ) . Interestingly , we find variants in oncodomain hotspots on 392 genes from either the TSGene database or UniProt’s tumor suppressor gene annotations , indicating that both oncogenes and tumor suppressors form hotspots at the domain-level , a phenomenon previously discovered for tumor suppressor genes at the gene-level [67–69] . Moreover , as illustrated in Fig 5 , the majority ( 90% ) of the remaining 3 , 041 genes in Fig 4C identified only by oncodomain hotspots are either members of domain families for which cancer relevance is known or are annotated with GO terms that are known to be important for cancer . Overall , oncodomain hotspots find many new genes that display similar somatic variant patterns to other genes within the same domain family that are well-studied in cancer genomics including 83 novel kinases ( cd00180 ) , 52 novel growth factors ( cd00054 & cd00053 ) , 33 novel Ras family members ( cd00882 ) , 26 novel cadherins ( cd00031 ) , 88 novel immunoglobins ( pfam00047 ) , and 43 novel Kelch-like ( KLHL ) genes . Additionally , oncodomain hotspots identify significant somatic variant clusters in the Melanoma Antigen ( MAGE ) family of genes which were never significant in other methods as well as the Rho-like GTPase family , which has known cancer involvement but is notorious for being somatically mutated only rarely [70 , 71] . Oncodomain hotspots also identify many genes involved with cell adhesion and cell junction organization , which are known to be important in cancer progression [72–74] and metastasis [75 , 76] , and genes involved with metabolism , which are also important in cancer progression [77–79] . Furthermore , many genes involved with the extracellular matrix or extracellular vesicles formed oncodomain hotpots , which are thought to be important in the regulation of cancer progression and metastasis [80–85] . Oncodomain hotspots are also formed on other gene families involved with processes thought to influence cancer initiation or progression such as ubiquitination [86–88] , proteolysis [89–91] , metabolic proteins [92 , 93] , and genes involved with actin binding and the cytoskeleton [94–96] . Interestingly , oncodomain hotspots also identify many membrane proteins , which are involved with signal transduction , which is known to be relevant in cancer [97 , 98] and experimental evidence confirms the important regulatory role played by membrane proteins in cancer [99–105] . Our results also indicate a strong pattern of variants occurring at specific domain family sites for genes involved with signal transduction , regulation of transcription , and nucleotide binding GO terms . Likewise , we find oncodomain hotspots in domain families that serve as the molecular machinery of transcription factors ( zinc fingers , KRAB domains , and WD40 beta propellers ) as well as ANK domains , which mediate protein-protein interactions [106] . Thus , oncodomain hotspots reveal a vast landscape of somatic variants that act at the level of domain families altering signaling pathways and gene regulation to influence cancer . Identifying the role in cancer , if any , of so-called “gene-hills” in Sjöblom et al . and Wood et al . has been an important challenge since rare variants are thought to play an important role in cancer [6 , 107 , 108] , which has led to an increase in network-based analyses for functional characterization [24 , 27–30] . A domain family-based analysis like oncodomain hotspots enables the identification of many novel , often rare variants that occur more frequently in specific positions within domain families than expected by chance . Indeed , when analyzing entire families of proteins and not specific members therein , mutational patterns emerge which suggest that rare variants play an important role since they often occur on genes with known cancer relevance . For example , protein kinases harbor somatic variants in 3 , 634/5 , 848 ( 62 . 1% ) of the tumors analyzed in this study yet only 27 / 465 human genes mapping to the PKc ( cd00180 ) domain model were considered significant by MutSigCV , 16 of which were significant in only the PAAD cancer type . In Fig 6 and S2 Fig , we summarize the results of comparing MutSigCV and CHASM respectively against oncodomain hotspots to evaluate the ability of these methods to identify rare and common variants relevant to cancer . The genes selected are members of the PKc ( cd00180 ) oncodomain family , the catalytic domain of protein kinases that are the most frequently mentioned in PubMed articles annotated with the “cancer” MeSH term , effectively ranking them by how frequently they are mentioned in the cancer literature . This family contains 465 genes encompassing all serine-threonine , tyrosine , and dual specificity kinases in the human genome . Results in Fig 6 highlight the importance of rare variants in cancer since many genes with known cancer relevance are not reported by MutSigCV ( shown in blue ) . Several instances exist where these MutSigCV and oncodomain hotspots agree ( purple ) and also where MutSigCV finds significance where the oncodomain method did not ( green ) . Surprisingly , MutSigCV performed poorly for these genes since only two of these genes ( EFGR and BRAF ) were significant in MutSigCV for any cancer type . When compared to both MutSigCV and CHASM ( S2 Fig ) , oncodomain hotspots still identify many more variants than MutSigCV and CHASM combined . However , CHASM is a machine learning method and does not incorporate the frequency of the variant but instead utilizes 70 features calculated from properties of genomic and protein sequence , predicted protein structure , and multiple sequence alignments . CHASM’s Random Forest algorithm is trained on a set of known driver mutations as a positive set and synthetically generated passenger mutations as a negative set . Thus , while MutSigCV would not be able to implicate these rare variants due to insufficient population frequency , CHASM uses properties learned from known driver mutations , which often agree with oncodomain hotspots that utilize population frequency alone . Furthermore , we find that oncodomain hotspots are capable of identifying more rare variants in these kinases than other methods while still identifying the obvious variants that occur with high frequency such as EGFR in LUAD and BRAF in THCA , SKCM , and LUAD . Moreover , oncodomain hotspots are able to identify genes that are known to be associated with particular cancer types where traditional methods may fail . For example the seven genes identified by oncodomain hotspots for COAD ( ERBB2 [109 , 110] , EGFR [111 , 112] , KIT [113 , 114] , BRAF [115–117] , RET [118 , 119] , CDK4 [120–122] , ALK [123–125] , and MAPK1 [126–128] ) are reported to have been involved with COAD . Interestingly , all of these genes were found to be mutated in only six or fewer patients with the exception of BRAF , which was mutated in 32 patients but was still not identified by MutSigCV or CHASM in S2 Fig . In other examples , the SRC gene is a well-known oncogene involved in the PI-3K cascade but no other method is able to detect any significance while oncodomain hotspots identify 8 somatic variants in oncodomain hotspots for LIHC , LUAD , SKCM , and UCEC where some evidence of SRC’s role is known [129–131] . Even for genes that were significant in MutSigCV , oncodomain hotspots are more sensitive as they identify those same genes as significant in more cancer types for which they are known to play a role like BRAF in STAD [132–134] , GBM [135–137] , and UCEC [138 , 139] and EGFR in COAD [111 , 112] , STAD [140 , 141] , and SKCM [142–144] . Indicating the ability of oncodomain hotspots to implicate rare variants , 48 variants on these PKc genes that were found in three or fewer tumor samples fell into oncodomain hotspots and five of these variants were found in only a single tumor sample . To conclude , in sequenced tumor samples , even somatic variants that are known to drive tumor progression can occur with relatively low frequency . Our novel oncodomain method for identifying likely driver variants reveals the structural and functional mutational patterns on conserved protein domains that are unique to each cancer type . This allows us to infer functional importance of even rare somatic variants via inference to somatic variants in other genes sharing a common protein domain . Determining which variants are most important for tumorigenesis will help elucidate the mechanisms driving tumor progression and could ultimately provide a new set of drug targets for families of genes that display similar variation at the structural and functional level . We expect oncodomain hotspots to be an integral tool for assessing novel rare variants in tumor samples , complimenting other existing tools .
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The analysis of somatic variants in sequenced tumor samples is important for understanding the molecular disruptions that underlie the vast differences in individual cancer phenotypes or response to treatment . In order to understand which somatic mutations are functionally important for the initiation or progression of cancer , traditional analyses are ‘gene-centric’ in that they focus on single genes with high mutation frequency in tumor samples . However , many genes with experimental evidence of cancer involvement are found to be mutated in only a few tumor samples , hampering the data-driven identification of important genes . In our analysis , we leverage decades of important findings from structural genomics into the study of somatic variants by utilizing conserved protein domain families . Our method identifies ‘oncodomain hotspots’ , sites within protein domain families with high mutation frequency in tumor samples . This enables our method to assess the importance of even rare variants by comparing to other genes with the same protein domain . By incorporating the structural and functional context encapsulated in protein domain families , we can identify even rare somatic variants in 5 , 437 genes , 3 , 041 of which are novel gene associations to cancer but are similar in structure and/or function to known cancer genes .
|
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2017
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Oncodomains: A protein domain-centric framework for analyzing rare variants in tumor samples
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The ability of HIV to establish a long-lived latent infection within resting CD4+ T cells leads to persistence and episodic resupply of the virus in patients treated with antiretroviral therapy ( ART ) , thereby preventing eradication of the disease . Protein kinase C ( PKC ) modulators such as bryostatin 1 can activate these latently infected cells , potentially leading to their elimination by virus-mediated cytopathic effects , the host’s immune response and/or therapeutic strategies targeting cells actively expressing virus . While research in this area has focused heavily on naturally-occurring PKC modulators , their study has been hampered by their limited and variable availability , and equally significantly by sub-optimal activity and in vivo tolerability . Here we show that a designed , synthetically-accessible analog of bryostatin 1 is better-tolerated in vivo when compared with the naturally-occurring product and potently induces HIV expression from latency in humanized BLT mice , a proven and important model for studying HIV persistence and pathogenesis in vivo . Importantly , this induction of virus expression causes some of the newly HIV-expressing cells to die . Thus , designed , synthetically-accessible , tunable , and efficacious bryostatin analogs can mediate both a “kick” and “kill” response in latently-infected cells and exhibit improved tolerability , therefore showing unique promise as clinical adjuvants for HIV eradication .
HIV/AIDS is a catastrophic pandemic that has claimed an estimated 35 million lives . Approximately 37 million individuals are currently HIV-positive . In 2015 , 2 . 1 million individuals were newly infected , and 1 . 1 million died of AIDS-related illnesses [1] . Antiretroviral therapy ( ART ) , the current recommended treatment for HIV infection , suppresses viral replication and prevents disease progression , allowing infected individuals to live with the disease [2] . However , ART does not cure the infection , in part because replication-competent HIV can persist within latently-infected CD4+ T cells throughout many years of continuous therapy [3 , 4 , 5] . These reservoir cells contain integrated HIV proviral genomes and can episodically re-supply active virus . Treatment thus requires the life-long use of ART , which is associated with numerous problems including health issues related to chronic chemoexposure , high financial cost and the need for strict compliance [6] . The development of strategies to reduce or eliminate the reservoir of latently-infected cells is therefore a research and clinical priority of global significance . Several strategies for eliminating latent HIV have been proposed ( reviewed in [7] ) . One promising approach is often referred to as “activation-elimination” or “kick and kill” . This is based on the observation that latently-infected cells do not typically express viral proteins and are therefore not killed directly by virus production ( viral cytopathic effects ) or through immune effector mechanisms such as cytotoxic T lymphocytes ( CTL ) or natural killer cells , which require viral protein expression to recognize infected cells . However , if HIV expression can be induced in latently-infected cells ( kick ) then these cells could become susceptible to cytopathic effects or virus- or immune-mediated cell killing mechanisms ( kill ) . It is currently unknown whether inducing expression of latent HIV in vivo alone is sufficient to deplete some or all latently-infected cells , or whether the “kill” arm of the approach will require augmenting , for example , with broadly-neutralizing anti-HIV antibodies [8 , 9] , anti-HIV immunotoxins [10] , pre-stimulated or genetically engineered CTLs [11 , 12] , or other mechanisms . The capacity of a particular stimulatory signal to result in the death of latently-infected cells is likely connected to its relative ability to induce HIV protein expression , with weak HIV latency reversing agents ( LRAs ) inducing little or no protein expression , and strong LRAs potentially inducing expression of sufficient HIV protein to trigger viral cytopathicity and/or immune surveillance in the host . However , this approach is further complicated because HIV expression is tightly connected to the activation state of the host CD4+ T cell , meaning that very strong LRAs might also induce CD4+ T cell activation , proliferation , and/or generalized immune stimulation accompanied by hypercytokinemia ( cytokine storm ) such as can occur following in vivo administration of the anti-CD3 antibody OKT3 along with interleukin ( IL ) -2 [13] . Therefore , an ideal LRA would strongly induce HIV expression and cause the death of latently-infected cells without over-activating immune cells . Numerous studies on agents that induce latency reversal through several different cellular pathways have been reported [2 , 14 , 15 , 16] . Of these , protein kinase C ( PKC ) modulators are an especially promising LRA class of preclinical leads , serving either as single agents or in combination with additional LRAs [2 , 14 , 17 , 18] . The vast majority of prior in vitro HIV studies with PKC modulators has focused on one of the first reported LRAs , naturally-occurring prostratin , with a more recent interest being directed also at ingenol esters and bryostatin 1 [19 , 20] . The bryostatins are a collection of at least 21 structurally related macrolactone natural products originally isolated from the marine bryozoan Bugula neritina [21] . Bryostatin 1 ( Fig 1A ) , the most studied of the naturally occurring bryostatins [22] , modulates PKC activity at low nanomolar concentrations and is implicated in a broad range of biological activities . Its use in cancer therapy has been explored in over 40 phase I and phase II clinical trials . It has also been studied in a phase IIb trial [23] for moderate-to-severe Alzheimer’s disease [24 , 25] . Additionally , bryostatin 1 potently induces HIV from latency in various in vitro models [17 , 26] , and is therefore a lead clinical candidate in HIV eradication efforts . This positive in vitro activity prompted a recent phase I clinical study in ART-treated patients , which showed that bryostatin 1 was safe at low doses , but higher doses would be required to effect PKC-mediated latency reversal [27] . The potential of lowering the dose of bryostatin 1 and thus increasing its tolerability with combination LRAs was not explored . Notwithstanding its clinical potential , the supply of bryostatin 1 is uncertain as it is produced in only low and variable amounts by its marine source organism . Sustainable harvesting of that source raises cost and environmental concerns . Most importantly , the natural bryostatins , serving putatively in part as antifeedants in their marine ecosystem , are neither evolved , optimized nor readily tuned for therapeutic applications such as HIV latency reversal . Indeed , natural products themselves represent only a small percentage ( 6% ) of new chemical entities introduced as drugs with the vast majority being derivatives or agents inspired by natural products [28] . To address in part the limited availability of natural products , the difficulty often encountered in their chemical derivatization due to their structural complexity and scarcity , and their generally unoptimized clinical potential , we have focused on a function oriented synthesis ( FOS ) strategy directed at creating therapeutic function through synthesis-informed design [29] . In brief , rather than focusing on structure alone , which is an all-or-nothing approach , FOS focuses on function which could be achieved with a wide range of structures through innovative design . Toward this end , based on a computer analysis of PKC modulators , we previously proposed [30] that only a subset of features in the complex bryostatin 1 structure might be responsible for its biological activity , and used [26] this pharmacophore model to design the first simplified and more synthetically accessible bryostatin analogs ( “bryologs” ) . Here , we report on the ability of novel bryologs to function like bryostatin 1 in activating viral gene expression from the actual latent HIV reservoir and to quantify in vivo activity and tolerability of one particularly promising analog ( SUW133 ) as required for its preclinical advancement . In a clear demonstration of the potential value of the FOS approach , we show that this bryolog is somewhat better tolerated in vivo than bryostatin 1 , that it is more effective in activating expression of latent HIV in human cells , and , significantly , that this expression ( “kick” ) causes a subset of these infected host cells to die without requiring an additional "kill" approach .
The structures of bryostatin 1 and bryolog SUW133 along with PKC isoform affinities are shown ( Fig 1 ) . In a preliminary screen for LRA activity , we previously demonstrated that SUW133 and several other bryologs effectively induced HIV from latency in the J-Lat 10 . 6 cell line [26] . Since HIV latency reversal varies depending on the model system used , and evaluating potential LRAs using primary latently-infected cells from ART-treated patients is an important step in their characterization , we have now tested SUW133 for its capacity to induce virus expression from resting CD4+ T cells obtained from six HIV-infected patients with viral loads suppressed by effective ART ( Subjects 1–6 , S1 Table ) . SUW133 induced significant levels of latent virus from these true reservoir cells ( Fig 2A ) as well as in an alternative cell line model for HIV latency ( U1 cells: S1 Fig ) . To directly compare SUW133 treatment with other commonly tested HIV LRAs and maximal T cell stimulation , CD4+ T cells were isolated from a further 3 ART-treated patients with undetectable plasma viral loads ( Subjects 7–9 , S1 Table ) . These cells were exposed to clinically relevant concentrations [31] of the histone deacetylase inhibitors panobinostat or vorinostat , the BET bromodomain inhibitor JQ1 , the PKC modulator bryostatin 1 , or anti-CD3+anti-CD28 antibody costimulation ( Fig 2B ) . SUW133 significantly outperformed panobinostat , vorinostat , JQ1 , and bryostatin 1 treatment in this assay , and induced approximately 1/3 the amount of cell-free virion-associated HIV RNA as maximal T cell stimulation ( costimulation ) . These data indicate that SUW133 is capable of inducing HIV from latency in primary patient-derived latently-infected cells . Prostratin and bryostatin 1 can each induce expression of the early T cell activation marker CD69 . We found that SUW133 also upregulates expression of this cell surface marker on primary CD4+ T cells ( Fig 3A ) , and that for a variety of synthetic prostratin and bryostatin analogs [18 , 26] this effect occurs at similar concentrations to those required to activate HIV from latency ( Fig 3B ) . This indicates that CD69 serves as an in vivo biomarker to assess when biologically active concentrations of a compound have been delivered and thus can be used to evaluate the compound’s relative potency . To determine whether the PKC modulator SUW133 has improved properties over bryostatin 1 in vivo , ascending concentrations of bryostatin 1 or SUW133 were administered by intraperitoneal injection into C57/bl6 mice and the relative in vivo bioactivity and acute toxicity of the compounds were evaluated . Both bryostatin 1 and SUW133 induced high levels of CD69 in murine splenocytes at 24h post-injection . However , SUW133 exhibited superior activity , with over 80% of CD4+ splenocytes expressing CD69 versus a maximum of approximately 60% in bryostatin 1 treated animals ( Fig 4A and 4B ) . Importantly , while bryostatin 1 had a very narrow toxicity free activation window between 2 . 5 μg and 5 μg per animal , SUW133 induced CD69 expression at doses as low as 1 μg and in some cases was tolerated at doses of up to 30 μg per animal . Thus SUW133 exhibits a moderately greater “therapeutic window” than the natural product and better induction , showing that toxicity and activation can be decoupled by structural variations . HIV latency purging strategies would ideally involve a transient “pulse” of stimulation that would be sufficient to induce activation and elimination of a portion of latent virus reservoir cells without causing persistent immune activation . Repetition of this treatment could eventually eliminate the latent reservoir . A single injection of 10 μg of SUW133 indeed resulted in transient expression of CD69 on the vast majority of murine splenocytes ( Fig 4C ) which declined by day 9 post-injection . CD25 ( a late T cell activation marker and component of the IL-2 receptor ) expression on CD3+ T cells in the spleens of these animals remained unchanged ( S2 Fig ) . Moreover , no detectable increase in plasma concentrations of TNF-alpha , interleukin ( IL ) -2 , and MIP-1-alpha , and only a short-term elevation in IL-6 levels was observed following administration ( S3 Fig ) . We next explored the activity of SUW133 in humanized mice , a proven , important and relevant model for studying HIV in vivo . The BLT mouse model [32] supports multi-lineage human immune reconstitution in many tissues within the mouse and represents one of the most advanced small animal models available for investigating HIV persistence and pathogenesis [33] . We and others have shown that BLT mice can be infected with HIV and form authentic post-integration latency in resting human CD4+ T cells [34 , 35] . We therefore elected to use this model to determine whether SUW133 could induce latent HIV expression in vivo . We initially verified the bioactivity of SUW133 in human cells in uninfected BLT mice by quantifying activation of CD69 expression in the spleen and blood one day after stimulation ( Fig 5 ) . This resulted in robust activation of CD69 with limited CD25 expression in both CD4+ T cells and other T cell subsets . Given the strong correlation between HIV latency reversal and CD69 upregulation we had previously identified ( Fig 3 ) , these data suggested that SUW133 would also be capable of inducing expression of latent HIV in vivo . To test this hypothesis , we then infected BLT mice with NL-HA , a replication-competent , pathogenic HIV reporter strain that encodes a short epitope from influenza hemagglutinin ( HA ) in place of vpr [36] . If cells are productively infected with this virus and are expressing viral proteins , the HA reporter protein is expressed on the cell surface . Consequently , induction of virus expression from latently-infected cells can be detected by flow cytometry . We have previously found that similar reporter viruses effectively establish latency in vivo in humanized mice [10 , 34 , 37] . Here , BLT mice were infected for 3–4 weeks then treated with ART ( consisting of daily injections of emtricitibine ( FTC ) , tenofovir disoproxil fumarate ( TDF ) , and raltegravir for 2–5 weeks ) and suppression of viral loads was monitored by RT-PCR . Mice were then bled to provide a baseline percentage of HA+ human cells in the blood , and subsequently treated with SUW133 by IP injection while maintaining ART ( Fig 6A ) . After an overnight incubation , mice were sacrificed and the percentage of HA+ ( HIV-expressing ) cells was quantified in the peripheral blood ( Fig 6B–6D , and S4 Fig ) and spleen ( Fig 6E ) . For spleen , control mice that had been infected and treated with ART but not stimulated with SUW133 were included as unstimulated comparators . In these assays , SUW133 induced significant numbers of latently-infected cells to express viral proteins , as measured by increases in the percentage of HA+ cells after in vivo stimulation ( Fig 6E ) . An important and as yet unanswered question is whether LRA-induced activation of HIV from latency alone is sufficient to cause death of the host cell , either through viral cytopathic effects or host anti-viral immune responses ( anti-HIV immune responses are detectable in BLT mice , [38] although at minimal levels ) . We therefore included two vital dyes in some of the analysis of infected cells that had been stimulated from latency in vivo . Ghost Red and Zombie Yellow dyes are excluded from cells with intact membranes and are enriched in dead or dying cells . We found that within 24 hours of activation , up to 25% of the cells induced to express HIV in vivo by SUW133 administration also stained with these cell death dyes , compared with a very low background staining in HA-negative cells from the same animals ( Figs 7 and S5 ) , demonstrating that a portion of the cells induced to express HIV proteins in vivo , in the absence of an additional “kill” approach , subsequently died .
“Kick and kill” approaches represent promising strategies for eliminating latent HIV and thus contributing to the cure of HIV-infected individuals . Yet , these approaches are limited by a relative lack of safe and effective LRAs that can strongly induce HIV expression in vivo [39] . Protein kinase C modulating agents such as bryostatin 1 are among the most effective LRAs to be tested [14] . However , preclinical development of this compound has been hampered by its lack of availability due to its scarce and variable production by its source organism and the attendant logistical , financial , and environmental issues associated with massive harvesting of that source organism from fragile marine reserves . Moreover , due to its complexity and insufficient availability , little has been done to modify the natural product to improve its therapeutic efficacy ( with respect to HIV latency reversal ) and tolerability ( dose-limiting toxicity , myalgia ) . Indeed , little is known even about whether efficacy and tolerability could be decoupled by such changes in the structure . Here , we demonstrate that a designed , synthetically accessible PKC activator inspired by bryostatin 1 exhibits a pan-PKC binding selectivity similar to bryostatin 1 , better tolerability in vivo than the natural product and a more potent capacity to activate HIV from latent reservoirs ex vivo and in vivo . Significantly , this activation leads to death of some latently-infected cells , thus providing for both “kick” and “kill” activities . It is likely that the observed death of previously latently-infected cells would increase if tested over longer periods of time or with repeated dosing , and could also be further improved by inclusion of therapeutic agents that are specifically intended to kill cells actively expressing HIV , potentially including cell-based therapeutics such as HIV-specific cytotoxic T lymphocytes [11] , natural killer cells , or Env-specific antibody based approaches including broadly neutralizing antibodies [8] or immunotoxins [10] . Similarly , the use of LRAs that would synergize with such PKC modulators would be expected to further improve tolerability by dose reduction [31] . Enhancements in delivery such as incorporation of bryologs into CD4-targeted nanoparticles as we have previously described for bryostatin 1 could also represent a more specific and efficient means to reactivate latent virus in CD4+ cells while reducing unwanted side-effects that are mediated through cell types that are not typically HIV-infected [40 , 41] . Several factors might contribute to the variable level of death observed in those cells actively-expressing HIV ( Figs 7 and S5 ) . Data on cell killing were obtained at a single timepoint around 24 hours post-activation . These data thus represent only a snapshot of an ongoing process . It is therefore very likely that additional cells could die at later timepoints . Furthermore , since humanization levels and anti-HIV immune responses are not identical in all animals , variations in the anti-HIV innate and adaptive immune responses in different humanized mice could lead to corresponding variations in the killing of cells that have been recently reactivated from latency . The amount of HIV protein produced in the newly reactivated cells may also in some cases be below the threshold required to trigger viral cytopathic effects that contribute to host cell death . This concept of multifactorial intracellular and extracellular signals contributing to the death of HIV-infected cells during post-integration expression is consistent with the observation that apoptosis in cells harboring pre-integrated virus can be influenced by a variety of different factors including microenvironmental stimuli such as cytokines [42] . Future extensions of the current study would include experiments designed to determine whether this single-dose of LRA is capable of inducing a long-term reduction in the frequency of latently-infected cells in the animals . We also did not directly measure the fraction of HIV proviruses from which SUW133 could reactivate HIV expression , or whether SUW133 is capable of inducing any of the apparently intact proviruses that have proven refractory to reactivation through other means [43] . Future studies could also entail determining the effects of multiple sequential activating events on the latent reservoir . The long-term consequences of inducing even modest levels of T cell activation ( as exemplified here by CD69 upregulation without abundant CD25 expression ) in essentially all T cells in vivo using PKC modulating LRAs are also unknown , and should be considered along with results from acute toxicity experiments in further preclinical studies . Nevertheless , given the vast body of research directed at naturally occurring LRAs , this proof of principle study , based on one of the most advanced small animal models available for investigating HIV persistence and pathogenesis , establishes that robust induction of HIV expression can be achieved with a designed agent and , significantly , that this agent causes death of some HIV reservoir cells . This study demonstrates how a natural lead can be used as an effective template for the design of a more accessible , efficacious , better tolerated and tunable analog representing now one of the most promising adjuvants for use with ART in strategies to eradicate HIV .
Primary human cells from HIV seronegative individuals were cultured in “RF10 medium” consisting of RPMI 1640 medium supplemented with 10% fetal bovine serum ( FBS , Omega Scientific ) , 100 U/mL of penicillin , and 100 μg/mL of streptomycin ( Invitrogen ) . PBMCs were isolated using Ficoll-Paque Plus separation ( GE Healthcare ) . Primary CD4+ T cells were separated from PBMCs by negative immunomagnetic selection using the CD4+ T cell Isolation Kit ( Miltenyi Biotec ) according to the manufacturer’s instructions . Cells were then exposed to compound for 24 hours before staining and flow cytometric analysis of CD69 levels . Compound incubations were performed by seeding 105 cells/well in 100 μL of RF10 media containing the appropriate concentration of compound in wells of a v-bottomed 96-well plate . Bryostatin 1 ( Tocris bioscience ) and Prostratin ( LC Laboratories ) were obtained commercially , and bryologs or prostratin analogs were synthesized as previously described [18 , 26] . C57/bl6 mice were obtained from the UCLA Department of Radiation Oncology . Compounds were suspended in DMSO and then further diluted in RF10 media to produce a final volume of 500 μL ( containing a maximum 4% DMSO ) . These 500 μL volumes were administered to mice by intraperitoneal injection . Mice were monitored for acute toxicities and then sacrificed at the indicated times . Spleens were removed and disaggregated using a steel mesh , and then filtered through a 40 μm filter to produce a single-cell suspension . The resultant splenocytes were then stained for flow cytometry to analyze surface expression of relevant markers . Humanized bone marrow liver thymus ( BLT ) mice [32] were constructed by the UCLA humanized mouse core using techniques described previously [34 , 44 , 45] . In brief , NOD . Cg-Prkdcscid IL2rgtm1Wjl ( Nod-SCID-common gamma chain knockout [NSG] ) mice were first irradiated with 270 rads and then transplanted under the kidney capsule with pieces of fetal thymus and liver tissue . Mice were then infused intravenously by retro-orbital injection with 5x105 human fetal liver-derived CD34+ cells isolated by immunomagnetic separation as previously described [46] . At 8 weeks post-transplantation and approximately every 2 weeks thereafter mice were evaluated for reconstitution with human cells . Mice were bled as previously described [34] and peripheral blood mononuclear cells analyzed by flow cytometry . Once reconstituted , mice were infected by retro-orbital injection with HIV strain NL-HA ( 200ng p24 per mouse ) , which is a near full-length , pathogenic strain of HIV that encodes a short HA epitope from influenza hemagglutinin in place of vpr that is expressed on the surface of productively infected cells [36] . Mice were bled periodically and plasma viral loads monitored by RT-PCR conducted by the UCLA CFAR virology core . Mice were treated with antiretroviral therapy consisting of emtricitibine ( FTC ) , tenofovir disoproxil fumarate ( TDF ) , and raltegravir , essentially as described previously [47] , which were administered through daily subcutaneous injection . For mice that were treated with a PKC modulator , the mice were first bled ( for pre-stimulation analysis of plasma viral load and HIV expression ) and then the PKC modulator was administered by intraperitoneal injection . Because BLT mice are usually less robust than wild-type mice ( due to surgery , humanization , and multiple bleeds etc . ) , the lowest levels of SUW133 in the HIV-infected BLT mice that induced significant activation of biomarker expression in wild-type mice were tested first . The following day ( 17–24 h after PKC modulator administration ) the mice were sacrificed and then PBMC and splenocytes were analyzed by flow cytometry . During standard flow cytometry staining , samples of 105 cells were suspended in 50 μL of a 1:1 dilution of phosphate buffered saline ( PBS ) :Human AB serum ( Sigma ) . Staining of C57/bl6 mouse splenocytes was performed by adding the following anti-mouse fluorescent conjugated antibodies: CD3-Fluorescein isothiocyanate ( FITC , eBioscience 11-0032-82 ) ; CD69-Phycoerythrin ( PE , eBioscience 12-0691-83 ) ; and CD4-Allophycocyanin ( APC , eBioscience 17-0041-83 ) . In some experiments CD4-APC was replaced with CD25-APC ( eBioscience 17-0251-82 ) and CD4-Phycoerythrin Cyanin 7 ( PC7 , eBioscience 25-0041-82 ) . For humanized mouse studies , PBMC and splenocytes from BLT mice were stained with anti-mouse CD45-Alexa 700 ( Biolegend 103128 ) and the following anti-human antibodies: CD45-e450 ( eBioscience 48-0459-42 ) ; CD3-APC-780 ( eBioscience; 47-0038-42 ) ; CD4-V500 ( BD biosciences; 560768 ) ; CD8-Peridinin chlorophyll protein ( PerCP ) -710 ( eBioscience 46-0087-42 ) ; CD25-PE ( Beckman IM0479U ) ; CD69-APC ( Biolegend 310910 ) ; CD14-PE-Cy7 ( Biolegend 325618 ) . Cells were separately stained for anti-human CD45 e450- ( eBioscience 48-0459-42 ) and anti-HA-Biotin PE ( Roche 12158167001 ) followed by a secondary Streptavidin , R-Phycoerythrin Conjugate ( Invitrogen s866 ) stain . This two-step stain was performed by incubating 1-2x106 cells at 4°C for 20–30 min , washing with 2% FBS in PBS , then adding the secondary stain , incubating again at 4°C for 20–30 min , and washing with 2% FBS in PBS before fixation with 2% paraformaldehyde . In some experiments , cell death was also evaluated by GHOST Red 780 ( Tonbo Biosciences 13–0865 ) or Zombie Yellow ( Biolegend 423104 ) stains . For this procedure , 106 cells were suspended in 100 μL PBS , and then the Zombie yellow and GHOST Red 780 dyes added . Cells were incubated for 20 min at room temperature in the dark and then washed twice with 2% FBS in PBS . Cells were then fixed with 2% paraformaldehyde and incubated for 20 min in the dark . In some experiments cells were then washed with 2% FBS in PBS , resuspended in 1:1 PBS:Human AB serum and then stained for human CD45 and HA , CD3-PE-Cy5 ( Biolegend 300310 ) , CD4-ECD ( Beckman 6604727 ) , CD8 Per-CO710 ( eBioscience 46-0087-42 ) and CD3-APC ( Cell signaling 9602S ) as described above . For human PBMC CD69 induction experiments , cells were stained with CD69-ECD ( Beckman Coulter 6607110 ) . During staining , cells were incubated at 4o C for 20 minutes , washed with 2% FBS in PBS , and then resuspended in 2% paraformaldehyde . Stained samples were stored at 4o C . Flow cytometry samples were analyzed using a Cytomics FC 500 ( Beckman Coulter ) or a LSR Fortessa ( BD Biosciences ) flow cytometer . Data were analyzed using FlowJo ( v7 ) software . Quantification of the levels of the murine cytokines IL-2 , IL-6 , MIP-1-alpha , and TNF-alpha in mouse plasma samples was performed using a custom Milliplex Mouse Cytokine/Chemokine Magnetic Kit ( Millipore ) according to the manufacturer’s recommendations . Wash steps were performed using a Bio-Plex II Wash Station ( Bio-Rad ) , and the data were acquired using a Bio-Plex 200 System with high-throughput fluidics ( Bio-Rad ) . The resultant data were analyzed using Bio-Plex Manager 6 . 1 software . Tests of independent groups were performed using the Wilcoxon Rank Sum test . For hypotheses where we , a priori , expected an increase due to stimulation , we used a 1-sided test . All other tests were two-sided . For mouse and in vitro studies , preliminary results suggested that in most cases a sample size of 3 in each group would be sufficient to observe a significant effect using a 1-sided Wilcoxon rank sum test . Where resources allowed , we added additional mice/replicates to allow for variance in the estimates . BLT mice with fully-suppressed viral loads were included in the analysis . No specific randomization or blinding was performed . For cytokine analysis , where cytokine concentration values were below the limit of detection , a value that was midway between 0 and the limit of detection was used . Paired data was analyzed using the Wilcoxon Signed Rank Test for paired data . If , a priori , we expected a decrease , we used a 1-sided test , otherwise we used a two-sided test . The figure legends state specifically if a test is 1 or 2 sided . Linear regression was used to assess the relationship between CD69 expression and latency activation . Residual plots and the R2 value suggest a good fit to the model . U1 cells [48] ( tested negative for bacteria , mold , yeast , and mycoplasma ) were obtained through the AIDS Reagent Program , Division of AIDS , NIAID , NIH from Dr . Thomas Folks . Cells and compound were seeded in a final 200 μL volume of RF10 media at a cell density of 20 , 000 cells/well in v-bottomed 96 well plates and incubated at 37°C for 48 hours . After incubation , cells were pelleted and supernatant harvested then diluted in a 2% Triton-x-100/phosphate buffered saline solution . The concentration of HIV p24 protein in the culture supernatant was quantified using the HIV p24 antigen enzyme linked immunosorbent assay ( ELISA ) kit ( Beckman Coulter ) according to the manufacturer’s instructions . J-Lat cells ( clone 10 . 6 ) [49] ( tested negative for bacteria , mycoplasma , and fungi ) were obtained through the AIDS Reagent Program , Division of AIDS , NIAID , NIH from Dr . Eric Verdin . Cells were incubated in 100 μL volume of RF10 medium containing the indicated concentrations of compound for 48h before analysis . Stimulations were performed in v-bottomed 96-well tissue culture plates with a starting cell density of 25000 cells/well . During harvesting , cells were washed and resuspended in 2% paraformaldehyde . The percentage of cells expressing GFP was then quantified by flow cytometry using a FC 500 flow cytometer ( Beckman Coulter ) and FlowJo software ( version 7 . 6 ) . Nine HIV-infected individuals receiving ART for a median of 2 . 6 years ( range 0 . 4–9 . 1 ) were studied . All study subjects maintained undetectable levels of plasma viremia ( <50 copies/mL ) at the time of study ( S1 Table ) . CD4+ T cells were isolated from cryopreserved peripheral blood mononuclear cells ( PBMCs ) using a cell enrichment kit ( StemCell Technologies ) . For patient samples 1–6 , resting CD4+ T cells were further isolated by depleting CD25+ , HLA-DR+ and CD69+ CD4+ cells using PE-conjugated antibodies ( BD Biosciences ) and anti-PE microbeads ( Miltenyi Biotec ) . CD4+ T cells were incubated with RPMI 1640-based medium containing antiretroviral drugs consisting of T-20 ( 100 μg/mL ) , tenofovir ( 1 μM ) , and emtricitabine ( 1 μM ) , and test stimuli at concentrations indicated in the figures . The copy number of virion-associated HIV RNA in the cell culture supernatants was measured using the Cobas Ampliprep/Cobas Taqman HIV-1 Test , Version 2 . 0 ( Roche Diagnostics ) following 36–48 hours of incubation of cells with the above study compounds . The limit of detection was 20 copies/mL . SUW133 corresponds to analog 4 in a previous study describing its synthesis [26] . The final Ki data for each of the isoforms for SUW133 is as follows:
|
HIV can persist for many years in a latent ( non-expressing ) state in individuals treated with antiretroviral therapy , and these latently-infected cells represent a major barrier to curing HIV infection . One potential approach to eliminating the reservoir cells is to induce them to express viral proteins while maintaining antiretroviral therapy , allowing these cells to be killed by the virus or the immune response . However , this approach has been hampered by a lack of compounds that can safely and effectively reverse HIV from latency in a living organism . Here we show that a designed , synthetic protein kinase C modulating compound is better-tolerated than the naturally-occurring product ( bryostatin 1 ) in mice and potently induces HIV expression from latency in a mouse model containing a human immune system . Importantly , this induction of virus expression causes some of the newly HIV-expressing cells to die . Hence , this compound shows unique promise as a component of the so-called “kick and kill” approach for HIV eradication .
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2017
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In vivo activation of latent HIV with a synthetic bryostatin analog effects both latent cell "kick" and "kill" in strategy for virus eradication
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Mouse Ikbkap gene encodes IKAP—one of the core subunits of Elongator—and is thought to be involved in transcription . However , the biological function of IKAP , particularly within the context of an animal model , remains poorly characterized . We used a loss-of-function approach in mice to demonstrate that Ikbkap is essential for meiosis during spermatogenesis . Absence of Ikbkap results in defects in synapsis and meiotic recombination , both of which result in increased apoptosis and complete arrest of gametogenesis . In Ikbkap-mutant testes , a few meiotic genes are down-regulated , suggesting IKAP's role in transcriptional regulation . In addition , Ikbkap-mutant testes exhibit defects in wobble uridine tRNA modification , supporting a conserved tRNA modification function from yeast to mammals . Thus , our study not only reveals a novel function of IKAP in meiosis , but also suggests that IKAP contributes to this process partly by exerting its effect on transcription and tRNA modification .
Meiosis is a fundamental and highly regulated process that takes place during gamete generation . Faithful execution of this process is essential for maintaining genome integrity . Errors and various types of disruption during meiosis can cause aneuploidy and result in developmental defects , including mental retardation in Trisomy 21 , infertility , to name two [1] . During the prophase I stage of the first meiotic division , homologous chromosomes undergo pairing and synapsis . Synapsis is mediated by a protein complex namely the synaptonemal complex ( SC ) , and is accompanied by chromosome recombination [2] . Unlike homologous autosomes , the X and Y chromosome synapsis occurs only at a very small region of homology , a pseudoautosomal region ( PAR ) [3] . Formation of the fully synapsed autosomal SCs as well as the partially synapsed sex chromosome are essential for DNA repair , recombination and subsequent desynapsis [4] . Consequently , DNA damage response ( DDR ) is initiated upon the recognition of the DNA lesion made by SPO11 , which is a type II-like topoisomerase that induces double-stranded breaks ( DSBs ) [5] . At the DSB sites , the DNA repair machinery generates DNA recombination between homologous chromosomes to ensure proper disjunction at metaphase I . The genetic studies in yeast and mouse helped identify many factors important for meiosis [6] , [7] , [8] , such as: the master regulators meiosis-inducing protein 1 ( Ime1 ) in yeast , and A-MYB ( MYBL1 ) in mouse [9] , [10] . Despite great progress in understanding the transcriptional regulation of the meiotic process [2] , very little is known about the role of translational control during this process . Our data presents evidence that the evolutionarily conserved factor Ikbkap/Elp1 governs meiotic progression at the level of both transcription and translation . Elp1 , also referred to as IKAP ( Inhibitor of kappaB kinase -associated protein ) , functions as a scaffold protein that assembles the Elongator and is encoded by Ikbkap gene ( we will use the MGI nomenclature , IKAP for the protein , and Ikbkap for the gene , hereafter ) . Elongator is a protein complex comprised of two copies of the core complex , Elp1–3 , and a sub-complex , Elp4–6 [11] . The protein complex “Elongator” was first purified in budding yeast through its association with the elongating RNA polymerase II ( RNAP II ) [12] . Similar protein complex was subsequently purified from human cells [13] , [14] , [15] . Interestingly , the components of the protein complex are highly conserved in different species that include yeast and human . The Elongator complex has important biological functions as deletion or mutation of any of its subunits results in severe phenotypes in yeast . Among the Elongator components , Elp3 likely serves as a catalytic subunit , because it not only harbors motifs characteristic of the GCN5 family of histone acetyltransferases ( HATs ) , but also has been shown to directly acetylate H3 lysine 14 ( H3K14 ) and possibly H4K8 in vitro [16] . These findings , combined with the studies demonstrating the association of Elongator with RNAP II holoenzyme , its ability to bind to nascent pre-mRNA , and to facilitate RNAPII transcribes through chromatin in an acetyl-CoA-dependent manner , support its role in transcription regulation [12] , [14] , [17] . Accumulating evidence suggest that Elongator , in addition to participating in transcriptional regulation , also plays pivotal role in the regulation of translation . The first evidence implicating the involvement of the Elongator in translation came from a genetic screen , which demonstrated that all genes encoding the yeast Elongator subunits are required for the formation of 5-carbamoylmethyl ( ncm5 ) , and 5-methoxycarbonylmethyl ( mcm5 ) side chains on uridines at the wobble position of certain tRNAs [18] . These modified nucleosides are important for efficient decoding of A- and G- ending codons through stabilizing codon-anticodon interactions during translation [19] , [20] , [21] . Studies have shown that all the six subunits of the Elongator are required for the early step of mcm and ncm side chain formation [18] . Although it is currently unclear how Elongator contributes to the generation of the modified tRNAs , this function is conserved in S . cerevisiae , S . pombe , C . elegans , and A . thaliana [18] , [22] , [23] , [24] . Whether such function is conserved in mammals remains to be determined . Using a loss-of-function approach , we demonstrate that IKAP plays an important role in male meiosis . First , we show that IKAP is highly expressed in male germ cells . Targeted deletion of Ikbkap in mice resulted in increased apoptosis in male germ cells and male infertility . Interestingly , autosomal and sex chromosome synapsis defects are observed in Ikbkap mutant spermatocytes . In addition , sustained RAD51 foci are observed on the autosomes of mutant spermatocytes , suggesting a homologous recombination repair defect . Detailed molecular studies revealed that the expression of a few meiotic genes is down-regulated in mutant testes . Furthermore , the levels of the Elongator-dependent tRNA modifications are reduced in the mutant testes . Our study thus reveals a critical function of Ikbkap in male meiosis , and demonstrates a conservation tRNA modification function in mammalian cells .
To explore a possible role of IKAP in gametogenesis , we analyzed the expression pattern of IKAP during spermatogenesis by immunofluorescence staining . This analysis revealed that IKAP is expressed in Tra98-positive gonocytes as early as postnatal day 0 ( P0 ) with a predominant cytoplasmic localization ( Figure 1A ) . This expression and localization pattern is maintained at P8 , as prospermatogonia developed into PLZF-positive undifferentiated spermatogonia ( Figure 1A ) . At P21 , IKAP expression remains in SYCP3-expressing meiocytes ( Figure 1A ) . At late stage of spermatogenesis , IKAP was detected in RNA polymerase II-positive round spermatids ( Figure 1A , arrows ) , but not in transition protein 1 ( TNP1 ) -positive elongated spermatids at P35 ( Figure 1A ) . In contrast to specific expression in germ cells , IKAP is undetectable in the GATA1-positive Sertoli cells ( Figure 1B , arrow ) or 3β-HSD positive Leydig cells ( Figure 1B ) . We also used the conditional knockout testes ( as below ) as negative controls for the purpose of antibody validation ( Figure S1 ) . Collectively , immunofluorescence staining revealed that IKAP is expressed in all stages of male germ cells except the elongated spermatids , but it is almost undetectable in somatic cells of testis . The germ cell-specific expression pattern of IKAP revealed above suggests that IKAP might have a role in spermatogenesis . Previous studies have demonstrated that Ikbkap null mutant mice die of cardiovascular and neuronal developmental defects at embryonic day E10 [25] , [26] . To bypass the embryonic requirement for Ikbkap , we used mice harboring a conditional knockout allele for Ikbkap with exon 4 flanked by two loxP sites ( Figure S2A ) . Mice homozygous for the Ikbkapflox conditional allele were viable and were born at Mendelian ratio ( data not shown ) . To explore the function of Ikbkap in spermatogenesis , we inactivated Ikbkap in the male germ line by crossing with the Vasa-Cre mice ( also known as Ddx4-Cre ) . Vasa-Cre induces recombination in germ cells starting from E15 . 5 , and is expressed in all spermatogenic cells postnatally [27] . The germ linage conditional Ikbkap mutant mice ( genotyped as Vasa-Cre; Ikbkapflox/− , referred to as CKO hereafter ) were obtained by crossing Ikbkapflox/flox females with Vasa-Cre; Ikbkapflox/+ males . The genotypes of control mice were either Vasa-Cre; Ikbkapflox/+ , or Ikbkapflox/− , or Ikbkapflox/+ . RT-qPCR analysis using P16 mouse testes demonstrates that the deletion efficiency is more than 80% ( Figure S2B ) . Western blot analysis and immunostaining using two commercial antibodies revealed marked reduction of IKAP protein in the CKO testes ( Figure S2C , S2D and data not shown ) . To test for a possible function of IKAP in spermatogenesis , CKO and control male mice were mated with wild-type females and the breeding capacity was monitored for 3 months . While the control mice gave birth at an average litter size 6 . 7±1 . 5 , no pups were obtained from wild-type females mated with CKO males , even though copulatory plugs were frequently observed in the females . These results suggest that loss of function of Ikbkap in male mice causes infertility . To determine the potential cause of infertility , we examined the size of male gonads and the presence of spermatozoa in the epididymis . We found that the size of the testes is significantly decreased in the CKO mice , and the testicular weight to body weight ratio was reduced by 25% at the age of 14 month ( Figure 2A ) . No spermatozoa were found in the epididymis of 2-month old CKO mice ( Figure 2B ) . Histological analyses indicated stage IV ( mid-pachytene ) arrest of the seminiferous epithelium ( Figure 2B ) , which is typical of diverse mouse meiotic mutants [28] . Indeed , in contrast to control seminiferous tubules , CKO testes lacked postmeiotic spermatids . To determine the stages at which Ikbkap deficiency causes germ cells perturbation , we examined the first round of spermatogenesis using juvenile testes . Immunostaining with the meiocyte maker SYCP3 revealed no obvious histological change in CKO testes at P14 ( Figure 2C ) , suggesting that CKO germ cells enter meiosis and progress to the prophase I normally as in testes of control mice . However , at P21 , while most of the tubules of control testes have postmeiotic round spermatids , CKO testes have almost no postmeiotic cells with SYCP3-expressing meiocytes disorganized and scattered through seminiferous tubules ( Figure 2C ) . At P35 , while control testes showed a full spectrum of spermatogenic cells , including primary spermatocytes , rounds spermatids , and spermatozoa , no post-meiotic cells , such as round and elongated spermatids , were found in the CKO testes ( Figure 2C and Figure S3A ) . These results suggest that Ikbkap deletion causes spermatogenesis arrest at meiotic prophase ( Figure 2C ) . Consistent with the lack of post-meiotic germ cells in the mutant testes , Terminal deoxynucleotidyl transferase dUTP nick end labeling ( TUNEL ) assays revealed an increase in the number of apoptotic cells in the CKO testes compared to that of the controls ( Figure 2D; p<0 . 001 ) , indicating that apoptosis at least partly explains the lack of round and elongated sperms in the CKO testes . Consistent with the absence of IKAP in somatic cells , no morphological change in Sertoli cells or Leydig cells was observed in CKO testes , and the density of GATA1-positive Sertoli cells and 3β-HSD-positive Leydig cells was also not altered in response to Ikbkap deletion ( Figure S3B ) . Taken together , the above results demonstrate that Ikbkap deletion impedes spermatogenesis during meiotic stage , resulting in increased apoptosis . The lack of post-meiotic germ cells in the CKO testes prompted us to examine the impact of Ikbkap deletion on meiotic process . The meiotic prophase is divided into five stages , including leptotene , zygotene , pachytene , diplotene , and diakinesis [2] . We first examined the stage distribution of spermatocytes in control and CKO testes based on chromosomal morphology and sex body status . In the control P21 testes , pachytene spermatocytes are the most abundant , accounting for half of the total cell population , followed by diplotene , zygotene and leptotene stage cells ( Figure 3A ) . In contrast , the CKO testes exhibited an accumulation of zygotene spermatocytes , and a significant decrease of pachytene spermatocytes ( Figure 3A , p<0 . 01 ) , suggesting that the impairment of meiotic progression takes place between zygotene to pachytene stage . To characterize the observed meiotic defect in detail , we performed co-immunostaining of meiotic chromosome spreads using antibodies against axial/lateral ( SYCP3 ) and central ( SYCP1 ) elements of the synaptonemal complex . In normal meiosis , the axial element of synaptonemal complex starts to form at the leptotene stage ( Figure S4 ) . Synapsis is initiated at the zygotene stage , as determined by the appearance of SYCP1- a marker of fully synapsed chromosome segments ( Figure 3B ) . Synapsis is completed at the pachytene stage as chromosome cores contained 20 fully synapsed bivalents , including XY pair synapsed at the pseudoautosomal region ( PAR ) . Despite normal development of axial elements , CKO spermatocytes exhibited an increase in the unpaired SC at zygotene stage . Zygotene-like nuclei , with approximately 40 or more short fragmented stretches of SYCP3 and no SYCP1 staining were observed in 61 . 3% ( n = 60 ) of CKO zygotene spermatocytes ( Figure 3B ) , suggesting a synapsis defect . To further analyze the synaptic defects , we investigated the centromere distribution by immunostaining with centromere marker CREST and SYCP3 . Prior to synapsis , 40 centromeres are usually observed in the control leptotene spermatocytes . As the synapsis progresses , the number of visible centromeres reduces and becomes 21 centromeric foci ( 19 from synapsed autosomes and 2 from the XY bivalent ) at the pachytene stage ( Figure 3C ) . In the CKO zygotene-like spermatocytes , we observed greater than 20 centromeric foci , most containing 40 CREST foci ( Figure 3C ) , indicating CKO spermatocytes failed to complete homologous chromosome pairing . Although 27 . 7% of CKO spermatocytes proceed to pachytene stage and exhibit 19 fully synapsed autosomal bivalent chromosomes ( Figure 3A and 3B ) , XY asynapsis , in which X and Y axes were not associated , were frequently observed in CKO spermatocytes ( 67 . 5% , n = 225 ) , as judged by the absence of SYCP1 ( Figure 3B and 3D ) . Taken together , Ikbkap deficiency in mouse spermatocytes leads to the disruption of synapsis . To further characterize the XY synapsis defect , we stained spermatocytes for γH2AX ( a phosphorylated form of histone H2AX ) , a marker of DSBs , which are abundant in the silenced sex body . At early stage of prophase I , phosphorylation of H2AX is induced by SPO11-catalyzed DSBs in meiotic DNA [5] . γH2AX exhibits a diffuse staining pattern during the leptotene and zygotene stages , and becomes exclusively localized on the sex chromosomes ( so called sex body ) within pachytene and diplotene spermatocytes [29] , [30] . We observed slightly decrease of γH2AX staining in CKO leptotene and zygotene spermatocytes as compared to the control , indicating inefficient initiation of DSB in the CKO testes ( Figure S4 ) . At pachytene stage , γH2AX localization was only restricted to the sex body , but not autosomes in the control spermatocytes ( Figure 4A ) . Although sex bodies are formed in the CKO pachytene spermatocytes , the γH2AX signals were more concentrated in chromatin surrounding the XY axes rather than in more distant region of chromatin ( Figure 4A ) . Moreover , not only weak γH2AX foci were persistent abnormally in the pachytene stage of CKO chromosome ( Figure 4A , arrows ) , but more than one localized γH2AX signals were frequently observed in CKO spermatocytes ( Figure S5 ) . We further confirmed that the axes of the autosomes in CKO spermatocytes were indeed covered by γH2AX cloud using confocal microscopy ( Figure S5 ) . These results suggest an accumulation of unrepaired DSBs . Next , we analyzed sex chromosome-specific synaptic defects in details , and found that 77% of mutant spermatocytes exhibited distinct dissociation of X and Y axis ( Type I in Figure 4A ) , whereas 10% represented illegitimate association of an end of X axis to autosomes ( Type II in Figure 4A ) , and 13% displayed persistent γH2AX signals along the autosomes multiple chromosome within sex body ( Type III in Figure 4A ) ( distribution of the three types is presented in Figure 4B ) . Taken together , our results indicate that Ikbkap is essential for both autosomal and XY synapsis as well as DSB repair . The persistence of γH2AX foci in CKO spermatocytes prompted us to investigate a possible DSB repair defect . In normal meiotic recombination , SPO11-induced DSBs are repaired by the eukaryotic RecA homologs RAD51 and DMC1 ( meiosis specific ) , which catalyze the invasion and strand exchange reaction between non-sister chromatids on homologous chromosomes [31] . To this end , we characterized the distribution of the RAD51 and DMC1 recombinases by immunostaining with an anti-RAD51 antibody that recognizes both proteins . In normal meiosis , RAD51 formed numerous foci at leptotene and zygotene stage , but these foci disappeared from autosomal axes and remained only at unsynapsed region like X and Y axis during pachytene stage [32] , [33] . Consistent with a decreased γH2AX staining pattern in the CKO spermatocytes , the number of RAD51/DMC1 foci was significantly decreased in the CKO leptotene and zygotene spermatocytes as compared to the control ( Figure S6 ) . These results indicate a defect in early meiotic recombination . Moreover , while RAD51/DMC1 foci were detected in sex chromosome of the control pachytene spermatocytes ( Figure 4D ) , they remained not only on the X axis , but also in autosomes in CKO pachytene spermatocytes . These results suggest an impairment in DSB repair in Ikbkap mutant spermatocytes . We next asked whether meiotic process can past the mid-pachytene stage in CKO spermatocytes by staining for Histone 1t ( H1t ) , a mid-pachytene marker . Both control and the remaining CKO spermatocytes at mid/late pachytene stage expressed H1t ( Figure 4C ) , suggesting that the remaining CKO spermatocytes progressed to mid-pachytene stage . To examine whether the defect in early recombination events led to the development of reciprocal exchanges ( crossovers ) between homologous chromosomes , we examined the distribution of mismatch repair protein MLH1 , which marks the locations of crossovers [34] . Mid-pachytene control spermatocytes had 1–2 MLH1 foci on each synapsed chromosome and ∼23 per nucleus ( Figure 4C ) . In contrast , a decreased number of MLH1 foci per nucleus were observed in CKO spermatocytes ( Figure 4C ) , suggesting a defect in crossover formation in CKO spermatocytes . We next sought to investigate the underlying mechanism by which Ikbkap deficiency causes spermatogenic arrest in pachytene stage . Depletion of IKAP in human cells has been previously liked to transcriptional and cell migration defects [35] . To identify potential Ikbkap-regulated genes in meiosis , we performed microarray analysis using RNAs purified from control and CKO P15 testis . We chose to use P15 testis because the first wave of spermatogenesis progresses to the pachytene stage around this time . We observed that the levels of 1810 transcripts were significantly altered ( paired t-test , P<0 . 05 ) . However , the changes are all less than 2-fold , as indicated by scatter plot analysis ( Figure 5A ) . Among the altered transcripts , 1103 were down-regulated , as illustrated by the heat map ( Figure 5B ) . Gene ontology ( GO ) analysis revealed that the affected genes that are most enriched are involved in cell cycle and M phase processes ( P value = 10−14∼10−15 ) . Other terms with a significant P value ( <10−4 ) include meiosis , DNA repair , spermatogenesis and male gamete formation ( Figure 5C ) . By comparing Ikbkap-affected genes with a list of genes that were previously demonstrated to be required for synapsis , we identified Spo11 , a type II like topoisomerase ( including α and β isoforms ) , Rad18 ( ubiquitin ligase ) , and subunits of cohesion , including Smc1β , Rec8 and Stag3 . RT-qPCR analysis confirmed their down-regulation in CKO testes ( Figure 5D ) . In addition , we verified the down-regulation of several spermatogenesis relevant genes including the boule-like ( BOLL ) , and Tudor domain containing 1 ( Tdrd1 ) ( Figure 5D ) . Interestingly , Spo11 , Smc1β , Rec8 and Rad18 are known to play a role in meiotic DSBs repair . Given the phenotypical similarity between Ikbkap , Spo11 [36] , [37] , Smc1β [38] , Rec8 [39] , [40] and Rad18 mutants [41] , we believe that down-regulation of Spo11 , Smc1β , Rec8 and Rad18 at least partly contribute to the Ikbkap CKO phenotype . In mammals , heterologous unsynapsed chromatins ( sex chromosomes ) are transcriptionally silenced during meiosis , a phenomenon called “meiotic sex chromosome inactivation” ( MSCI ) [42] . Failure in MSCI leads to apoptosis of pachytene spermatocytes , which has been proposed to be the reason for the elimination of the spermatocytes of asynaptic mutants , such as Spo11 or Dmc1 [43] , [44] . Given the sex chromosome synapsis defect exhibited in the Ikbkap mutant , we asked whether it affects MSCI . At P15 , when MSCI is established in normal meiosis , genes on the sex chromosomes were not repressed and were significantly up-regulated in CKO testes as compared with control testes ( Kolmogorov-Smirnov test , P<0 . 05 ) ( Figure 6A ) . We furthered confirmed the sex chromosome specific up-regulation in CKO testes as compared to autosomes ( Figure 6A ) . To further validate the results , we analyzed the gene expression level of selected X- , Y- , and autosome-linked genes by RT-qPCR . Among the three Y-linked genes ( Zfy1 , Zfy2 , Ube1y1 ) analyzed , Zfy2 was significantly up-regulated in CKO testes ( Figure 6B ) . We also analyzed four X-linked genes that are expressed in meiotic and postmeiotic cells ( Ccnb3 , Nxt2 ) , or in premeiotic cells but repressed in meiotic and postmeiotic cells ( Tex16 , Hprt ) [45] . In addition to few autosomal genes that we examined in Figure 5D , we also examined additional five autosomal genes that are expressed in meiotic cells ( Syce1 , Syce2 , Sycp1 , Sycp3 and Tex12 ) . While the expression of the autosomal genes was either not altered or down-regulated , the X-linked genes Ccnb3 , Tex16 , and Hprt showed significant increase in CKO testes ( Figure 6B ) . Taken together , only X- and Y-linked genes , which are normally repressed during prophase I in male germline , showed increased expression in CKO testes , suggesting that Ikbkap is important for MSCI . Elongator has been shown to directly interact with tRNA in vitro [11] , [18] and is required for wobble uridine tRNA modification in yeast , plant and worm [18] , [22] , [23] , [24] . To determine whether this function is conserved in mouse , we asked whether wobble uridine tRNA modification is affected by Ikbkap deletion . To test this possibility , total tRNA was extracted from P15 testis and subjected to nucleotide digestion before LC-MS-MS analysis . We used synthetic nucleoside standards to determine the retention time and nucleoside-to-base ion transition . Similar to S . cerevisiae , S . pombe and C . elegans tRNAs , and in accordance with previous studies [46] , we found that mouse tRNA contain mcm5U , ncm5U , and mcm5s2U ( Figure 7A and 7B ) . Importantly , the levels of mcm5U , ncm5U , and mcm5s2U in tRNA are significantly reduced in the CKO testes ( Figure 7B ) . Quantification indicates that mcm5U , mcm5s2U , and ncm5U levels in the tRNAs of the CKO testes are only about 33% , 37% , and 47% that of the control levels , respectively ( Figure 7C ) . Similar results were obtained from tRNAs isolated from P21 or 2 month-old testes ( data not shown ) . tRNA from Elongator mutants of budding yeasts showed accumulation of 2-thio uridine ( s2U ) , which is absent in wild-type tRNA , probably reflecting the thiolation of unmodified wobble uridine [18] . Indeed , we found correspondingly that , while s2U was readily detectable in the tRNAs of the CKO testes , it was not detectable in control testes ( Figure 7B ) . This result suggests that IKAP deficiency causes accumulation of unmodified wobble uridine , some of which is thiolated into s2U . Taken together , our result suggests that , similar to yeast Elp1p and C . elegans ELPC-1 , mouse IKAP is responsible for early steps of mcm5s2U , and ncm5U modification of tRNAs . The incomplete elimination of the formation of wobble uridine modification in CKO testes could be due to the incomplete deletion of Ikbkap and/or possible compensation by other pathways .
Key events in meiotic prophase I include: ( 1 ) introduction of SPO11-dependent double-stranded breaks ( DSBs ) , ( 2 ) synapsing of the homologous chromosomes , ( 3 ) meiotic sex chromosome inactivation ( MSCI ) , and ( 4 ) repair of DSBs by homologous recombination [2] . Ikbkap- deficient spermatocytes arrest at pachytene stage and show various meiotic phenotypes , including aberrant homologous and sex chromosomal synapsis , accumulation of unrepaired DSBs , lack of crossing over , as well as defective MSCI . Defects in synapsis and DSB repair are observed in CKO spermatocytes , suggesting that Ikbkap plays a role in these processes . One of the possible causes of meiotic arrest in the CKO spermatocytes could be activation of a pachytene checkpoint . Spermatocytes with defects in chromosome synapsis and/or recombination commonly trigger pachytene checkpoint control that can delay or arrest meiosis at the pachytene stage of prophase I [47] . However , the accumulation of mid-pachytene marker H1t suggests that the remaining CKO spermatocytes transits past mid-pachytene stage , which is later than pachytene checkpoint ( Figure 4C ) . Another plausible explanation for spermatocyte elimination could be defective MSCI . MSCI is a quality control system unique to spermatocytes , and malfunction of MSCI is sufficient to trigger apoptosis of the pachytene spermatocytes [48] , [49] . Indeed , we observed up-regulation of transcripts from the sex chromosomes in the CKO spermatocytes , suggesting a deficiency in MSCI . In particular , Zfy2 expression was significantly up-regulated ( Figure 6B ) . Zfy1/2 paralogs are thought to be stage IV killer genes as ectopic expression of Zfy1/2 in XY males is sufficient to phenocopy the pachytene arrest phenotype [43] . Such spermatocytes undergo apoptosis and are eliminated at stage IV of the testicular epithelial cycle . Taken together , our results suggest that loss of Ikbkap in germ cells likely triggers a pachytene checkpoint , which together with defective MSCI leads to spermatocyte arrest and apoptosis . Another question raised in our study was how Elongator contributes to meiotic defects in male germ cells . Our study showed that the expression of major meiotic genes involved in synapsis , including Spo11 ( inclusive of α and β isoforms ) , Rad18 , Smc1β , Rec8 and Stag3 are down-regulated in P15 juvenile testes . Among them , Smc1β , Rec8 and Stag3 belongs to the cohesin complex which provides sister chromatid cohesion and ensures chromosome segregation in mitosis and meiosis [50] . In mammalian germ cells , meiotic-specific cohesin complex contains four evolutionarily conserved protein subunits: two SMC ( structural maintenance of chromosomes ) proteins , SMC1β and SMC3 , which heterodimerize , and two non-SMC subunits , REC8 and STAG3 [50] . They form a ring-shaped structure which embraces sister chromatids [51] . Knockout mouse models for SMC1β [38] and REC8 [39] , [40] have been developed . While male meiosis of Smc1β-deficient mice is blocked in pachytene stage , Rec8-deficient spermatocytes could not proceed to pachytene . Interestingly , both Smc1β and Rec8-deficient mice show severe defects in synapsis , recombination , as well as crossover [38] , [39] , [40] , which phenocopy Ikbkap meiotic phenotypes . In addition to cohesion complex , SPO11 , which introduces DSBs during meiotic prophase , was also down-regulated in CKO spermatocytes . Given that Spo11 deficiency results in failure in the initiation of meiotic recombination [36] , [37] , inefficient generation of DSBs in CKO leptotene/zygotene spermatocytes might result from the down-regulation of Spo11 . Furthermore , Spo11α , one of major Spo11 isoforms , and Rad18 , are important for XY pairing . In contrast to high expression of Spo11β in the early prophase , Spo11α , a smaller isoform of Spo11 , is highly expressed in mid- to late prophase [52] . Importantly , the XY pairing takes place later in meiotic prophase than autosomal pairing . In fact , mice that lack Spo11α exhibit abnormal synapsis in sex chromosomes while autosomal homologous pairing and synapsis are normal , suggesting that Spo11α plays a role in XY synapsis [53] . RAD18 , an E3 ubiquitin ligase , has an essential function in the repair of meiotic DSBs and loss of function of RAD18 also results in XY asynapsis [41] . Therefore , it is likely that down-regulation of Smc1β , Rec8 , Stag3 , Spo11 , and Rec18 is at least partly responsible for the CKO phenotype . Whether Ikbkap directly regulates these genes in the male germ cells by facilitating their transcriptional elongation remains to be determined . Despite a predominant cytoplasmic location , a few studies have reported that in certain organisms , some subunits of the Elongator can localize to the nucleus [14] , [15] , [54] , [55] , [56] , [57] . Moreover , studies in human cells have demonstrated that Elongator is preferentially recruited to the open reading frames of a number of genes [35] , supporting a role for Ikbkap in transcription . Our results also suggest that Ikbkap positively regulates critical genes involved in synapsis and autosomal DSB repair . Due to the lack of chromatin immunoprecipitation ( ChIP ) -grade IKAP antibodies , we were unable to address whether IKAP directly contributes to transcription regulation by performing ChIP assays . Thus , we cannot exclude the possibility that IKAP contributes to transcription indirectly . Accumulating evidence indicate that Elongator has an important role in tRNA modification , which has been well-documented in several model systems , including yeasts , nematode , and plants [18] , [22] , [23] , [24] . However , it remains unknown whether this function of the Elongator is conserved to mammals . In this study , we present the first evidence demonstrating that Elongator complex is required for the formation of mcm5 and ncm5 side chains at wobble uridines of tRNA in mammalian cells , supporting its conserved function in all eukaryotes . The conservation of this function raises the question of whether the Elongator complex itself is directly involved in the wobble uridine tRNA modification . One previous study has shown that the S-adenosyl-methionion ( SAM ) binding domain present in Elp3 is able to transfer methyl groups to RNAs [58] . In addition , mutations in the conserved residues of the histone acetyl transferase ( HAT ) domains of Elp3 also affect tRNA-modifying activity [18] . Therefore , Elp3 appears to harbor at least two enzymatic activities . Interestingly , recent studies on the crystal structure of the Elp4–6 sub-complex have revealed its potential role in substrate recognition and tRNA modification [11] , [59] . Elp4 , Elp5 , and Elp6 all share the same RecA-like protein fold , and Elp4/5/6 forms a hetero-hexameric conformation resembling hexameric RecA-like ATPase [11] . The ring-like structure of the sub-complex together with the hydrolysis of ATP are essential for its binding to the anti-codon stem-loops of tRNA as mutations in the homologous nucleic acid binding loop ( L2 ) of Elp6 resulted in the loss of tRNA binding capacity [11] . Thus , it is possible that removal of IKAP may affect the integrity of the complex and thereby affecting its function in tRNA modification . This possibility is supported by studies in yeast and human cells demonstrating that deletion of Ikbkap leads to the loss of Elp3 as well as the integrity of the Elongator [35] , [60] . Further structural and enzymological analyses of the Elp1–3 sub-complex and the Elongator holo-complex will help clarify the mechanism by which Elongator contributes to tRNA modification . Interestingly , phenotypes observed in Elongator mutants , including those in RNAPII transcription and exocytosis , could be suppressed by overexpression of two tRNAs ( tRNALysUUU and tRNAGlnUUG ) in budding yeast [21] , suggesting that the phenotypes are caused by lack of mcm5s2U modification on certain tRNAs . Therefore , it is likely that the primary effect of Ikbkap deficiency in germ cells is caused by tRNA modification defect , rather than dysregulation on transcription . Mutations in the human Ikbkap genes have been shown to cause familial dysautonomia ( FD ) ( also known as Riley-Day syndrome ) . FD is an autosomal recessive disease characterized by defects in the development and maintenance of autonomic and sensory nervous system [61] , [62] . FD has been mainly associated with a single nucleotide substitution in the splice site of intron 20 of the Ikbkap gene , which ultimately leads to decreased expression of IKAP in a tissue-specific manner . Dietrich et al . has generated mice harboring exon 20 deletion allele ( IkbkapΔ20 ) which phenocopy Ikbkap null mutations [26] . The mutants display severe cardiovascular phenotypes and die at E10 [26] . To circumvent the embryonic lethality of Ikbkap mutants , they further generated Ikbkap flox/flox mice with exon 20 floxed ( referred to as Ikbkap floxE20/floxE20 hereafter ) . In contrast to our Ikbkap flox/flox mice , which are viable and normal , Ikbkap floxE20/floxE20 mice display low body weight , and skeletal and neuronal abnormalities . Biochemical analyses showed that Ikbkap floxedE20 allele results in severe reduction in expression of full-length IKAP protein [63] . IKAP expression in Ikbkap floxE20/floxE20 and Ikbkap Δ20/floxE20 brains is 10% and 5% that of wild-type mice , respectively . Interestingly , both models recapitulate the major phenotypic and neuropathological features , including optic neuropathy , seizures , ataxia , impaired development and maintenance of sensory and autonomic systems , reduced number of fungiform papillae on the tongue , gastrointestinal dysfunction as well as skeletal abnormalities [63] . Our study suggests that IKAP-mediated tRNA modification may play a role in the pathogenesis of FD . Characterization of a brain-specific Ikbkap knockout model may reveal how IKAP contributes to FD .
A mouse line harboring a FRT-flanked βGeo cassette upstream of loxP-flanked exon 4 of Ikbkap gene was obtained from Knockout Mouse Project ( KOMP ) Repository . Ikbkap flox/flox mice were generated by crossing mice carrying the Ikbkapβ-geo-flox allele to Rosa26R-FLP mice . Vasa-Cre transgenic mice were provided by Dr . Diego H . Castrillon [27] . All mouse strains were maintained in a mixed genetic background ( 129/Sv×C57BL/6 ) and received standard rodent chow . The primer sequences used for genotyping are listed in Table S1 . Experimental animals and studies were approved by the Institutional Animal Care and User Committee ( IACUC ) of University of North Carolina at Chapel Hill . Testis tissues were fixed with 4% paraformaldehyde ( PFA ) , dehydrated , and embedded in paraffin . For histological analysis , sections ( 7 µm ) were stained with hematoxylin and eosin ( H&E ) or periodic acid schiff's ( PAS ) . For immunohistochemistry , deparaffinized sections after antigen retrieval were blocked with 5% donkey serum and a biotin-blocking system ( Dako , http://www . dako . com/ ) . The following antibodies were used: anti-IKAP ( LSBio , https://www . lsbio . com/ ) , anti-SCP1 , anti-SCP3 , anti-DMC1 ( Abcam , http://www . abcam . com/ ) , anti-PLZF , anti-GATA1 , anti-3β-HSD ( Santa Cruz Biotech , http://www . scbt . com/ ) , anti-c-Kit ( Cell Signaling Technology , http://www . cellsignal . com/ ) , anti-γH2AX , anti-RAD51 ( this polyclonal antibody recognize both RAD51 and DMC1 [9] ) ( Millipore , http://www . millipore . com/ ) , anti-MLH1 , anti-Ki67 ( BD Biosciences , http://www . bdbiosciences . com/ ) , anti-Tra98 ( Bio Academia , http://www . bioacademia . co . jp/en/ ) , and anti-H1t ( a gift from M . A . Handel , The Jackson Laboratory ) . Sections were washed with 0 . 1% Triton X-100/phosphate-buffered saline ( PBST ) buffer and incubated with biotinylated secondary antibodies ( Jackson ImmunoResearch , http://www . jacksonimmuno . com/ ) . Signal detection was carried out with the Avidin-Biotin Complex kit ( Vector Laboratories , http://www . vectorlabs . com/ ) or Tyramide Signal Amplification system ( TSA , Invitrogen , http://www . invitrogen . com/ ) . Peroxidase activity was visualized with 3 , 3′-diaminobenzidine ( DAB , Vector Laboratories ) . Nuclear staining was carried out with 4 , 6-diamidino-2-phenylindole ( DAPI; Sigma-Aldrich , http://www . sigmaaldrich . com/ ) , and sections were mounted with fluorescence mounting medium ( Dako ) prior to imaging . Images were captured with a Zeiss Axiophoto fluorescence microscope or a Zeiss laser-scanning confocal microscope with a spinning disk ( CSU-10 , Yokogawa ) . TUNEL assays were performed on paraffin-embedded tissue sections using In Situ Cell Death Detection Kit ( Roche , http://www . rocheusa . com/ ) , following the manufacturer's instruction . Testes were removed , decapsulated and shredded by needles in PBS , and the cell suspension was filtered with 100 mm mesh to remove the debris . The suspension was incubated with equal volume of a 2× hypotonic extraction buffer ( 30 mM Tris-HCl pH 8; 5 mM EDTA; 1 . 7% sucrose; 0 . 5% trisodium citrate ) for 7 minutes . After centrifugation , cells were suspended with 100 mM sucrose solution to release hypotonized nuclei . A drop of nuclear suspension was spread onto slides that have been dipped in fixation solution ( 1% paraformaldehyde; 0 . 15% Triton X-100; 3 mM dithiothreitol ( Sigma-Aldrich ) ) . The slides were dried slowly in a humidified chamber for overnight , washed in 0 . 4% Photo-Flo solution ( Kodak , http://www . kodak . com/ ) , and dry again for storage . For immunofluorescent staining , the slides were permeabilized with 0 . 4% Triton X100/PBS for 20 minutes , rinsed with 0 . 1% tween 20/PBS , blocked with 5% donkey serum for 1 hour at room temperature ( RT ) , and then incubated with primary antibodies at an optimized concentration overnight at 4°C . After wash , the slides were incubated with Alexa fluorophore conjugated secondary antibodies ( Invitrogen ) for 1 hour at RT . The standard protocol was followed as described above . The stage of prophase I for each spermatocyte was determined by chromosomal morphology and sex body status . We use SYCP3 and γH2AX staining to visualize the chromosomal changes and XY body , respectively . P15 juvenile testes from control or CKO mice ( n = 3 for each genotype ) were collected and total RNAs were extracted from mouse testes using Trizol ( Invitrogen ) and were cleanup using RNeasy kit ( Qiagen ) . Samples were submitted to the UNC Functional Genomics Core Facility for RNA labeling , amplification , hybridization , and scanning . Samples were applied on Affymetrix Gene 1 . 0 ST assays ( Affymetrix ) , and the procedures were followed according to the manufacturer's instructions . Data were analyzed and the expression patterns were presented as a scatter plot using GeneSpring GX software ( Agilent Technologies ) . Total RNAs were extracted from mouse testes using Trizol ( Invitrogen ) and were cleanup using RNeasy kit ( Qiagen ) . The RNAs were treated with DNase I and first-strand cDNA were synthesized by SuperScriptIII reverse transcriptase using random hexanucleotide primers according to the manufacturer's instructions ( Invitrogen ) . Quantitative RT-PCR analyses were carried out using the ViiA7 Real-Time PCR System ( Applied Biosystems ) and FastStart Universal SYBR Green Master ( Roche Applied Science ) . All expression data were normalized to Gapdh . The primer sequences for RT-qPCR are listed in Table S1 . Mass spectrometric analysis of nucleosides was performed essentially as previously described [46] , [64] . For sample preparation , 1 µg of total tRNA were heat-denatured , hydrolyzed with 90 U of Nuclease S1 ( Sigma ) in Buffer 1 ( 0 . 5 mM ZnSO4 , 14 mM sodium acetate , pH 5 . 2 ) at 37°C for 1 hour ( total volume is 44 . 5 µL ) , followed by the addition of 5 µL 10× Buffer 2 ( 560 mM Tris-Cl , 30 mM NaCl , 10 mM MgCl2 , pH 8 . 3 ) , 0 . 5 µg of phosphodiesterase I ( Worthington ) and 2 U of Calf Intestinal Alkaline Phosphatase ( New England Biolabs ) for an additional 1 hour ( final volume 50 µL ) . The digested DNA was then filtered with Nanosep3K ( Pall Corporation ) , and 10 µL of filtered samples were subjected to LC-MS/MS analysis using an UPLC ( Waters ) coupled to a TSQ-Quantum Ultra triple-quadrupole mass analyzer ( ThermoFinnigan ) using heat assisted electrospray ionization ( HESI ) in positive mode ( spray voltage of 3000 V , API temperature of 250°C , sheath gas flow rate 35 arb , AUX gas flow rate 25 arb , capillary temperature of 285°C ) . Liquid chromatography ( LC ) was performed with a 2 . 1×100 mm HSS T3 1 . 8 µm column ( Waters ) with gradient elution at flow rate of 200 µl/min using 0 . 02% acetic acid in water as mobile phase A and methanol as mobile phase B . The gradient was 0→3 . 5 min , 3% B , 3 . 5→12 . 5 min , 3%→16 . 2%B , 12 . 5 →13 min , 16 . 2%B→30%B , 13→15 min , 30%B , 15→16 min , 30%→3%B , 16→20 min , 3%B . The eluent was directed to the mass spectrometer that was running in multiple reaction monitoring ( MRM ) mode , monitoring the transition of m/z 317 . 0 to 153 . 0 ( mcm5U ) , m/z 302 . 0 to 153 . 0 ( ncm5U ) , m/z 333 . 0 to 169 . 0 ( mcm5s2U ) , m/z 261 . 0 to 129 . 0 ( 2-thio-U ) and m/z 245 . 0 to 113 . 0 ( U ) for RNA samples . We investigated reproductive capacities of VasaCre; Ikbkap flox/flox male mice by mating one male with two wild-type females for 3 months . Female mice were checked for vaginal plugs each morning , and the litter sizes were recorded . Results are presented as mean ± SEM . Statistical analysis was carried out by Student's t test . The statistical analysis of boxplot was carried out by Kolmogorov-Smirnov test . P values less than 0 . 05 were considered statistically significant .
|
The process of meiosis is responsible for gamete formation and ensures that offspring will inherit a complete set of chromosomes from each parent . Errors arising during this process generally result in spontaneous abortions , birth defects , or infertility . Many genes that are essential in regulating meiosis have also been implicated in DNA repair . Importantly , defects in DNA repair are common causes of cancers . Therefore , identification of genes important for normal meiosis contributes not only to the field of reproduction but also to the field of cancer biology . We studied the effects of deleting mouse Ikbkap , a gene that encodes one of the subunit of the Elongator complex initially described as an RNA polymerase II–associated transcription elongation factor . We demonstrate that Ikbkap mutant mice exhibit infertility and defects in meiotic progression . Specifically , homologous and sex chromosomes fail to synapse ( become associated ) , DNA double-strand breaks are inefficiently repaired , and DNA crossovers are significantly decreased in Ikbkap males . We also demonstrate that the requirement for Elongator in tRNA modification , which has been shown in lower eukaryotes , is conserved in mammals . Our findings suggest novel roles for Ikbkap in meiosis progression and tRNA modification , which have not been reported previously .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"meiosis",
"chromosome",
"biology",
"genetics",
"epigenetics",
"biology",
"genomics"
] |
2013
|
Ikbkap/Elp1 Deficiency Causes Male Infertility by Disrupting Meiotic Progression
|
Protein-DNA interactions play important roles in regulations of many vital cellular processes , including transcription , translation , DNA replication and recombination . Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function , potentially leading to diseases . Developing a mechanistic understanding of impacts of variants on protein-DNA interactions becomes a persistent need . To address this need we introduce a new computational method PremPDI that predicts the effect of single missense mutation in the protein on the protein-DNA interaction and calculates the quantitative binding affinity change . The PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms with parameters optimized on experimental sets of 219 mutations from 49 protein-DNA complexes . PremPDI yields a very good agreement between predicted and experimental values with Pearson correlation coefficient of 0 . 71 and root-mean-square error of 0 . 86 kcal mol-1 . The PremPDI server could map mutations on a structural protein-DNA complex , calculate the associated changes in binding affinity , determine the deleterious effect of a mutation , and produce a mutant structural model for download . PremPDI can be applied to many tasks , such as determination of potential damaging mutations in cancer and other diseases . PremPDI is available at http://lilab . jysw . suda . edu . cn/research/PremPDI/ .
There has been a rapid development of genome-wide techniques in the last decade along with significant lowering of the cost of gene sequencing , which generated widely available genomic data . However , the interpretation of genomic data and prediction of the association of genetic variations with diseases and phenotypes still require significant improvement [1] . Crucial prerequisite for proper biological function is a protein’s ability to establish highly selective interactions with macromolecular partners . Protein-DNA interactions play important roles in regulations of many vital cellular processes , including transcription , translation , DNA replication , repair and recombination . Sequence variants occurring in these DNA binding proteins that alter protein-DNA interactions may cause significant perturbations or complete abolishment of function , potentially leading to many diseases , such as cancer and heart diseases [2–4] . One possible way to assess the effect of a mutation on protein-DNA interaction is to experimentally measure the binding affinity change . However , while site-directed mutagenesis methods are inexpensive and fast , surface plasmon resonance [5] , isothermal titration calorimetry [6] , FRET [7] and other methods used to measure binding affinity can be time-consuming and costly . Therefore , the development of reliable computational approaches to predict the effects of missense mutations on proteins and their complexes would give us important clues for identifying functionally important missense mutations , understanding the molecular mechanisms of diseases and facilitating their treatment and prevention . With recent rapid advances in computational biology , many approaches have been developed to offer a phenotypic classification of mutations into damaging and neutral categories [8–10] , to calculate the impact of mutations on protein stability [11–13] and protein-protein interactions [14–18] . Previously , we developed two methods for predicting the effect of single mutation on protein-protein binding affinity change . One used modified MM/PBSA , statistical scoring energy functions and structure minimization protocol with explicit solvent model [17] . The other updated method of MutaBind [14] , which combined additional features and used a 100-step energy minimization in the gas phase that considerably increases the prediction accuracy and calculation speed . Our method was applied to predict the effects of cancer mutations on the binding between CBL ubiquitin ligase and E2 conjugating enzyme , where predicted binding affinity changes were successfully compared with the experiments using cancer and non-cancer cell lines [19] . However , very few methods can predict the effects of mutations on protein-DNA binding affinity [20] . Very recently , two prediction methods with servers , mCSM-NA [21] and SAMPDI [22] , were proposed for performing this task . mCSM-NA relies on graph-based signatures and can predict the effect of single mutation on protein-DNA and protein-RNA binding , while SAMPDI combines modified MM/PBSA based energy terms with additional knowledge-based terms for predicting the protein-DNA binding affinity change upon single mutation . As we know , machine learning methods that use different features and training sets may produce different performances on diverse mutations and complexes[23] . Therefore , more fast and accurate computational methods need to be developed for increasing the range of applications on different kinds of complexes and mutations and explaining the mechanisms , such as the molecular mechanisms of disease progression caused by mutations . To address this need we present a new computational method and webserver , PremPDI ( http://lilab . jysw . suda . edu . cn/research/PremPDI/ ) which is based on molecular mechanics force fields and fast side-chain optimization algorithms . PremPDI can evaluate the effects of sequence variants and disease mutations ( both interfacial and non-interfacial mutations ) on protein-DNA interactions; calculate the quantitative change in binding affinity upon single mutation; assess deleterious effects and produce models of mutant complexes . PremPDI is validated using different types of cross-validation and is compared with two other methods using a variety of training and test sets . PremPDI can be applied to many tasks , including finding potential driver missense mutations in cancer , investigating the effects of sequence variations on protein fitness in evolution and protein design .
ProNIT database [24] includes experimentally measured values of changes in binding free energies upon single and multiple amino acid substitutions ( called “mutations” hereafter ) derived from the scientific literatures for protein-nucleic acid complexes with experimentally determined structures . dbAMEPNI database [25] , being developed recently , focuses on the effects of single alanine-scanning mutations on the experimentally measured binding affinities between protein and nucleic acid . It comprises a total of 577 mutations with quantitatively characterized thermodynamic effects , among of them 345 were taken from ProNIT database . Both databases were used for compiling the dataset for parameterization of PremPDI . The following criteria were applied in constructing our dataset: removal complexes without wild-type protein structures or with modified residues or nucleotides at the binding interface of protein-DNA; removal mutations for their mutated sites with missing coordinates in the corresponding wild-type complex structures; eliminating ProNIT entries with multiple mutations restricting our set to single mutations . Furthermore , to avoid the inconsistency between nucleic acids used for measuring binding affinity and those for developing prediction model based on complex 3D structures , we carried out the comparison of sequence similarity between the nucleic acids of binding sites observed in the protein-DNA structures and the sequences used in the corresponding experiments . Then the entries with high sequence similarity ( 80% ) for the nucleic acids in the binding interface were kept . ProNIT database includes the sequences of DNA used for measuring binding affinity , while dbAMEPNI database does not . So , we manually compiled them from the corresponding references . There are some entries where several experimental values are available for the same mutation . For these cases that are not drastically different from each other , we used an average value of experimental changes in binding free energy . In addition , 20 mutations from five protein-DNA complexes abstracted from SAMPDI training set [22] were also included in our dataset . As a result , the experimental set used in this study includes 219 single mutations from 49 wild-type protein-DNA complexes ( it will be referred to as “Prempdi” ) ( S1 Table ) . Only 105 mutations obtained from ProNIT database have the information of experimental pH . Thus , we chose the experimental pH to be neutral assuming that at neutral pH the ionizable residues have default charged states . The number of mutations for each protein-DNA complex is shown in S1 Fig We also compared our dataset with the training datasets used for developing SAMPDI and mCSM methods , and the details are shown in S1 Table . Crystal or NMR structures of wild-type protein-DNA complexes were obtained from the Protein Data Bank ( PDB ) [26] , and biological assembly 1 of crystal structure or the first model of NMR was used as the initial structure . First we introduced a single mutation on the wild-type Protein-DNA complex structure using BuildModel module from FoldX [27] software package . Missing heavy side chain atoms and hydrogen atoms were added for the wild type and mutant using VMD program [28] based on the topology file from the CHARMM36 force field [29] . Then a 100-step energy minimization in the gas phase was carried out for both wild type and mutant using harmonic restraints ( with the force constant of 5 kcal mol-1 Å-2 ) applied on the backbone atoms of all residues . Minimization was done only for protein-DNA complexes , and protein or nucleic acid structures of binding partners were retained assuming the rigid-body binding . The energy minimization was carried out with NAMD program version 2 . 12 [30] using the CHARMM36 force field [29] . A 12 Å cutoff distance for nonbonded interactions was applied to the systems . Lengths of hydrogen-containing bonds were constrained by the SHAKE algorithm [31] . The current structure optimization protocol was chosen based on its highest accuracy and speed . The performances for other structure optimization protocols that have been tried are shown in S2 Table . The minimized structures of wild-type and mutant complexes were used for the calculation of energy terms . Our goal is to design a method to assess the effects of mutations on protein-DNA binding . Mutations can affect binding in different ways [32] . They may change the components of protein-DNA interaction energies , may affect the solvation of a complex , may change the hydrogen-bond network and may directly disrupt binding hotspot sites [33] . Besides , the interactions between protein and the two types of nucleic acids ( DNA and RNA ) are also different , which was validated by a detailed computational comparison at the atomic contact level [34] . Here , through analysis of different kinds of protein sequence and structural features ( S3 Table shows all features considered in our model selection ) , we found that nine features contributed significantly to the quality of multiple linear regression model ( MLR ) for the calculation of ΔΔG value ( change in binding affinity upon mutation ) affecting protein-DNA interactions ( Table 1 ) . The features that contribute significantly to the quality of PremPDI model are described below .
The p-value and contribution of each term to the PremPDI model are shown in Table 1 , and all terms contribute significantly to the energy model with p-values less than 0 . 01 . If we train and test our model on the ‘Prempdi’ set , the Pearson correlation coefficient between experimental and calculated changes in binding free energies is R = 0 . 71 ( Fig 1a and Table 2 ) and the corresponding root-mean-square error ( RMSE ) is 0 . 86 ( Table 2 ) . Among 219 mutations in “Prempdi” dataset , 179 ones belong to alanine-scanning single mutations defined as substitutions of residues into alanine and 134 ones located on the interfaces of protein-DNA complexes according to our definition ( see Method‘ section ) . The results show that our model does not present bias to alanine-scanning mutations and yields good performance for non-alanine-scanning mutations with R = 0 . 64 and RMSE = 0 . 81 ( Table 2 ) . As was shown previously [14 , 17] , mutations located on the interface region present average larger effects on protein-protein interactions and are better predicted compared to non-interface mutations . In this study , PremPDI yields statistically significant correlation ( p-value < 0 . 01 ) in predicting non-interfacial mutations and the correlation reaches value as high as 0 . 69 and RMSE is 0 . 85 . We also tried several other machine learning methods such as random forest , support vector machine and neural network to build our model using these nine features . Cross-validation and leave one complex validation that will be discussed in the next section show that multiple linear regression represents the best performance . In addition , we performed multicollinearity analysis to investigate the linear association across each feature . Pearson correlation matrixes and variance inflation factors ( VIF ) for the energy features in PremPDI are shown in S6 Table . The results show that ΔΔGsolv has relatively strong correlation with ΔNHbondp1-p2 ( R = -0 . 71 ) , SAcom/p2wt has relatively strong correlation with Lmut with R of 0 . 74 , and the rest of the correlations are either small or are not significantly different from zero . The VIFs of all features are less than three representing relatively low multicollinearity . We removed highly correlated features from our energy function that results in decrease of prediction accuracy . For instance , removal ΔNHbondp1-p2 from PremPDI MLR model leads to the decrease of correlation from 0 . 71 to 0 . 68 . Thus , all nine features were kept in our final model to achieve the optimal performance . PremPDI takes about five minutes to perform calculations for a single mutation in a protein-DNA complex with 300 residues and 30 nucleotides running on a single processor core , and it requires additional two-to-three minutes for each additional mutation per complex . Our goal is to construct a computational method that can achieve a high prediction accuracy for large and diverse sets of single mutations . In many cases , overfitting may occur when the parameters of computational methods are tuned to minimize the mean square deviations of predicted from experimental values in the training set , thus leading to the decreased generalized performance [38] . At the same time the training set should be as comprehensive as possible , while in our study the data set used for training and testing is relatively small . To address this issue , we performed three types of cross-validation . In case of “CV1” cross-validation ( Fig 1b ) , 50% mutations selected randomly from “Prempdi” set were used for training and the remaining mutations for testing , the procedure was repeated 50 times . In “CV2” cross-validation we randomly chose 80% of all mutations as training and used the remaining 20% mutations for testing , also repeated 50 times . The average Pearson correlation coefficient is R = 0 . 68 for both “CV1” and “CV2” with small standard error of 0 . 06 ( Fig 1b ) . The RMSE is 0 . 9 kcal mol-1 for both cross validations ( Table 2 ) . Since the prediction accuracy of mutational effects largely depends on sequence and structure of a complex , we performed a “leave-one-complex-out” procedure ( “CV3” cross-validation ) . Namely , we trained the parameters on experimental ΔΔG values of mutations from 48 protein-DNA complexes and then applied the model to mutations from the remaining one complex . This procedure was repeated for each complex . The Pearson correlation coefficient between experimental and computed ΔΔG values using this procedure is R = 0 . 63 with RMSE of 0 . 95 kcal mol-1 ( Fig 1c and Table 2 ) . In addition , for alanine-scanning , non-alanine-scanning , interfacial and non-interfacial mutations , they also present relatively high correlation coefficients and low RMSEs in “CV3” cross-validation , especially for interfacial mutations ( Table 2 ) . We also analyzed the variation of the weighting coefficient for each feature in “CV1” , “CV2” and “CV3” cross-validation respectively . The results are shown in S7 Table . The standard deviations of the weighting coefficients are relatively small even for “CV1” cross-validation , 50% mutations from “Prempdi” set were used for training and the remaining mutations for testing , which indicates the variation is not significant across each fold . In addition , the average weighting coefficients in each cross-validation were compared with the weighting coefficients of the final PremPDI model and the results show that the differences for all energy features are very small . All the validations indicate that our PremPDI model does not overfit on its training set and all features have significant contribution to the energy function . Predicting the quantitative values of binding affinity changes is quite challenging . A much easier task , attempted by many studies , is to classify mutations based on their effects into deleterious or neutral . Several thresholds of experimentally determined ΔΔG , 1 , 1 . 5 , 2 . 0 and 2 . 5 kcal mol-1 , were tested for defining mutations with deleterious ( highly destabilizing ) effects ( see S2 Fig ) . The number of mutations in each category is shown in S2a Fig Threshold of 1 kcal mol-1 has the most balanced dataset . To quantify the performance of PremPDI scores , we performed Receiver Operating Characteristics ( ROC ) and precision-recall analyses . Sensitivity or true positive rate was defined as TPR = TP/ ( TP + FN ) and specificity or true negative rate was defined as TNR = 1-FPR = TN/ ( FP+TN ) . Additionally , in order to account for imbalances in the labeled dataset , the quality of the predictions was described by Matthews correlation coefficient ( MCC ) , a performance measure which is known to be more robust on unbalanced datasets: MCC=TP*TN-FP*FN√ ( TP+FP ) * ( TP+FN ) * ( TN+FP ) * ( TN+FN ) S2b–S2e Fig show the ROC and precision-recall curves by applying PremPDI on the “Prempdi” training/test set using different thresholds . S2f Fig depicts the basic summary of performance metrics , including AUC for ROC and precision-recall curves and MCC . The results show that threshold of 1 . 5 kcal mol-1 has the highest AUC-ROC of 0 . 91 and MCC of 0 . 61 in distinguishing deleterious and neutral mutations ( S2b and S2f Fig ) . Threshold of 1 kcal mol-1 has the highest AUC-PR of 0 . 83 and its AUC-ROC and MCC is 0 . 84 and 0 . 58 respectively ( S2d and S2f Fig ) . S2c and S2e Fig show that threshold of 1 kcal mol-1 classification has the best performance in the deleterious mutation prediction with less than 10% false positive rate and more than 50% precision . Here , we choose ΔΔGexp = 1 kcal mol-1 as the threshold to define deleterious effect , and it is also in agreement with SAMPDI method for classifying large and small effects [22] . Fig 1d shows the ROC curves for PremPDI and PremPDI ( CV3 ) to distinguish deleterious and neutral effects using threshold of 1 kcal mol-1 . Therefore , PremPDI classifies a mutation as deleterious if its predicted ΔΔG is higher or equal to 1 . 10 kcal mol-1 ( S3 Fig ) . This threshold corresponds to 14% FPR and 77% TPR which minimizes the value of error ER= ( 1−TPR ) 2+FPR2 to compensate retrieval sensitivity and specificity . We compared our method with the other two available machine learning methods , mCSM-NA [21] and SAMPDI [22] . mCSM-NA uses graph-based signatures to calculate the changes in protein-nucleic acid binding affinity upon single mutations . SAMPDI uses a combination of modified MM/PBSA based energy terms with additional knowledge-based terms to predict the ΔΔG values of interfacial mutations for protein-DNA complexes . The training sets for parameterizing PremPDI method and the other two have some differences , which is shown in S1 Table . Among 219 mutations from 49 complexes in PremPDI training set ( “Prempdi” ) , 105 mutations from 16 complexes overlap with mCSM-NA training set of “Mcsm” ( the overlapped set is named as “P . O . M” ) and 77 mutations from 11 complexes overlap with SAMPDI training set of “Sampdi” ( the overlapped set is named as “P . O . S” ) . 114 mutations from 33 complexes in “Prempdi” are not included in the “Mcsm” ( named as “P . D . M” ) and 142 mutations from 43 complexes in “Prempdi” are not in the “Sampdi” ( named as “P . D . S” ) . Since SAMPDI is used in particular for interfacial mutations , we created a subset of “P . D . S” and named it as “P . D . S . I” that includes 77 interfacial mutations from 32 complexes . We performed several types of comparisons between our method and the other two using four different test sets . “P . O . M” or “P . O . S” is the test set of overlapped mutations used for developing PremPDI and mCSM or SAMPDI respectively . So , we compared PremPDI with them using the model that built on the whole ‘Prempdi’ dataset . “P . D . M” or “P . D . S . I” test set represents the mutations that are included in the ‘Prempdi’ but not in the ‘Mcsm’ or ‘Sampdi’ . So , to be fair , we used both “leave-one-complex-out” ( CV3 ) results and the model built on the independent ‘P . O . M’ or ‘Prempdi-P . D . S . I’ dataset ( named as PremPDI ( Ind ) ) to compare with the other methods respectively . Pearson correlation coefficients and RMSE between experimental measurements ( ΔΔGexp ) and predictions show that PremPDI presents a similar performance with mCSM-NA method and performs better than SAMPDI in predicting quantitative values of ΔΔG ( Table 3 ) . ROC curves shown in Fig 2 and AUC-ROC , AUC-PR and MCC values presented in Table 3 ( The number of mutations in each category is shown in S4 Fig ) demonstrate that the performance of PremPDI is notable in estimating deleterious effects ( highly destabilizing ) for all test sets and better than mCSM-NA and SAMPDI methods . The main requirement of the webserver is the 3D structure of a protein-DNA complex . The users can either input PDB code of the complex , then structures of either biological assemblies or asymmetric unit will be retrieved from the Protein Data Bank , or they can upload their own file with atomic coordinates . In either case , the structure file should contain at least two chains . After the structure was retrieved correctly , the server will display a 3D view of the complex colored by chains or partners using the GLmol software . Each chain is listed with the corresponding protein or nucleic acid name . At the second step , two interacting partners should be defined . The user can assign one or multiple chains to either Partner 1 or Partner 2 , but both partners should include at least one chain . Here , we restrict Partner 1 to proteins and Partner 2 to DNA and the selected protein/DNA chain will be put into the box of Partner1/Partner2 automatically . Only the selected chains of two partners will be taken into account during the calculation . If the interface size between two partners is more than 100 Å2 , we define them interacting with each other and then perform the calculation . Interface size is calculated as the difference between the solvent accessible surface areas of complex and unbound partners . The third step is to select mutations ( Fig 3 ) . Each mutation will be treated independently and up to 16 single mutations can be selected for one submission . After the chain and the mutated residue are selected , they can be visualized in the wild-type complex using the 3D viewer . For each mutation of a protein-DNA complex , PremPDI server provides the following results: Results can be viewed directly on the browser ( Fig 3 ) or downloaded as a plain text file .
|
Developing methods for accurate prediction of effects of amino acid substitutions on protein-DNA interactions is important for a wide range of biomedical applications such as understanding disease-causing mechanism of missense mutations and guiding protein engineering . Very few methods have been developed for predicting the effects of mutations on protein-DNA binding affinity . Here we report a new computational method , PRedicts the Effects of single Mutations on Protein-DNA Interactions ( PremPDI ) . The core of the PremPDI method is based on molecular mechanics force fields and fast side-chain optimization algorithms that makes the PremPDI algorithm efficient and being fast enough to handle large number of cases . The performance of the PremPDI protocol was tested against experimentally determined binding free energy changes of 219 mutations from 49 protein-DNA complexes and yields very good correlation coefficient . The PremPDI webserver is available to the community at http://lilab . jysw . suda . edu . cn/research/PremPDI/ .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"and",
"discussion"
] |
[
"deletion",
"mutation",
"protein",
"interactions",
"crystal",
"structure",
"condensed",
"matter",
"physics",
"mutation",
"substitution",
"mutation",
"protein",
"structure",
"mutation",
"databases",
"crystallography",
"research",
"and",
"analysis",
"methods",
"sequence",
"analysis",
"solid",
"state",
"physics",
"bioinformatics",
"proteins",
"biological",
"databases",
"molecular",
"biology",
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"protein",
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"comparison",
"biochemistry",
"sequence",
"databases",
"database",
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"genetics",
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"life",
"sciences",
"physical",
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"macromolecular",
"structure",
"analysis"
] |
2018
|
PremPDI estimates and interprets the effects of missense mutations on protein-DNA interactions
|
An accurate and precisely annotated genome assembly is a fundamental requirement for functional genomic analysis . Here , the complete DNA sequence and gene annotation of mouse Chromosome 11 was used to test the efficacy of large-scale sequencing for mutation identification . We re-sequenced the 14 , 000 annotated exons and boundaries from over 900 genes in 41 recessive mutant mouse lines that were isolated in an N-ethyl-N-nitrosourea ( ENU ) mutation screen targeted to mouse Chromosome 11 . Fifty-nine sequence variants were identified in 55 genes from 31 mutant lines . 39% of the lesions lie in coding sequences and create primarily missense mutations . The other 61% lie in noncoding regions , many of them in highly conserved sequences . A lesion in the perinatal lethal line l11Jus13 alters a consensus splice site of nucleoredoxin ( Nxn ) , inserting 10 amino acids into the resulting protein . We conclude that point mutations can be accurately and sensitively recovered by large-scale sequencing , and that conserved noncoding regions should be included for disease mutation identification . Only seven of the candidate genes we report have been previously targeted by mutation in mice or rats , showing that despite ongoing efforts to functionally annotate genes in the mammalian genome , an enormous gap remains between phenotype and function . Our data show that the classical positional mapping approach of disease mutation identification can be extended to large target regions using high-throughput sequencing .
Genome sequences are essential tools for comparative and mutational analyses [1] , [2] . As a part of the Mouse Genome Sequencing Consortium , we sequenced mouse Chromosome 11 in the C57BL/6J mouse strain . The 2002 draft mouse genome reported 8681 gaps in mouse Chromosome 11 , and was estimated to lack 4 . 3 Mb of sequence , despite a good shotgun assembly and a physical map [3] . Using clone-by-clone sequencing of the physical map of mouse Chromosome 11 , we have assembled this chromosome into a near contiguous sequence with the highest quality of any mouse chromosome assembly ( completed sequence is available in the NCBI assembly of the mouse genome at www . ensembl . org ) . The sequence of Chromosome 11 from C57BL/6J now provides a basis for understanding the trait characteristics of this strain , for cross-strain comparative analysis , for identifying modifiers and quantitative trait loci ( QTLs ) , and for the precise design of knockout alleles . Accurate DNA sequence also facilitates the discovery of disease associated genes from forward genetic screens , as well as the engineering of mutations and rearrangements in the mouse to model human diseases . We used the completed sequence to design primers to re-sequence DNA from mutants isolated in a phenotype-driven regional recessive N-ethyl-N-nitrosourea ( ENU ) genetic screen targeted to mouse Chromosome 11 through the use of a balancer chromosome [4] , [5] . ENU primarily induces point mutations , which have been a challenge to fine map and identify [6] . Classical positional cloning involves narrowing the molecular interval by meiotic mapping , followed by candidate gene sequence analysis , which is time consuming and expensive . Further , in many organisms , including Caenorhabditis elegans , Drosophila melanogaster , and Escherichia coli , chemical point mutagens often generate a multitude of linked lesions [7] , which can confound the designation of a candidate gene to the mutant phenotype . In the mouse , genes are dispersed , chromatin contains large stretches of noncoding sequence , and inbred strains with fixed genetic backgrounds are readily available . We reasoned that mutations isolated using the regional screen were ideal to test the efficacy of mutation identification by sequencing after ENU point mutagenesis , because mutations were induced on a C57BL/6J chromosome , and because mutant recovery was targeted to an interval between Trp53 and Wnt3 , restricting the analysis to a 34 megabase ( Mb ) region [5] . Mouse Chromosome 11 and its conserved counterpart human Chromosome 17 are very gene dense , making classical positional cloning efforts unrealistic for large numbers of mutants . Identifying DNA lesions in mutant lines should assign candidate genes , while it defines the numbers and nature of DNA lesions detected after ENU treatment . We illustrate the value of finished sequence by recovering a rich catalogue of candidate genes in ENU-induced mutant lines . No mutations have been reported previously for the majority of the candidate genes . Here , we report for the first time that a splice-site mutation in nucleoredoxin causes craniofacial defects in the perinatal lethal line l11Jus13 .
The sequence of mouse Chromosome 11 ( ∼4 . 6% of the mouse genome ) was generated by sequencing 910 bacterial artificial chromosomes ( BACs ) from a linear physical array [8] , [9] . The clone-by-clone sequencing of this chromosome generated over 4 million raw sequence reads , which were collapsed into 118 . 8 Mb of finished sequence . The current assembly contains three main contiguous sequences of approximately 31 . 4 , 54 . 0 and 33 . 4 Mb separated by two gaps , which were found by fiber-FISH to be 91 and 6kb , respectively ( Table S1 ) . An additional gap represents the acrocentric heterochromatin . Thus , the physical length of the chromosome is estimated to be 121 . 8 Mb . The telomeric half of mouse Chromosome 11 ( Mmu11 ) contains the equivalent of the entire euchromatic region of human Chromosome 17 ( Hsa17 ) ( Figure 1 ) , with the exception of a small number of genes that differ between the two organisms ( Table S2 ) . We previously described a screen for autosomal recessive mouse mutants using the point mutagen ENU , which was targeted to the Mmu11 region most highly conserved with Hsa17 , a 34 Mb interval between Trp53 and Wnt3 ( Figure 1 ) [5] . A large number of recessive mutant lines with a wide range of phenotypes , including craniofacial abnormalities , neurological defects , infertility , impaired growth , and lethality , were isolated in the screen . Each of the mutants was identified by its phenotype after breeding ENU-treated C57BL/6J male mice , and was maintained in trans to a balancer chromosome , which was derived from 129S5/SvEv embryonic stem ( ES ) cells . To identify the DNA lesions responsible for the phenotypes in these lines , we designed primers for the 14 , 000 annotated exons and intron/exon boundaries from all of the protein coding genes in the 34 Mb interval ( Table S3 ) , representing approximately 17 , 000 sequence tags . We then carried out bi-directional sequencing of PCR amplicons from DNA of either a homozygous or heterozygous individual from each of 41 mutant lines , a total of over 14 Mb of sequence covering 7 . 8 Mb of transcribed DNA from each line , which represents approximately one-fourth of the total DNA content of the 34 Mb balancer region . A total of 1727 sequence variants were identified , but most occurred in multiple heterozygous mutant lines and were previously published single nucleotide polymorphisms ( SNPs ) between the C57BL/6J and 129S5/SvEv strains [10] . Eighty-one unique potentially causative base pair changes were identified in the mutant lines . Each lesion was confirmed by re-sequencing of DNA PCR products amplified from 2–4 independent phenotype-true animals and all appropriate controls ( Figure S1 ) . However , five base changes in three mutant lines were confirmed in only a subset of DNAs . This indicates that the ENU-induced base change was present in the original DNA that was sequenced , but did not segregate with the phenotype in other animals and was therefore not causative of the phenotype ( Table S4 ) . The remaining changes included five new SNPs , which may be unique to our 129 substrain , or may not have been previously reported . That these occurred in only one line each is possibly due to the randomness of crossovers in each line . Twelve of the lesions initially identified by sequencing were not confirmed in any mutant animal whose DNA was re-sequenced . The most common DNA base changes identified were AT-GC transitions ( 42 . 2% ) . AT-TA transversions represented 29 . 7% of the bases changes followed by GC-AT ( 10 . 9% ) , GC-TA ( 10 . 9% ) , and AT-CG ( 6 . 3% ) . These data are similar to the most predominant lesions reported for ENU-induced mutations after treatment of mouse spermatogonia [11] . The numbers of confirmed ENU-induced lesions per mutant mouse line fits a Poisson distribution: no lesions were detected in eight mutant lines , one lesion was confirmed in each of seventeen mutant lines , two lesions in seven mutant lines , three lesions in five mutant lines , four lesions in two mutant lines , and five lesions in two mutant lines ( X2 ( 5df ) = 3 . 99; p<0 . 001; see calculations in Materials and Methods ) . This finding indicates that some mutant lines could carry more than one nucleotide variant that either individually or together produce a phenotype . Of the 23 coding base changes identified in 19 different mutant lines , only 4 were synonymous . The non-synonymous base pair changes provide a valuable list of candidate genes for the ENU-induced mutations . The genetic code assists in the interpretation of these lesions , since such base changes cause obvious defects in protein coding sequences . Three of the non-synonymous lesions result in the insertion of a stop codon , which may cause protein truncation or elicit nonsense-mediated decay . One of these mutations , C4228T , which generates Q106X in l11Jus15 , is in a member of the mediator complex that directs transcription , mediator complex 31 ( Med31 ) . Sixteen non-synonymous missense mutations were identified , most of which occur as the sole lesion within the DNA that was sequenced in a line ( Table 1 , Table S4 ) . Notably , we previously reported a missense mutation in the postnatal lethal line l11Jus51 , and this line was re-sequenced as a positive control . The mutation E541V in Slc4a1 was the only confirmed lesion identified in 6 . 27×106 bp sequenced from this line , showing the sensitivity of detection , as well as demonstrating the relatively low frequency of ENU lesions . Two mutant lines , craniofacial08 ( crf08 ) and the lethal line l11Jus52 , contain two missense mutations each . Crf08 has an isoleucine to valine substitution ( I207V ) in olfactory receptor 394 ( Olfr394 ) as well as a lysine to glutamic acid substitution ( K663E ) in mediator complex 13 ( Med13 ) . L11Jus52 has a tyrosine to cysteine substitution ( Y340C ) in the frizzled 2 homolog ( Fzd2 ) and a glutamine to proline substitution ( Q341P ) in the plexin domain containing 1 gene ( Plxdc1 ) . To predict the likelihood that an amino acid change is deleterious , we employed the SNAP ( screening for non-acceptable polymorphisms ) algorithm using default parameters and full-length protein coding sequences [12] . It predicts the neutrality of the mutation , calculates the percent accuracy of this analysis , and provides a reliability index ( Table 1 ) . The SNAP analysis revealed that the I207V transition in Olfr394 is a neutral amino acid change , while K663E in the transcriptional regulator Med13 is non-neutral , suggesting that the mutation causes a functional change . Both of the lesions in l11Jus52 , Y340C in Fzd2 and Q341P in Plxdc1 , are predicted to cause a functional change . Ultimately , confirmation of candidate genes will require transgenic rescue or crosses with additional alleles . Thirty-six of the 59 potentially causative base pair changes were found in non-coding regions of genes , including 25 within introns , 2 in 5′ untranslated ( UTR ) elements , 7 in 3′ UTRs , and 2 downstream of a gene ( Table 1 ) . We did not sequence conserved microRNAs in this project . However , a search of miRNA information databases revealed that none of the ENU-induced mutations lie within known miRNA sequences that may fall within or near genes . Further , no consensus splice sites or start codons were created by the lesions , though one consensus splice site was destroyed . Therefore , the noncoding lesions present more of a challenge to link cause and effect , since most of them occur within introns or 3′UTRs , and may affect gene regulation , splicing , transcript stability , translational efficacy , or may have no effect at all . Because multi-species sequence conservation is often a predictor of function , we compared a 100 base pair mouse sequence surrounding each ENU-induced base change to that of six other vertebrate organisms: human , rat , Rhesus monkey , horse , dog , and chicken . The comparisons produce a number based on the percent identity , which we have arbitrarily designated a “match score” . This analysis showed that the coding region mutations are conserved , as expected . However , it also showed that many of the noncoding mutations are located within highly conserved regions as well ( Figure 2 ) . All of the non-coding regions were chosen for re-sequencing because they lie near exons or are non-coding UTRs , so one may expect them to be well-conserved , similar to the coding regions . However , the coding region lesion match score averaged 493 , whereas the noncoding lesion match score averaged 304 . The 100 bases surrounding the T to A lesion in l11Jus13 within a consensus splice site of nucleoredoxin ( Nxn ) produced a match score of 481 . Both l11Jus05 and crf12 have independent mutations , T to A and C to A , respectively , in the 3′UTR of Med13 ( the same gene with a coding mutation in crf08 ) , which occur in regions that produced match scores of 568 and 561 , respectively ( Figure 2 and Table S5 ) . The regions surrounding these two noncoding lesions had the highest match scores in our conservation analysis . Either of these mutations may cause a change in message stability or translation , perhaps through perturbations of interactions with microRNA . Indeed , an analysis of TargetScan revealed that the lesion in l11Jus05 lies within a seed sequence for the microRNA miR200 [13] , [14] . We examined the lesions in l11Jus13 , a line that carried five confirmed noncoding mutations in the novel Riken Protein RP23-396N4 . 2 , 39S ribosomal protein L27 ( Mrpl27 ) , next to Brca1 gene ( Nbr1 ) , max-like protein X ( Mlx ) , and nucleoredoxin ( Nxn ) . These lesions give match scores in our conservation analysis of 180 , 202 , 260 , 410 , and 481 , respectively . The critical interval for the l11Jus13 perinatal lethal phenotype was narrowed by meiotic mapping to 6 Mb of DNA extending from the SNP rs3702197 to the SNP rs13481117 ( Figure 3A ) . All lesions other than that in Nxn are excluded from this region . The T to A lesion in Nxn occurs two base pairs after exon 6 to alter a consensus splice donor sequence . RT-PCR and sequencing confirmed that the mutation leads to aberrant splicing of the transcript in homozygous l11Jus13 ( NxnJ13/J13 ) embryos . Thirty base pairs of intronic sequence are included in the transcript , predicting an in-frame insertion of 10 amino acids into the protein , which was present in E12 . 5 homozygous mutants at about 30% of wild-type levels ( Figure 3B–3F ) [15] . A null allele of Nxn was obtained from the European Conditional Mouse Mutagenesis Program ( EUCOMM ) , Nxntm1Eucomm/+ ( Nxn+/− ) , and a complementation test was carried out . Thirty-four mice from crosses between Nxn+/− and NxnJ13/+ were examined at weaning and none ( Expected = 8 ) were NxnJ13/− ( p<0 . 001 ) . Ninety-seven percent of the NxnJ13/J13 mutants die perinatally by postnatal day 1 ( P1 ) ( Figure S2 ) [16] . All NxnJ13/J13 embryos had craniofacial dysmorphology ( Figure 4A and 4B ) , and most had cleft palates . Skeletal preparations at E18 . 5 showed that NxnJ13/J13 embryos had a seven percent decrease in mandible length ( p<0 . 001 ) when compared to their control littermates ( Figure 4A and 4B ) . The decrease in bone length was not found in femurs and body mass was not significantly different at this time point , showing that this decrease was not due to a body size difference in the mutants ( Figure S2 ) .
Mouse Chromosome 11 is the first full mouse chromosome to be completely finished . Here , we analyzed the most conserved region between mouse Chromosome 11 and human Chromosome 17 by mutagenesis and sequencing in the largest ENU mutation identification project to date . This chromosome contains 4 . 6% of the total mouse genome sequence , yet contains nearly 10% of the estimated total number of transcribed genes , showing that mouse Chromosome 11 is a gene dense region . Previous data suggested that this region contains more than the average number of essential genes [17] , consistent with a high recovery of mutations in the Chromosome 11 screen [5] . Classical positional cloning efforts would have required many crosses in each mutant line to narrow the critical intervals to a region small enough to re-sequence , which would have been a massive effort for 41 mutant lines . Fifty-nine unique lesions were confirmed in 55 genes in 31 of the mutant lines . The simplest of these to associate with candidate genes are those that alter the coding sequence of a gene , although any of the lesions reported here could be predicted to be causal until proven otherwise . An F453V mutation in Dvl2 , an activator of Wnt signalling , occurred in the growth mutant gro01 . The knockout of Dvl2 dies perinatally of heart defects [18] , so gro01 may be a hypomorphic allele of Dvl2 . Two independent mutations in the Wnt receptor Fzd2 occurred in the mutant lines l11Jus52 ( Y340C ) and l11Jus54 ( C504Y ) . Fzd2 has not been targeted by mutation previously . The aspartoacylase ( Aspa ) mutation in nur07 ( Q193X ) represents a model of human Canavan disease , a progressive disorder of myelination [19] . By far , the majority of ENU-induced base changes that were identified occur in genes that have no known function or for which no mutations have been reported , including many full-length RIKEN cDNA sequences , a plekstrin homology domain protein ( Plekhm1 ) , and the mediator complex components Med13 and Med31 . The causative nature of four ( K22E in Hes7 , E541V in Slc4a1 , Q193X in Aspa , and Q105X in Med31 ) of the candidate gene mutations has been shown by complementation tests or protein studies prior to this publication [5] , [19] ( and data not shown ) , and one in Nxn reported here; however , the causal nature of others remains to be demonstrated . A large proportion of the lesions occurred in noncoding regions . Multi-species conservation analysis was carried out to predict whether these lesions lie in a significantly conserved region ( Figure 4 ) . In the lethal line l11Jus13 , which carries 5 independent noncoding mutations , a splice-site lesion in Nxn , which lies in a highly conserved region , is responsible for the mutant phenotypes . Of note , the five lesions in this line were well-spaced ( at 76 Mb , 94 . 5 Mb , 101 Mb , 101 . 5 Mb , and 104 . 5 Mb ) , and none other than that in Nxn was found in a 6 Mb critical interval that contains over 180 genes . Although the l11Jus13 line had five base changes , it was the exception . Seventeen of the lines had only one base change in the entire 7 . 8 Mb of DNA that was sequenced . Together , our data suggest that in the majority of ENU-induced mouse mutant lines , accessory DNA lesions will not complicate assigning mutations to phenotypes . NxnJ13 homozygous mutants likely die as a consequence of cleft palate , which causes an inability to suckle at birth ( Figure S2 ) . Cleft lip and/or cleft palate is a common birth defect , which can be caused by the tongue protruding into the space where the palate should close during embryogenesis [20] , [21] . Therefore , the cleft palate in NxnJ13 homozygous mutants could be due to a physical failure of the palate to fuse as a consequence of the small mandibles causing a protrusion of the tongue . Nxn lies within the region commonly deleted in Miller-Dieker Lissencephaly Syndrome ( MDLS—OMIM #247200 ) . Patients with this disorder sometimes have micrognathia ( small jaw ) , cleft palate , and heart defects , raising the possibility that Nxn plays a role in the pathogenesis of this disease [22] . In addition , the triad of glossoptosis ( displacement of the tongue ) , micrognathia , and cleft palate is seen in Pierre Robin syndrome ( OMIM #261800 ) . Recently , an autosomal dominant form of this disorder was associated with deletions around the SOX9 gene on 17q ( OMIM #608160 ) ; however , there are clearly still autosomal recessive forms of this disease for which the underlying genetic lesion has yet to be identified [23] , and NXN is a promising candidate gene for these cases . Nxn has been previously implicated as a negative regulator of the canonical Wnt pathway and the noncanonical PCP pathway in cell culture and in Xenopus [24] , [25] . Disrupting the signalling of either or both of these pathways could result in the craniofacial abnormalities observed in the NxnJ13/J13 mutants . In spite of the large number of candidate genes reported here , we know that our mutation detection is incomplete . Our overall mutation rate was 2 . 6×10−7 , which is within the published range for mutation rates defined after ENU treatment and sequencing ( 1 . 04×10−6 to 3 . 1×10−7 ) [26] , [27] . We predict that we have identified 36–100% of the possible lesions in our mutants . Prior searches for point mutations in an allelic series at myosin light chain 5a ( Myo5a; dilute ) and bone morphogenetic protein 5 ( Bmp5; short ears ) , failed to identify approximately 1/3 of the lesions within annotated coding sequences [28]–[30] . Further , two of five ENU-induced alleles of quaking ( qk ) lie outside the coding region or the 5′ and 3′ UTRs [31] , [32] . Despite extensive efforts to catalogue the coding component of mouse Chromosome 11 , it is likely that there are genes , particularly those expressed at discrete developmental time points or in rare cell types that have not been annotated . However , some of the missing Bmp5 lesions were later shown to lie in regulatory regions [33] . Here , we show that many of the ENU-induced lesions lie in non-coding regions , even though our exon-based sequencing strategy targeted only 125 base pairs outside each exon along with the 5′ and 3′ UTRs . We would predict that the mutants reported here have additional lesions in non-coding regions that were not sequenced in this project or in the exon sequences that were not obtained for each mutant . Some of these may lie in regulatory regions . The first ENU-induced mutation discovered to disrupt the sequence of a microRNA was recently reported to cause deafness [34] . MicroRNAs were not previously included in re-sequencing strategies for candidate genes , because they were not annotated . We report the first lesion in a miR200 seed sequence in the 3′ UTR of Med13 in the lethal line l11Jus05 , which dies at E 8 . 5 with cardiovascular and neural tube defects [16] . Med13 is a component of the mediator complex , which associates with RNA Polymerase II to direct transcription . It is expressed during embryonic development and throughout the brain and skeleton in the adult [35] . The mediator complex is required during development as evidenced by the fact that deletion of the components Med1 and Med21 produce embryonic lethal phenotypes due to cardiac defects [36] , [37] . MiR200 is required for the mesenchymal/epithelial transition during embryonic development and is involved in cancer metastasis [38] , [39] . Further studies of this mutation will help us to determine how miR200 regulates Med13 . Future sequencing efforts in any project designed to identify causes of mutation or disease should include microRNAs , 3′ and 5′UTRs , and any other highly-conserved noncoding regions . Although the functional nature of many conserved noncoding regions is not apparent , perhaps some of our mutations will allow us to determine the “genomic code” in non-coding conserved sequences . As polymorphisms are detected in Genome Wide Association Studies ( GWAS ) , the correlation of SNPs or copy number variation with disease will require knowledge of the biological function of each gene . Of the over 900 annotated transcripts in the Trp53-Wnt3 interval , 27% are associated with mutant phenotypes from gene targeting or spontaneous mutation ( Table S6 ) . However , only six of the candidate genes reported here have been targeted by mutation in the mouse ( Mapk14 [40] , [41] , Hes7 [42] , Slc4a1 [43] , Stat3 [44] , Nf1 [45] , and Dvl2 [18] ) , and only one in the rat ( Plekhm1 ) [46] . Seven genes are associated with disease mutations in the Online Mendelian Inheritance in Man database ( OMIM ) ( MPDU1 [47] , PLEKHM1 [46] , STAT3 [48] , SLC4A1 [49] , NF1 [50] , AIPL1 [51] and ASPA [52] ) , of which five are associated with mutations in mouse or rat ( Table 1 ) . Therefore , prior to this study , function was associated with only nine of the genes we report , showing that the majority of the lesions reveal new functions for the candidate genes . In aggregate , these data show that functional annotation of the mouse genome is still in its infancy , especially when one considers the low degree of saturation of our mutagenesis screen [17] . The mouse genome has a tremendous potential to provide biological and experimental annotation of both genes and noncoding conserved sequences relevant to GWAS , if this gap between function and phenotype is to be bridged . Here we show that re-sequencing must no longer be restricted to a few candidate genes . Faster , cheaper , high-throughput methods for re-sequencing reduce the need for narrowing candidate gene intervals to small regions by meiotic mapping . Targeted re-sequencing using Next Generation ( NextGen ) technologies should be attempted for mutation detection in additional mutant lines that map to restricted molecular intervals . The classical microcapillary sequencing method has a relatively low error rate . However , comparisons of the efficacy of the various NextGen methods show that each is sequence context dependent [53] , [54] . Regardless of error rate , cost or ease of use , our data show that although most sequencing methods are exon-based , strategies used for mutation detection should include conserved non-coding as well as coding sequences , also known as the “conservome” [54] . Altogether , accurate genome sequence and cheaper sequencing technologies provide a new avenue for understanding genomes , genome evolution , disease mutations and biological function .
All animal work was approved by the Institutional Animal Care and Use Committee ( IACUC ) . Our animal facility is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care International ( AAALAC ) . The sequence of mouse Chromosome 11 was generated using a hierarchical strategy to generate a sequence-ready physical map of the chromosome followed by clone-by-clone sequencing to generate high-quality finished sequence . Alignments for cross species comparative analysis were performed with WU-BLASTN ( http://blast . wustl . edu ) using the finished sequence assembly of mouse Chromosome 11 ( National Center for Biotechnology Information ( NCBI ) build 37 ) and of the human genome ( NCBI 37 ) . All sequences were repeat-masked with RepeatMasker ( http://repeatmasker . genome . washington . edu ) and low-quality alignments ( E-value >10−30 ) were removed prior to analysis . We performed manual annotation of the finished mouse Chromosome 11 following the human and vertebrate analysis and annotation ( HAVANA ) guidelines ( http://www . sanger . ac . uk/HGP/havana/ ) to identify 2 , 545 gene structures ( approximately 30% higher than previous computational predictions alone ) , which include 1 , 597 protein-coding loci , 450 processed transcripts , and 498 pseudogenes ( Table S2 ) . Before the process of manual annotation , an automated analysis pipeline for similarity searches and ab initio gene predictions was run , and the resulting data were manually annotated using the graphical in-house annotation tool “otterlace” . Manual gene annotation is available in Vega ( http://vega . sanger . ac . uk/index . html ) [55] . Protein coding loci were subcategorized into known and novel loci depending on whether the cDNA had an entry in RefSeq ( human loci ) or Mouse Genome Database ( mouse loci ) . If no open reading frame could be determined the locus was classified as a transcript and labeled novel or putative , depending on level of supporting evidence . The Chromosome 11 ENU mutagenesis screen was performed as described previously using C57BL/6J ENU-treated males and the 129 . Inv ( 11 ) 8BrdTrp53-Wnt3 balancer chromosome [4] , [5] . The inversion was generated in 129S5SvEv ES cells , and restricts the recovery of viable recombination products , so the region should remain 129S5 in subsequent crosses . After isolating a line based on its phenotype , animals were mated at least four times ( N4 ) to a 129S6/SvEv or congenic 129S6 . Rex line to allow for recombination , and then each line was maintained in trans to 129 . Inv11 ( 8 ) Brd . Sequences for the exons and their 1kb flanking sequences were extracted from Vega for all known protein-coding genes , novel coding sequences , and transcripts in the target region . Repeats in the sequence were masked using RepeatMasker ( http://www . repeatmasker . org/ ) prior to primer design . Primers were designed automatically using Primer3 ( http://frodo . wi . mit . edu/ ) to amplify each exon and at least 125bp on either side of the exon with an optimum amplicon size of 450–550bp . A series of overlapping primer pairs was designed for each larger exon to obtain complete coverage . Any exons failing automatic primer design had primers designed manually . Primer pairs were checked for uniqueness prior to ordering and pre-screened to determine the optimum conditions for amplification . Amplification was routinely performed on 48 DNA samples with 8 sequence tagged sites ( STSs ) for each sequence run . For initial large-scale sequencing , only one DNA from each mutant line was used . Because 7 . 8 Mb of transcribed linear DNA was sequenced per line for both strands , a total of over 560 Mb of DNA sequence was analyzed . One line reported here , l11Jus48 , was not sequenced for the entire region because the causative lesion was found by concurrent candidate gene sequencing . The majority of exons were amplified at 60°C . After amplification , an aliquot of the product was visualized on an agarose gel . Prior to sequencing , the remaining PCR product was purified using Exonuclease 1 and Shrimp Alkaline Phosphatase . Bi-directional sequencing of amplicons was carried out using Big Dye chemistry . For more details , please refer to http://www . sanger . ac . uk/humgen/exoseq/ . SNPs were called using ExoTrace ( http://www . sanger . ac . uk/humgen/exoseq/analysis . shtml ) , a novel algorithm developed in-house for the detection of sequence variants . The program works by comparing actual peak heights with the expected peak height for a homozygous base . A base is called as homozygous if the relative peak height in a single channel exceeds a threshold and the signal in all other channels is significantly smaller than the expected peak height . A base is called as heterozygous if the signal in two channels is approximately half the expected homozygous peak height and there is no significant signal in the other channels . ExoTrace processes the sense and antisense sequence reads separately and subsequently combines the results to allow SNP scoring . Each SNP is assigned a status according to a set of pre-defined rules . All SNPs below a certain threshold were subjected to manual review using a modified version of GAP4 , part of the Staden Sequence Analysis Package software ( http://staden . sourceforge . net/ ) , created for the ExoSeq project . Eighty-one of 1727 sequence variants that were identified in first pass sequencing were chosen for re-sequencing . Comparisons of sequence from each mutant line against every other line provided a control for SNPs in the B6 and 129 substrains . ENU-induced lesions that are causative are expected to occur only once in a single mutant line . Primers were designed manually using Primer3 ( v . 0 . 4 . 0 , http://frodo . wi . mit . edu ) to flank the candidate mutations ( Table S7 ) . Genomic DNA was phenol-chloroform extracted from livers , embryos , or tails from homozygous or heterozygous mutants was PCR-amplified and sequenced directly using the PCR primers and BigDye Terminator v3 . 1 ( Applied Biosystems ) according to the manufacturer's instructions . The sequencing chromatograms were analyzed with Sequencher 4 . 7 . The locations of the mutations are displayed on Ensembl v52 . The l11Jus13 ( NxnJ13 ) mutation is maintained in trans to 129 . Inv ( 11 ) 8BrdTrp53-Wnt3 [4] . The NxnJ13+/+Inv line was crossed five times to congenic 129S6 . Rex+/+Inv mice , to recover Rex+/+NxnJ13 animals and allow for recombination . Mice were genotyped with D11Mit327 or D11Mit132 for embryonic and neonatal studies prior to the identification of the causative lesion . The line was crossed four additional times to the 129S6/SvEvTac inbred mouse strain after the mutation was found , creating a new congenic line 129S6 . NxnJ13 . DNA was prepared from mouse tails , embryonic tissue , or yolk sac by either phenol/chloroform extraction or alkaline lysis ( tissue is treated with 50 mM NaOH at 95°C for 20 minutes , followed by neutralization with 1/5 volume of 0 . 5 M Tris , pH 8 . 0 ) . Genotyping of embryos , neonates , and adult mice was performed by PCR analysis of at least two STS markers flanking nucleoredoxin . Primer pairs for three different STS markers were used: 1 ) D11Bhm148 ( Forward primer 5′- AGGGGAAGTCCTGTATGGACA-3′ and Reverse primer 5′-ACCAACCTCGATAGAGCCATC-3′ ) , 2 ) rs13481111 ( F-GTAAGGACAAAGAGGACTGCCAAG and R-AATGACAGACAGGAGGAAATCCAT ) , or 3 ) D11Mit245 ( F-ATGAGACCATGCTCCTCCAC and R-TTGTCCTCTGACCTTCACACC ) . The PCR mixture contained 5× Promega GoTaq PCR buffer , 0 . 3 mM dNTPs , 0 . 5 µM primer mix , 250 ng template , and 0 . 25 U Taq Polymerase ( New England Biolabs ) . Cycling conditions for D11Bhm148 were: 94°C 5 min; 40 cycles of 94°C 45 sec , 55°C 45 sec , 72°C 45 sec; then 7 min at 72°C; followed by incubation at 4°C . The PCR products ( C57BL/6J - 97 bp , 129SvEvTac - 107 bp ) were resolved on 4% NuSieve gels ( Lonza ) . Cycling conditions for rs13481111 were: 94°C 5 min; 40 cycles of 94°C 45 sec , 57°C 45 sec , 72°C 45 sec; then 7 min at 72°C; followed by incubation at 4°C . Digestion of the 236 bp PCR product with Sau96I ( New England Biolabs ) yields 2 bands for C57BL/6J ( 72 and 164 bp ) and one for 129SvEvTac ( 236bp band remains uncut ) . These products were resolved on 2 . 5% SeaKem LE agarose gels ( Lonza ) . The cycling conditions for D11Mit245 were: 94°C 5 min; 40 cycles of 94°C 45 sec , 60°C 45 sec , 72°C 45 sec; then 7 min at 72°C; followed by incubation at 4°C . The PCR products ( C57BL/6J - 152 bp , 129SvEv - 140 bp ) were resolved on 5% MetaPhor gels ( Lonza ) . To generate the Nxntm1EUCOMM ( Nxn−/− ) allele , C57BL/6N JM8 ES cells containing a multipurpose conditional “knockout-first” construct targeted to Nxn were obtained from the European Conditional Mouse Mutagenesis Program ( EUCOMM ) . These ES cells were injected into C57BL/6Brd Tyr−/− blastocysts and implanted into pseudopregnant mothers . Male chimeric offspring with black and white coats were then mated to C57BL/6Brd Tyr−/− females , and black progeny were genotyped for the knockout-first allele with the following primers: NxnFor ( TTGGGTATGCCCGACTCCCCCACC ) , NxnRev ( CCTTCAGCCCTCTCCTTTCTGTGC ) , and loxPrev ( TGAACTGATGGCGAGCTCAGACC ) . Two PCR reactions were set up for each sample , one with NxnFor and NxnRev , which gives a 435 bp PCR product from the mutant and a 568 bp product from wild-type , and one with NxnFor and loxPrev , which gives a 228 bp PCR product from the mutant , but none from the wild-type . PCR conditions were: 5× Promega GoTaq PCR buffer , 0 . 3 mM dNTPs , 0 . 5 µM primer mix , 250 ng template , 1M betaine , 0 . 25 U Taq Polymerase ( New England Biolabs ) . The cycling conditions were as follows: 94°C 5 min; 40 cycles of 94°C 45 sec , 56°C 45 sec , 72°C 45 sec; then 7 min at 72°C; followed by incubation at 4°C . The PCR products were resolved on 2% agarose gels . Meiotic mapping was performed by crossing NxnJ13+/+Inv to congenic 129S6 . Rex/Rex mice , followed by intercrossing NxnJ13+/+Rex mice , and examining the progeny for recombination events . Embryos and neonates with an abnormal facial structure and a cleft palate were classified as affected progeny ( n = 82 ) , and mice that survived to weaning were classified as unaffected progeny ( n = 239 ) . Two or more polymorphic markers between the C57BL/6J and 129S6/SvEvTac strains were analyzed for every DNA sample , which was obtained from embryonic tissue or adult mouse tails by phenol/chloroform extraction . The following microsatellite markers were assessed: D11Mit4 , D11Mit219 , D11Mit322 , D11Bhm148 , D11Mit245 , D11Mit120 , D11Mit324 , D11Mit39 , D11Mit327 , D11Mit132 , and D11Mit333 . The following SNPs were assessed: rs3702197 , rs13481111 , rs13481113 , rs13481117 , and rs13481125 . Primer sequences and restriction enzymes used to digest the SNPs are shown in Table S8 . RNA was made from pools of three E14 . 5 heads and livers with RNA STAT-60 ( Tel-Test ) , and treated with DNase I ( Invitrogen ) following the manufacturer's instructions . Two micrograms of RNA were reverse transcribed into cDNA using SuperScript III First-Strand Synthesis System for RT-PCR ( Invitrogen ) with random hexamers following the manufacturer's instructions . Gapdh was used as a positive control using the following primers: F-CGGAGTCAACGGATTTGGTCGTAT and R-GCCTTCATGGTGGTGAAGAC . Cycling conditions were: 94°C 5 min; 30 cycles of 94°C 30 sec , 60°C 30 sec , 72°C 30 sec; then 7 min at 72°C; followed by incubation at 4°C . Nxn RT-PCR was carried out using the following primers: F-GGTGCTCAATGACGAGGACT and R-GCCTCCTCTTCTTTGGCTTT , which amplified the junction between exons 6–7 ( 213 bp wild-type product and 243 bp mutant product ) . Cycling conditions were: 94°C 5 min; 35 cycles of 94°C 45 sec , 60°C 45 sec , 72°C 45 sec; then 7 min at 72°C; followed by incubation at 4°C . The PCR products were gel extracted , cleaned with Zymoclean Gel DNA Recovery Kit ( Zymo Research ) , and sequenced directly using the PCR primers and Big Dye Terminator v3 . 1 ( Applied Biosystems ) . The Big Dye terminator was removed with Centri . Sep 8 ( Princeton Separations ) , according to the manufacturer's instructions . Sequencing was performed by the Child Health Research Center ( Baylor College of Medicine ) and the sequencing chromatograms were analyzed with Sequencher 4 . 7 . The miRBase Sequence Database ( http://www . mirbase . org/ ) was used to identify 18 known miRNA sequences that lie within the Chromosome 11 balancer region . Two miRNAs , mmu-mir-324 and mmu-mir-423 , lie within genes in which mutations were found , dishevelled 2 ( Dvl2 ) and coiled-coil domain containing 55 ( Ccdc55 ) , respectively , though neither mutation identified in these genes is within the miRNA sequence itself . Target Scan 4 . 2 ( http://www . targetscan . org/ ) was used to determine if any of the mutations that did not cause amino acid changes had altered a known conserved miRNA target . Total embryo protein extracts were prepared by grinding embryos in reducing 2× SDS sample buffer ( 200 mM Tris-HCl pH 6 . 8 , 3% w/v SDS , 20% v/v glycerol , 10% v/v β–mercaptoethanol , 4% v/v saturated bromophenol blue solution ) using a Tekmar electronic tissue homogenizer . Samples were resolved by SDS-PAGE on a precast 4–20% Tris-HCl polyacrylamide gel ( Bio-Rad ) and transferred to a Hybond ECL nitrocellulose membrane ( Amersham ) . Nucleoredoxin was visualized using the previously described polyclonal anti-Nxn antibody [24] , which was made against full-length protein and purified against a C-terminal fragment . Experiments were repeated using a polyclonal antibody made against an N-terminal fragment of Nxn as well [24] . Primary antibody ( 1∶1000 of 0 . 5 mg/mL stock ) incubation was followed by an anti-rabbit secondary antibody ( 1∶10 , 000 of 0 . 8 mg/mL stock ) linked to horseradish peroxidase ( Jackson Immunoresearch ) and detected on Hyperfilm ECL film using the ECL Plus Western Blotting Detection kit ( Amersham ) according to the manufacturer's instructions . Actin was used as a loading control and was visualized similarly using the rabbit anti-actin antibody ( A2066 ) from Sigma . Timed matings were carried out on intercrosses between NxnJ13+/+Inv×NxnJ13+/+Inv and NxnJ13/+×NxnJ13/+mice . The day that a vaginal plug was observed was designated E0 . 5 . Embryos were dissected at E15 . 5 and E18 . 5 , visualized by light microscopy with a Leica microscope ( Diagnostic Instruments ) , and photographed using a SPOT digital camera . Mice at E18 . 5 were weighed with a laboratory balance ( Mettler Toledo ) and Student's t-tests were carried out to determine significance . Whole-mount skeletal/cartilage preparations were carried out with solutions containing alcian blue , which stains cartilage , and alizarin red , which stains mineralized bone . The skin and the internal organs were removed from E18 . 5 and P0 mice , fixed overnight in 95% ethanol , stained overnight with an alcian blue solution ( 0 . 015% alcian blue 8GX from Sigma , 20% acetic acid , 80% ethanol ) , transferred to 95% ethanol for at least three hours , transferred to 2% KOH for at least 24 hours , stained overnight with an alizarin red solution ( 0 . 005% alizarin sodium sulfate from Sigma , 1% KOH ) , cleared for at least two days with 1% KOH/20% glycerol , and stored in a 1∶1 mix of glycerol and 95% ethanol . The entire procedure was carried out at room temperature . Adobe Photoshop 6 . 0 was used to measure the lengths of mandibles and femurs and Student's t-tests were performed to determine if significant differences in bone length occurred .
|
Here we show that tiny DNA lesions can be found in huge amounts of DNA sequence data , similar to finding a needle in a haystack . These lesions identify many new candidates for disease genes associated with birth defects , infertility , and growth . Further , our data suggest that we know very little about what mammalian genes do . Sequencing methods are becoming cheaper and faster . Therefore , our strategy , shown here for the first time , will become commonplace .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry/molecular",
"evolution",
"genetics",
"and",
"genomics/animal",
"genetics",
"genetics",
"and",
"genomics/gene",
"discovery",
"genetics",
"and",
"genomics/comparative",
"genomics",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics",
"genetics",
"and",
"genomics/functional",
"genomics",
"computational",
"biology/comparative",
"sequence",
"analysis",
"computational",
"biology/molecular",
"genetics",
"cell",
"biology/developmental",
"molecular",
"mechanisms",
"molecular",
"biology/molecular",
"evolution",
"evolutionary",
"biology/genomics",
"computational",
"biology/genomics",
"developmental",
"biology/molecular",
"development",
"evolutionary",
"biology/developmental",
"molecular",
"mechanisms",
"developmental",
"biology/developmental",
"molecular",
"mechanisms"
] |
2009
|
Discovery of Candidate Disease Genes in ENU–Induced Mouse Mutants by Large-Scale Sequencing, Including a Splice-Site Mutation in Nucleoredoxin
|
HIV is notorious for its capacity to evade immunity and anti-viral drugs through rapid sequence evolution . Knowledge of the functional effects of mutations to HIV is critical for understanding this evolution . HIV’s most rapidly evolving protein is its envelope ( Env ) . Here we use deep mutational scanning to experimentally estimate the effects of all amino-acid mutations to Env on viral replication in cell culture . Most mutations are under purifying selection in our experiments , although a few sites experience strong selection for mutations that enhance HIV’s replication in cell culture . We compare our experimental measurements of each site’s preference for each amino acid to the actual frequencies of these amino acids in naturally occurring HIV sequences . Our measured amino-acid preferences correlate with amino-acid frequencies in natural sequences for most sites . However , our measured preferences are less concordant with natural amino-acid frequencies at surface-exposed sites that are subject to pressures absent from our experiments such as antibody selection . Our data enable us to quantify the inherent mutational tolerance of each site in Env . We show that the epitopes of broadly neutralizing antibodies have a significantly reduced inherent capacity to tolerate mutations , rigorously validating a pervasive idea in the field . Overall , our results help disentangle the role of inherent functional constraints and external selection pressures in shaping Env’s evolution .
HIV evolves rapidly: the envelope ( Env ) proteins of two viral strains within a single infected host diverge as much in a year as the typical human and chimpanzee ortholog has diverged over ∼5-million years [1–4] . This rapid evolution is central to HIV’s biology . Most humans infected with HIV generate antibodies against Env that effectively neutralize viruses from early in the infection [5–7] . However , Env evolves so rapidly that HIV is able to stay ahead of this antibody response , with new viral variants escaping from antibodies that neutralized their predecessors just months before [5–7] . Env’s exceptional evolutionary capacity is therefore essential for the maintenance of HIV in the human population . A protein’s evolutionary capacity depends on its ability to tolerate point mutations . Detailed knowledge of how mutations affect Env is therefore key to understanding its evolution . Many studies have estimated the effects of mutations to Env . One strategy is experimental: numerous studies have used site-directed mutagenesis or alanine scanning to measure how specific mutations affect various aspects of Env’s function [8–17] . However , these experiments have examined only a small fraction of the many possible mutations to Env . Another strategy is computational: under certain assumptions , the fitness effects of mutations can be estimated from their frequencies in global or intra-patient HIV sequences [18–22] . However , these computational strategies are of uncertain accuracy and cannot separate the contributions of inherent functional constraints from those of external selection pressures such as antibodies . Therefore , a more complete and direct delineation of how every mutation affects Env’s function would be of great value . It is now possible to make massively parallel experimental measurements of the effects of protein mutations using deep mutational scanning [23–25] . These experiments involve creating large libraries of mutants of a gene , subjecting them to bulk functional selections , and quantifying the effect of each mutation by using deep sequencing to assess its frequency pre- and post-selection . Over the last few years , deep mutational scanning has been used to estimate the effects of all single amino-acid mutations to a variety of proteins or protein domains [26–39] , as well as to estimate the effects of a fraction of the amino-acid mutations to many additional proteins ( e . g . , [40–42] ) . When these experiments examine all amino-acid mutations , they can be used to compute the mutational tolerance of each protein site , thereby shedding light on a protein’s inherent evolutionary capacity . Recently , deep mutational scanning has been used to examine the effects of amino-acid mutations on the binding of antibodies to Env protein displayed on mammalian or yeast cells [43 , 44] , or the effects of single-nucleotide mutations scattered across the HIV genome on viral replication in cell culture [45] . However , none of these studies comprehensively measure the effects of all Env amino-acid mutations on viral replication . Therefore , we currently lack comprehensive measurements of the site-specific mutational tolerance of Env . Here we use deep mutational scanning to experimentally estimate how all amino-acid mutations to the ectodomain and transmembrane domain of Env affect viral replication in cell culture . At most sites , our measurements correlate with the frequencies of amino acids in natural HIV sequences . However , there are large deviations at sites where natural evolution is strongly shaped by factors ( e . g . , antibodies ) that are absent from our experiments . Our results also show that site-to-site variation in Env’s inherent capacity to tolerate mutations helps explain why epitopes of broadly neutralizing antibodies are highly conserved in natural isolates . Overall , our work helps elucidate how inherent functional constraints and external selective pressures combine to shape Env’s evolution , and demonstrates a powerful experimental approach for comprehensively mapping how mutations affect HIV phenotypes that can be selected for in the lab .
We used the deep mutational scanning approach in Fig 1A to estimate the effects of all single amino-acid mutations to Env . We applied this approach to Env from the LAI strain of HIV [46] . LAI is a CXCR4-tropic subtype B virus isolated from a chronically infected individual and then passaged in human T-lymphocytes . We chose this strain because LAI and the closely related HXB2 strain have been widely used to study Env’s structure and function [8–11 , 47–49] , providing extensive biochemical data with which to benchmark our results . LAI’s Env is 861 amino acids in length . We mutagenized amino acids 31–702 ( throughout this paper , we use the HXB2 numbering scheme [50] ) . We excluded the N-terminal signal peptide and the C-terminal cytoplasmic tail , since mutations in these regions can alter Env expression in ways that affect viral infectivity in cell culture [51–53] . The region of Env that we mutagenized spanned 677 residues , meaning that there are 677 × 63 = 42 , 651 possible codon mutations , corresponding to 677 × 19 = 12 , 863 possible amino-acid mutations . To create plasmid libraries containing all these mutations , we used a previously described PCR mutagenesis technique [31] that creates multi-nucleotide ( e . g , gca→CAT ) as well as single-nucleotide ( e . g , gca→gAa ) codon mutations . We created three independent plasmid libraries , and carried each library through all subsequent steps independently , meaning that all our measurements were made in true biological triplicate ( Fig 1B ) . We Sanger sequenced 26 clones to estimate the frequency of mutations in the plasmid mutant libraries ( S1 Fig ) . There were an average of 1 . 4 codon mutations per clone , with the number of mutations per clone roughly following a Poisson distribution . The deep sequencing described in the next section found that at least 79% of the ≈104 possible amino-acid mutations were observed at least three times in each of the triplicate libraries , and that 98% of mutations were observed at least three times across all three libraries combined . The plasmid libraries therefore sampled most amino-acid mutations to Env . We produced virus libraries by transfecting each plasmid library into 293T cells . The viruses in the resulting transfection supernatant lack a genotype-phenotype link , since each cell is transfected by many plasmids . We therefore passaged the transfection supernatants twice in SupT1 cells at an MOI of 0 . 005 to create a genotype-phenotype link and select for functional variants . Importantly , neither 293T nor SupT1 cells express detectable levels of APOBEC3G [54 , 55] , which can hypermutate HIV genomes [56 , 57] . This is a crucial point: although HIV encodes a protein that counteracts APOBEC3G , a fraction of viruses will lack a functional version of this protein and so have their genomes hypermutated in APOBEC3G-expressing cells . For each library , we passaged 5 × 105 infectious particles in order to maintain library diversity . We used Illumina deep sequencing to quantify the frequency of each mutation before and after passaging . In order to increase the sequencing accuracy , we attached unique molecular barcodes or “Primer IDs” to each PCR amplicon [58–61] . We sequenced the plasmids to assess the initial mutation frequencies , and sequenced non-integrated viral DNA [62] from infected SupT1 cells to assess the mutation frequencies in the viruses . A concern is that errors from sequencing and viral replication ( e . g . , from viral reverse transcriptase ) would introduce bias . To address this concern , we paired each mutant library with a control in which we generated wildtype virus from unmutated plasmid . Sequencing the control plasmids and viruses enabled us to estimate and statistically correct for the rates of these errors ( S2 Fig ) . Overall , these procedures allowed us to implement the deep mutational scanning workflow in Fig 1 . Our deep mutational scanning experiments require that selection purge the virus libraries of non-functional variants . As an initial gene-wide measure of selection , we analyzed how different types of codon mutations ( nonsynonymous , synonymous , and stop-codon mutations ) changed in frequency after selection . In these analyses , we corrected for background errors from PCR , sequencing , and viral replication by subtracting the mutation frequencies measured in our wildtype controls from those measured in the mutant libraries ( S2 Fig ) . Stop-codon mutations are expected to be uniformly deleterious . Indeed , after correcting for background errors , stop codons were purged to <1% of their initial frequency in the twice-passaged viruses for each replicate , indicating strong purifying selection ( see the data for “all sites” in Fig 2A ) . The second viral passage is important for complete selection , as stop codons remain at about ≈16% of their initial frequency in viruses that were only been passaged once ( S3 Fig ) . Interpreting the frequencies of nonsynonymous mutations is more nuanced , as different amino-acid mutations have different functional effects . However , a large fraction of amino-acid mutations are deleterious to any protein [63–65] . Therefore , one might expect that the frequency of nonsynonymous mutations would decrease substantially in the twice-passaged mutant viruses . But surprisingly , even after correcting for background errors , the average frequency of nonsynonymous mutations in the passaged viruses is ≈90% of its value in the mutant plasmids ( see the data for “all sites” in Fig 2A ) . However , the average masks two disparate trends . In each library , a few sites exhibit large increases in the frequency of nonsynonymous mutations , whereas this frequency decreases by nearly two-fold for all other sites ( see the data for the subgroups of sites in Fig 2A ) . An obvious hypothesis is that at a few sites , amino-acid mutations are favored because they are adaptive for viral replication in cell culture . Consistent with this hypothesis , the sites that experienced large increases in mutation frequencies are similar among the three replicates ( Fig 2B ) , suggestive of reproducible selection for mutations at these sites . Moreover , these sites are spatially clustered in Env’s crystal structure in regions where mutations are likely to enhance viral replication in cell culture ( Fig 3 and S1 Table ) . One cluster of mutations disrupts potential glycosylation sites at the trimer apex ( Fig 3A ) . This result suggests that some of the glycans that help shield Env from antibodies in nature [6 , 66] actually decrease viral fitness in the absence of immune selection . This idea is consistent with previous studies showing that that loss of glycosylation sites can enhance viral infectivity in cell culture [67–69] . A second cluster overlaps sites where mutations influence Env’s conformational dynamics , which are commonly altered by cell-culture passage [70 , 71] . It has been hypothesized that neutralization-resistant Envs primarily assume conformations that mask conserved antibody epitopes , while lab-adapted variants more efficiently sample different conformations associated with CD4 binding [72] . Thus , the adaptive mutations we observe may enable Env to more efficiently use CD4 in cell culture , but would not be selected in nature because they expose conserved epitopes . A third cluster is at the co-receptor binding interface ( Fig 3B ) , where mutations may enhance viral entry in cell culture . Therefore , while most of Env is under purifying selection against changes to the protein sequence , a few sites are under selection for cell-culture adapting amino-acid mutations . If our experiments are indeed identifying mutations to LAI that are beneficial in cell culture , then one expectation is that some of these mutations might fix after prolonged passage of LAI in cell culture . Interestingly , almost exactly such an experiment was performed in the early study of HIV . The LAI strain used in our study was initially isolated from a chronically infected individual and then passaged in cell culture for a short period of time before cloning [46 , 77] . HXB2 , another common lab strain , is derived from a variant of LAI that was repeatedly passaged in a variety of cell lines , initially as a contaminant of other viral stocks [78 , 79] . There are 23 amino-acid differences between the Env proteins of LAI and HXB2 . Although the predecessor for HXB2 was not passaged in the same SupT1 cell line that we used , if its passage in other cell lines led to mutations that were generally adaptive to cell culture , then we would expect them to introduce amino acids in HXB2 that are also selected in our deep mutational scan of LAI . Indeed , we found that most differences between LAI and HXB2 introduced mutations to amino acids that our experiments suggest are more preferred in cell culture than the wildtype LAI amino acid ( S2 Table ) . Thus , our results are consistent with the expectation that HXB2 is more adapted to cell culture than LAI . The average error-corrected frequency of synonymous mutations changes little after selection ( an average decrease to 96% of the original frequency; see the data for “all sites” in Fig 2A ) . This overall trend is consistent with the fact that synonymous mutations usually have smaller functional effects than nonsynonymous mutations . However , synonymous mutations can sometimes have substantial effects [21 , 80–82] , particularly in viruses like HIV that are under strong selection for RNA secondary structure and codon usage [83 , 84] . To assess selection on synonymous mutations on a more site-specific level , we examined the change in frequency of multi-nucleotide codon mutations across env’s primary sequence ( Fig 4 ) . The rationale behind examining only multi-nucleotide codon mutations is that they are not appreciably confounded by errors from PCR , deep sequencing , or de novo mutations from viral replication ( S2 and S4 Figs ) . In a region roughly spanning codons 500 to 600 , selection strongly purged both synonymous and nonsynonymous multi-nucleotide codon mutations ( Fig 4 ) . This region contains env’s Rev-response element ( RRE ) [85] , a highly structured region of RNA that is bound by the Rev protein to control the temporal export of unspliced HIV transcripts from the nucleus [86 , 87] . The finding of strong selection on the nucleotide as well as the amino-acid sequence of the RRE region of Env therefore agrees with our biological expectations . The previous section examined broad trends in selection averaged across many sites . But our data also enable much more fine-grained estimates of the preference for every amino-acid at every position in Env . We define a site’s preference for an amino acid to be proportional to the enrichment or depletion of that amino acid after selection ( correcting for the error rates determined using the wildtype controls ) , normalizing the preferences for each site so that they sum to one . We denote the preference of site r for amino acid a as πr , a , and compute the preferences from the deep-sequencing data as described in [88] . Since we mutagenized 677 residues in Env , there are 677 × 20 = 13 , 540 preferences . If selection in our experiments exactly parallels selection in nature and there are no shifts in mutational effects as Env evolves , then these preferences are the expected frequencies of each amino acid at each site in an alignment of Env sequences that have reached evolutionary equilibrium under a mutation process that introduces each amino acid with equal probability [31 , 89] . Fig 5 shows Env’s site-specific amino-acid preferences after averaging across replicates and re-scaling to account for the stringency of selection in our experiments ( details of this re-scaling are in the next section ) . As is immediately obvious from Fig 5 , sites vary dramatically in their tolerance for mutations . Some sites strongly prefer a single amino acid , while other sites can tolerate many amino acids . For instance , site 457 , an important receptor-binding residue [8] , has a strong preference for aspartic acid . However , this site is adjacent to a variable loop ( sites 460–469 ) where most sites tolerate many amino acids . Another general observation is that when sites tolerate multiple amino acids , they often prefer ones with similar chemical properties . For instance , sites 225 and 226 prefer hydrophobic amino acids , while sites 162 to 164 prefer positively charged amino acids . To confirm that our experiments captured known constraints on Env’s function , we examined mutations that have been characterized to affect key functions of Env . Table 1 lists mutations known to disrupt an essential disulfide bond , binding to receptor or co-receptor , or protease cleavage . In almost all cases , the deleterious mutation introduces an amino-acid that our experiments report as having a markedly lower preference than the wildtype amino acid . Therefore , our measurements largely concord with existing knowledge about mutations that affect key aspects of Env’s function . A crucial aspect of any high-throughput experiment is assessing the reproducibility of independent replicates . Fig 5 shows the average of the preferences measured in each replicate . Fig 6A shows the correlations among the 13 , 540 site-specific amino-acid preferences estimated from each of the three replicates . The correlations are modest , indicating substantial replicate-to-replicate noise . In principle , this noise could arise from differences in the initial plasmid mutant libraries , bottlenecks during the generation of viruses by transfection , bottlenecks during viral passaging , or bottlenecks during the sequencing of proviral DNA from infected cells . Analysis of technical replicates of the first or second round of viral passaging indicates that most of the noise arises from bottlenecks during the viral passaging or sequencing steps . Specifically , measurements from replicate 3 are no more correlated to those from replicates 3b-1 or 3b-2 ( which are repeated passages of the same transfection supernatant , Fig 1B ) than they are to those from totally independent replicates ( compare Fig 6 and S6 Fig ) . However , replicates 3b-1 and 3b-2 ( which shared the first of the two viral passages , Fig 1 ) do yield more correlated measurements than independent replicates ( S6 Fig ) . The existence of bottlenecks during viral passage is also suggested by the data in S4 and S5 Figs . Therefore , the experimental reproducibility could probably be increased by passaging more infectious viruses at each step . If bottlenecks cause each replicate to sample slightly different mutations , then perhaps the total number of tolerated mutations per site will be similar between replicates , even if the exact mutations differ . To test this hypothesis , we computed the effective number of amino acids tolerated at each site as the exponential of the Shannon entropy of the site’s amino-acid preferences . Fig 6B shows that the effective number of amino acids tolerated at each site is more correlated between replicates than the preferences themselves . We further reasoned that even if bottlenecking causes slight variations in the preferred amino acids between replicates , each site would still tend to prefer amino acids with similar chemical characteristics . To test this hypothesis , we quantified the extent that each site preferred hydrophobic or hydrophilic amino acids by computing a site-specific hydrophobicity score from the amino-acid preferences . Fig 6C shows that these preference-weighted hydrophobicities are more correlated between replicates than the preferences . Therefore , even though there is replicate-to-replicate noise in the exact amino acids preferred at a site , the effective number of tolerated amino acids and the chemical properties of these amino acids are similar among replicates . In the previous section , we showed that our experimentally measured amino-acid preferences captured the constraints on Env’s biological function for sites with known mutational effects ( Table 1 ) . If this is true across the entire protein , then our measurements should correlate with the frequencies of amino acids in natural HIV sequences . Table 2 shows that there is a modest correlation ( Pearson’s R ranging from 0 . 29 to 0 . 36 ) between the preferences from each experimental replicate and the frequencies in an alignment of HIV-1 group-M sequences ( a phylogenetic tree of these sequences is in Fig 7A; sites in Env variable loops that can not be reliably aligned are excluded as described in the Methods ) . Since each replicate suffers from noise due to partial bottlenecking of the viral diversity , we hypothesized that averaging the preferences across replicates should make them more accurate . Indeed , averaging the replicates increased the correlation to R = 0 . 4 ( Table 2 ) . The concordance between deep mutational scanning measurements and natural sequence variation is improved by accounting for differences in the stringency of selection in the experiments compared to natural selection [89 , 91] . Specifically , if the measured preference is πr , a and the stringency parameter is β , then the re-scaled preference is ( πr , a ) β/[ ∑a′ ( πr , a′ ) β ] . A stringency parameter of β > 1 means that natural evolution favors the same amino acids as the experiments , but with greater stringency . Table 2 shows that for all replicates , the stringency parameter that maximizes the correlation is >1 . Therefore , natural selection prefers the same amino acids as our experiments , but with greater stringency . After averaging across replicates and re-scaling by the optimal stringency parameter , the Pearson correlation is 0 . 44 between our experimentally measured preferences and amino-acid frequencies in the alignment of naturally occurring HIV sequences ( Fig 7B ) . Is this a good correlation ? At first glance , a correlation of 0 . 44 seems unimpressive . But we do not expect a perfect correlation even if the experiments perfectly concord with selection on Env in nature . There are several factors that are expected to reduce the correlation between the experimentally measured preferences and amino-acid frequencies in natural sequences . First , our experiments examine the effects of mutations to Env from the LAI strain . However , it is well known that epistasis can cause the effects of mutations to differ among homologs of the same protein [92 , 93] , and many examples of this phenomenon have been documented in HIV Env [94–97] . Therefore , our measurements for the LAI Env are probably not completely generalizable to all other strains . In addition , natural HIV sequences are drawn from a phylogeny ( Fig 7A ) , not an ideal ensemble of all possible Env sequences . The frequencies of amino acids in this phylogeny reflect evolutionary history as well as natural selection . For instance , if several amino acids are equally preferred at a site , one is likely to be more frequent in the alignment due to historical contingency . Additionally , natural evolution is influenced by the genetic code and mutation biases: a mutation from the tryptophan codon TGG to the valine codon GTT is extremely unlikely even if valine is more preferred than tryptophan . Mutation biases inherent in reverse transcription [98] or APOBEC3G-induced hypermutation [54] could also bias some evolutionary outcomes over others . Therefore , the correlation will be imperfect even if the preferences completely concord with natural selection—the question is how the actual correlation compares to what is expected given the phylogenetic history and mutation biases . To determine the expected correlation if the experimentally measured amino-acid preferences reflect conserved constraints in Env , we simulated evolution along the phylogenetic tree in Fig 7A under the assumption that the experimentally measured preferences exactly match natural selection . Specifically , we used pyvolve [99] to simulate evolution using the experimentally informed site-specific codon substitution models described in [91] , which define mutation-fixation probabilities in terms of the amino-acid preferences . In addition to the preferences and the stringency parameter β = 2 . 1 from Table 2 , the substitution models in [91] require specification of parameters reflecting biases in the mutation process . We estimated nucleotide mutation bias parameters of ϕA = 0 . 55 , ϕC = 0 . 15 , ϕG = 0 . 11 , and ϕT = 0 . 18 from the frequencies at the third-nucleotide codon position in sequences in the group-M alignment for sites where the most common amino acid had 4-fold codon degeneracy . We used the transition-transversion ratio of κ = 4 . 4 estimated in [100] . For these simulations , we scaled the branch lengths so that the average pairwise protein divergence was the same in the actual and simulated alignments . The correlation between the preferences and amino-acid frequencies in a representative simulated alignment is shown in Fig 7C . As this plot illustrates , the expected correlation is only about 0 . 46 if the experimentally measured preferences exactly describe natural selection on Env under our model . The simulated frequencies in Fig 7C show the same pattern of bi-modality ( most values near zero or one ) as the actual frequencies in Fig 7B despite the fact that the preferences used in the simulations allow multiple amino acids at most sites ( see Fig 5 ) . This fact illustrates that bi-modality in the amino-acid frequencies can arise from the historical contingency inherent in a phylogenetic tree even if multiple amino acids are tolerated at most sites . As a control , we also simulated evolution using substitution models in which the preferences have been randomized among sites ( Fig 7D ) ; as should be the case , there is no correlation in these control simulations . So the actual correlation is nearly as high as expected if natural selection concords with the preferences measured in our experiment . We next investigated if there are parts of Env for which there is an especially low correlation between our experimentally measured preferences and natural amino-acid frequencies . For instance , antibodies exert selection on the surface of Env in nature [6 , 7 , 101 , 102] . We therefore examined the actual and simulated correlations between the preferences and frequencies as a function of solvent accessibility ( Fig 7E and 7F ) . For all sites ( right side of Fig 7E , left side of Fig 7F ) , the actual correlation is only slightly lower than the range of correlations in 100 simulations . For more buried sites , both the simulated and actual correlations increase ( Fig 7E ) , presumably because sites in the core of Env tend to have stronger preferences for specific amino acids . But as sites become more surface-exposed , the actual correlation drops below the value expected from the simulations ( Fig 7F ) . Therefore , our experiments provide a relatively worse description of natural selection on Env’s surface than its core—probably because the evolution of the protein’s core is shaped mostly by inherent functional constraints that are effectively captured by our experiments , whereas the surface is subject to selection pressures ( e . g . , antibodies ) that are not modeled in our experiments . Comparing disulfide-bonded cysteines and glycosylation sites vividly illustrates this dichotomy between inherent functional constraints and external selection pressures . Env has 10 highly conserved disulfide bonds , most of which are essential for the protein’s inherent function [49] . Env also has numerous N-linked glycosylation sites , many of which are also highly conserved in nature , where they help shield the protein from antibodies [6 , 66] . In contrast to the disulfides , only some glycosylation sites are important for Env’s function in the absence of immune selection [67 , 69] . Fig 8 shows that our experimentally measured preferences are highly correlated with natural amino-acid frequencies at the sites of the disulfides , but not at the glycosylation sites . This result can easily be rationalized: the disulfides are inherently necessary for Env’s function , whereas many glycosylation sites are important largely because of the external selection imposed by antibodies . Our experiments therefore accurately reflect the natural constraints on the former but not the latter . The fact that we found well-tolerated mutations at all of Env’s glycosylation sites ( S7A Fig ) might seem surprising given that other studies have shown that some glycosylation sites are important for Env’s function in certain HIV strains [67 , 69] . However , these studies were all performed in HIV strains substantially diverged from LAI . A study in HXB2 ( which is closely related to LAI ) found that individual mutations are at least partially tolerated at all glycosylation sites in Env’s gp120 subunit when assaying for viral infectivity in cell culture [103] . Therefore , glycosylation sites may be especially expendable in the LAI strain used in our study . Different sites in Env evolve at different rates in natural HIV sequences . For instance , sites on the apical surface of Env evolve especially rapidly [104] . These differences in evolutionary rate arise from two factors . First , some sites are inherently better at tolerating mutations without disrupting Env’s essential functions . Second , some sites are under stronger immune selection for rapid sequence change . However , since Env in nature is under selection both to maintain its function and escape immunity , it is difficult to deconvolve these factors . Our experiments estimate each site’s inherent tolerance for mutations under selection purely for Env’s function in cell culture , without the confounding effects of immune selection ( for the remainder of this section , we define a site’s mutational tolerance as the Shannon entropy of its amino-acid preferences shown in Fig 5 ) . We can therefore assess whether regions of Env that evolve rapidly or slowly in nature also have unusually high or low inherent tolerance to mutations . We focused on two regions of Env . First , we analyzed portions of the protein classified as “variable loops” due to extensive variation in nature [105 , 106] . These loops are frequently targeted by antibodies that drive rapid sequence evolution [102 , 107] . Because these loops evolve rapidly , we hypothesized they would have a high inherent mutational tolerance . But an alternative hypothesis is that their rapid evolution more attributable to strong selection from antibodies than an unusually high mutational tolerance . Second , we focused on epitopes of antibodies that broadly neutralize many HIV strains . Because these epitopes are highly conserved in nature and often overlap with regions of known functional constraint [108–113] , we hypothesized they would have a low mutational tolerance . However , an alternative hypothesis is that these epitopes evolve slowly not because they are mutationally intolerant but simply because they are under weaker immune selection . Indeed , broad immune responses targeting these epitopes only develop in 20% of infected individuals and generally only after multiple years of infection [114] . In testing these hypotheses , it is important to control for other properties known to affect mutational tolerance . This can be done by using multiple linear regression to simultaneously analyze how several independent variables affect the dependent variable of mutational tolerance . Relative solvent accessibility ( RSA ) is the strongest determinant of mutational tolerance in proteins [115] , so we included RSA as a variable in the regression . The region of env that contains the RRE is under strong nucleotide-level constraint [85–87 , Fig 4] , so we also included being in the RRE as a binary variable in the regression . We defined the variable loops as indicated in Fig 5 , and included being in one of these loops as a binary variable in the regression . Finally , we used crystal structures to delineate broadly neutralizing antibody epitopes . We focused on broadly neutralizing antibodies targeting the CD4 binding site , since most other broadly neutralizing antibodies target either glycans ( which are subject to pressures that are not well-modeled in our experiments; Fig 8A ) or a membrane-proximal region of gp41 that is not fully resolved in crystal structures of trimeric Env making it impossible to correct for RSA . Specifically , we analyzed the three antibodies with the greatest breadth from [116]: VRC01 ( PDB 3NGB [117] ) , 12A21 ( PDB 4JPW [118] ) , and 3BNC117 ( PDB 4JPV [118] ) . We defined a site as part of an epitope if it was within a 4Å inter-atomic distance of the antibody , and included the number of epitopes in which a site is found as a discrete variable in the regression . The results of the multiple linear regression are in Table 3 . As expected , increased solvent accessibility is strongly associated with increased mutational tolerance , whereas presence in the RRE is strongly associated with decreased mutational tolerance . After correcting for these effects , sites in broadly neutralizing epitopes have significantly reduced mutational tolerance . In contrast , sites in the variable loops have higher mutational tolerance , but this effect is not statistically significant . Some of the loops are more variable in nature than others [119] . However , even when the loops are considered independently , none of these regions has a statistically significant association with mutational tolerance ( S3 Table ) . Overall , this analysis provides statistical confirmation of something that is widely assumed in the study of HIV: broadly neutralizing antibodies are unique because they target regions of Env that are inherently intolerant of mutations . However , we fail to find strong statistical support for the hypothesis that variable loops are especially tolerant of mutations . Thus , the rapid evolution of these loops in nature is probably more attributable to strong immune selection than exceptionally high inherent mutational tolerance .
We have used deep mutational scanning to experimentally estimate the effects of all amino-acid mutations to most of HIV Env . Our experiments select for Env variants that enable HIV to undergo multi-cycle replication in a T-cell line . The broad trends in our data are consistent with what is expected from general considerations of how gene sequence maps to protein function: stop codons are efficiently purged by selection , many but not all nonsynonymous mutations are selected against , and synonymous mutations are less affected by selection except at regions where the nucleotide sequence itself is known to be biologically important . We also find a few sites where nonsynonymous mutations are strongly favored by selection in our experiments , probably because they adapt the virus to cell culture by affecting Env’s conformational dynamics , co-receptor binding , and glycosylation . We use our experimental data to estimate the preference of each site in Env for each amino acid . We show that these preferences correlate with amino-acid frequencies in natural HIV sequences nearly as well as would be expected if the experimentally measured preferences capture the true selection on Env in nature . The strongest deviations between our measurements and amino-acid frequencies in HIV sequences occur at sites on the surface of the virus that in nature are targeted by pressures ( such as antibodies ) that are not present in our experiments . The ability to identify deviations between our measurements and amino-acid frequencies in nature points to a powerful aspect of our approach: it can de-convolve the role of inherent functional constraints and external selection pressures in shaping Env’s evolution . For instance , it is known that some regions of Env are conserved in nature and thus are susceptible to broadly neutralizing antibodies . But other regions of Env such as the variable loops exhibit extensive variability and are generally targeted by more strain-specific antibodies . To what extent are these patterns of conservation shaped by Env’s inherent capacity to evolve versus the fact that immune selection tends to target the variable loops more readily than the broadly neutralizing antibody epitopes ? By measuring Env’s mutational tolerance at each site under functional selection alone , we show that the epitopes of broadly neutralizing antibodies indeed have a reduced capacity to tolerate mutations irrespective of the action of immune selection . However , we do not find strong statistical support for the hypothesis that the variable loops are especially tolerant of mutations compared to the rest of the protein . Thus , the rapid evolution of these loops probably results more from strong immune selection than exceptionally high inherent mutational tolerance . In the future , our measurements could also be used to examine the role of Env’s mutational tolerance in shaping the evolution of epitopes targeted by cellular immunity [120] . More generally , our experiments provide high-throughput experimental data that can augment computational efforts to infer features of HIV’s fitness landscape [18–20 , 22 , 121] . Such data will aid in efforts to understand viral evolutionary dynamics both within and between patients . Our study examined the replication of the CXCR4-tropic LAI strain isolated from a chronically infected individual , and used a T-cell line that expresses high levels of receptor relative to many primary cells [122 , 123] . This experimental setting is obviously a simplified representation of the actual environment in which HIV replicates . However , we anticipate that our approach could be extended to examine the effects of Env mutations in more complex experimental settings that may better mimic the selection on viruses in humans . For instance , comparing our measurements to those made on transmitted-founder viruses should help elucidate how selective constraints differ among HIV strains . Examining viral replication in cells with different receptor and co-receptor distributions should make it possible to isolate the role of cell-type specific selection in shaping HIV evolution [124 , 125] . Adding factors such as antibodies should enable the comprehensive identification of how mutations affect HIV immune escape . Such experiments will augment the results described here with maps of how mutational effects shift under various biologically relevant scenarios , thereby further enhancing our ability to understand the internal and external forces driving HIV evolution .
The computer code to analyze the sequencing data and generate the figures is provided in a series of IPython notebooks in S3 File . Illumina sequencing data are available from the Sequence Read Archive ( http://www . ncbi . nlm . nih . gov/sra ) under the accession numbers in S10 File . We use the HXB2 numbering system [50] unless otherwise noted . The “variable loop” definitions were taken from http://www . hiv . lanl . gov/ , not including the flanking disulfide-bonded cysteines as part of the loops . We created the codon mutant libraries in the context of the pro-viral genomic plasmid pLAI , which encodes the LAI strain of HIV [46] . This plasmid was obtained from the lab of Michael Emerman . The plasmid sequence is in S4 File . We created codon mutant libraries of env using the PCR mutagenesis technique described in [31] ( see also [33 , 38] ) except that we performed two total rounds of mutagenesis rather than the three rounds in [31] . The codon tiling mutagenic primers are in S5 File . The end primers were: 5’-ttggaatttctggcccagaccgtctcatgagagtgaaggagaaatatcagcacttg-3’ and 5’-catctgctgctggctcagc-3’ . We created three replicate libraries by performing all the steps independently for each replicate starting with independent plasmid preps . We cloned the PCR mutagenized env amplicons into the LAI plasmid with high efficiency to create plasmid mutant libraries . To seamlessly clone the PCR products into the proviral plasmid , we created a recipient version of the plasmid that had env replaced by GFP flanked by restriction sites for BsmBI , which cleaves outside its recognition sequence . We named this recipient plasmid pLAI-δ env-BsmBI; its sequence is in S6 File . We digested both this recipient plasmid and the gel-purified PCR amplicons with BsmBI ( there are BsmBI sites at either end of the PCR amplicon ) , gel purified the digested PCR products , and ligated them into the plasmid using a T4 DNA ligase . We column purified the ligation products , electroporated them into competent cells ( Invitrogen , 12033-015 ) , and plated the transformed cells on LB plates supplemented with 100 μg/mL ampicillin . For each of the three replicate libraries , we performed enough transformations to yield >1 . 4 million unique colonies as estimated by plating dilutions of each transformation on separate plates . Control ligations lacking an insert yielded at least 10-fold fewer colonies . The transformed cells were scraped from the plates , grown in liquid LB-ampicillin at 37°C for ∼4 hours , and mini-prepped to obtain the plasmid mutant libraries . For the wildtype controls , we prepped three independent cultures of the wildtype LAI proviral plasmid . We generated the mutant virus libraries by transfecting the mutant plasmid libraries into 293T cells obtained from the American Type Culture Collection ( ATCC ) . For each replicate , we transfected two 12-well tissue-culture plates to increase the diversity of the generated viruses . Specifically , we plated 293T cells at 2 . 4×105 cells/well in D10 media ( DMEM supplemented with 10% FBS , 1% 200 mM L-glutamine , and 1% of a solution of 10 , 000 units/mL penicillin and 10 , 000 μg/mL streptomycin ) . The next day , we transfected each well with 1 μg plasmid using BioT ( Bioland Scientific LLC , B01-01 ) . For the three wildtype controls we used the same process but with only a single 12-well plate per replicate . At one day post-transfection , we aspirated the old media , replacing it with fresh D10 . At ∼60 hours post-transfection , we filtered the transfection supernatants through 0 . 4 μm filters . To remove residual plasmid DNA from the transfection , we then treated the filtrate with DNase-I ( Roche , 4716728001 ) at a final concentration of 100 U/mL in the presence of 10 mM magnesium chloride ( Sigma , M8266 ) at 37°C for 20–30 minutes . We froze aliquots of the DNase-treated supernatant at -80°C . Aliquots were thawed and titered by TZM-bl and TCID-50 assays as described below . We passaged the transfection supernatants in SupT1 cells obtained from the NIH AIDS Reagent Program [126] . SupT1 cells were maintained in a media identical to the D10 described above except that the DMEM was replaced with RPMI-1640 ( GE Healthcare Life Sciences , SH30255 . 01 ) . Before infecting cells , for replicates 1 , 2 , and 3 ( but not replicate 3b ) , we first filtered thawed transfection supernatants through a 0 . 2 μm filter in an effort to remove any large viral aggregates . We then infected 108 SupT1 cells with 5 × 105 TZM-bl units of the mutant library transfection supernatant in a final volume of 100 mL SupT1 culture medium in a vented tissue-culture flask ( Fisher Scientific , 14-826-80 ) . In parallel , we passaged 105 TZM-bl units of transfection supernatant for each wildtype control in 20 million SupT1 cells in a final volume of 20 mL . At one day post-infection , we pelleted cells at 300×g for 4 minutes and resuspended in fresh media to the same volume as before . At two days post-infection , we added fresh media equal to the volume already in the flask to dilute the cells and provide fresh media . We harvested virus at three days post-infection ( for replicates 1 , 2 , and 3 ) or four days post-infection ( for replicate 3b ) by pelleting cell debri at 300×g for 4 minutes and then collecting the viral supernatant for storage at -80°C . To remove residual culture media and plasmid DNA from the cell pellets , we washed pellets two times in PBS . The washed cells were resuspended in PBS to a final concentration of 107 cells/mL , and aliquots were frozen at -80°C for DNA purification . We conducted a second passage by infecting new cells with the passage-1 viral supernatants . The second passage differed from the first passage in the following ways: Before infecting cells , we filtered passage-1 supernatant of replicate 3b-2 through a 0 . 2 μm filter but did not filter any of the other replicates . We also had to modify the passaging conditions for some replicates due to low titers of the passage-1 supernatants . For viruses in which the passage-1 supernatant was at too low a concentration to infect at an MOI of 0 . 005 in the volumes indicated above , we added additional passage-1 supernatant , and then reduced the volume to that indicated above during the day-one media change . As stated in the Results section , passaging more than 5 × 105 TZM-bl units of the mutant library at each step would probably help increase reproducibility between experimental replicates . We measured viral titers using TZM-bl reporter cells obtained from the NIH AIDS Reagent Program [127] . Specifically , we added 2×104 cells in 0 . 5 mL D10 to each well of a 48-well plate . We made dilutions of viral inoculum and infected cells with 100 uL of each dilution . At 2 days post-infection , we fixed cells in a solution of 1% formaldehyde and 0 . 2% glutaraldehyde in PBS for 5 minutes at room temperature , washed with PBS to remove the fixing solution , and stained for beta-galactosidase activity with a solution of 4 mM potassium ferrocyanide , 4 mM potassium ferricyanide , and 0 . 4 mg/mL X-gal in PBS at 37°C for 50 minutes . After washing cells with PBS to remove the staining solution , we used a microscope to count the number of blue cells per well , computing the viral titer as the number of blue cells per mL of viral inoculum . We were concerned that the infectious titer in SupT1 cells might differ from the TZM-bl titers . We therefore also performed TCID50 assay to directly measure infectious titers in SupT1 cells . To do this , we made dilutions of viral transfection supernatant in a 96-well tissue-culture plate and added SupT1 cells at a final concentration of 2 . 5×105 cells/mL in a final volume of 180 μL/well . At 4 and 8 days post-infection , we passaged supernatant 1:10 into fresh media to prevent cells from becoming over confluent . At 12 days post-infection , we measured the titer of culture supernatants using the TZM-bl assay to determine which SupT1 infections had led to the production of virus . Based on binary scoring from these TZM-bl assays , we calculated titers using the Reed-Muench formula [128] as implemented at https://github . com/jbloomlab/reedmuenchcalculator . At least for the LAI strain used in our experiments , the SupT1 TCID50 titers were approximately equal to the TZM-bl titers . Therefore , we used only the less time-consuming TZM-bl assay for all subsequent titering . We purified non-integrated viral DNA from aliquots of frozen SupT1 cells using a mini-prep kit ( Qiagen , 27104 ) with ∼107 cells per prep . In some cases , we then concentrated the purified DNA using Agencourt AMPure XP beads ( Beckman Coulter , A63880 ) using a bead-to-sample ratio of 1 . 0 and eluting with half of the starting sample volume . We next generated PCR amplicons of env to use as templates for Illumina sequencing . We created these amplicons from plasmid or mini-prepped non-integrated viral DNA by PCR using the primers 5’-agcgacgaagacctcctcaag-3’ and 5’-acagcactattctttagttcctgactcc-3’ . PCRs were performed in 20 μl or 50 μl volumes using KOD Hot Start Master Mix ( 71842 , EMD Millipore ) with 0 . 3 μM of each primer and 3 ng/μl of mini-prepped DNA or 0 . 3 ng/μl of plasmid as template . The PCR program was: For replicate 3b , there were a few modifications: the annealing temperature was 64 . 9°C , the extension time was 54 seconds , and we performed only 25 cycles . To quantify the number of unique template molecules amplified in each PCR , we performed standard curves using known amounts of template env in pro-viral plasmid , and ran the the bands on an agarose gel alongside our amplicons for visual quantification . We performed a sufficient number of PCR reactions to ensure that amplicons from plasmid were coming from > 106 unique template molecules , and amplicons from viral DNA were coming from ∼2 × 105 template molecules . All PCR products were purified with Agencourt beads ( using a sample-to-bead ratio of 1 . 0 ) and quantified by Quant-iT PicoGreen dsDNA Assay Kit ( Life Technologies , P7589 ) . We deep sequenced these amplicons using the strategy for barcoded-subamplicon sequencing in [38] , dividing env into six subamplicons ( this is a variation of the strategy originally described in [58–60] ) . The sequences of the primers used in the two rounds of PCR are in S9 File . Our first-round PCR conditions slightly differed from [38]: our 25 μL PCRs contained 12 . 5 μL KOD Hot Start Master Mix , 0 . 3 μM of each primer , and 5 ng of purified amplicon . For replicates 1 , 2 , and 3 , the first-round PCR program was: For replicate 3b , we used the same program , but with 9 PCR cycles instead of 11 . Prior to the second round PCR , we bottlenecked each subamplicon by diluting it to a concentration that should have yielded between 3 and 5×105 unique single-stranded molecules per subamplicon per sample . We purified the second-round PCR products using Agencourt beads , quantified with PicoGreen , pooled in equimolar amounts , and purified by agarose gel electrophoresis , excising DNA corresponding to the expected ∼500 base pairs in length . We sequenced the purified DNA using multiple runs of an Illumina MiSeq with 2×275 bp paired-end reads . We used dms_tools ( http://jbloomlab . github . io/dms_tools/ ) , version 1 . 1 . dev13 , to filter and align the deep-sequencing reads , count the number of times each codon mutation was observed both before and after selection , and infer Env’s site-specific amino-acid preferences using the algorithm described in [88] . The code that performs this analysis is in S3 File . Figures summarizing the results of the deep sequencing are also in this supplementary file . We downloaded the 2014 filtered web alignment of env from http://www . hiv . lanl . gov/ , including all subtypes for HIV-1/SIVcpz . We then curated this alignment in the following ways . First , we removed sequences differed in length from HXB2 ( including gap characters ) or contained a premature stop codon , ambiguous residue , or frame-shift mutation . Next , we removed columns in the alignment for which we lacked deep mutational scanning data , columns that had >5% gap characters , or columns in variable loops that appeared poorly aligned by eye . Finally , we randomly selected 30 sequences per subtype for group-M subtypes A , B , C , D , F , and G , for a total of 180 sequences . The resulting alignment is in S7 File . The phylogenetic tree in Fig 7 was inferred using RAxML [129] with the GTRCAT substitution model . We computed absolute solvent accessibilities based on the PDB structure 4TVP ( including all three Env monomers after removing antibody chains ) using DSSP [130 , 131] . We normalized absolute solvent accessibilities to relative ones using the maximum accessibilities provided in the first table of [132] . The relative solvent accessibilities are listed in S8 File .
|
HIV is infamous for the rapid evolution of its surface protein , Env . The ability to measure the effects of all mutations to Env under defined selection pressures in the lab would open the door to better understanding the factors that shape this evolution . However , this is a daunting experimental task since there are over 104 different single-amino acid mutations to Env . Here we leverage next-generation sequencing to perform a single massively parallel experiment that estimates the effects of all these mutations on viral replication in cell culture . Our measurements are largely consistent with existing knowledge about the effects of mutations at functionally important sites , and show that inherent mutational tolerance varies widely across Env . Our work provides new insight into Env’s evolution , and describes a powerful experimental approach for measuring the effects of mutations on HIV phenotypes that can be selected for in the lab .
|
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2016
|
Experimental Estimation of the Effects of All Amino-Acid Mutations to HIV’s Envelope Protein on Viral Replication in Cell Culture
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The presence of aspartic protease inhibitor in filarial parasite Brugia malayi ( Bm-Aspin ) makes it interesting to study because of the fact that the filarial parasite never encounters the host digestive system . Here , the aspartic protease inhibition kinetics of Bm-Aspin and its NMR structural characteristics have been investigated . The overall aim of this study is to explain the inhibition and binding properties of Bm-Aspin from its structural point of view . UV-spectroscopy and multi-dimensional NMR are the experiments that have been performed to understand the kinetic and structural properties of Bm-Aspin respectively . The human aspartic proteases that are considered for this study are pepsin , renin , cathepsin-E and cathepsin-D . The results of this analysis performed with the specific substrate [Phe-Ala-Ala-Phe ( 4-NO2 ) -Phe-Val-Leu ( 4-pyridylmethyl ) ester] against aspartic proteases suggest that Bm-Aspin inhibits the activities of all four human aspartic proteases . The kinetics studies indicate that Bm-Aspin follows a competitive mode of inhibition for pepsin and cathepsin-E , non-competitive for renin and mixed mode for cathepsin-D . The triple resonance NMR experiments on Bm-Aspin suggested the feasibility of carrying out NMR studies to obtain its solution structure . The NMR titration studies on the interactions of Bm-Aspin with the proteases indicate that it undergoes fast-exchange phenomena among themselves . In addition to this , the chemical shift perturbations for some of the residues of Bm-Aspin observed from 15N-HSQC spectra upon the addition of saturated amounts of aspartic proteases suggest the binding between Bm-Aspin and human aspartic proteases . They also provide information on the variations in the intensities and mode of binding between the proteases duly corroborating with the results from the protease inhibition assay method .
Lymphatic filariasis is a mosquito borne infection caused by Wuchereria bancrofti , Brugia malayi or Brugia timori that affects 120 million people in 73 countries and another 1100 million people are at the risk of contracting this dreadful disease [1] , [2] . Infection is initiated when infective mosquito bite the susceptible humans living in the endemic areas . Because of the seriousness associated with this infection , lymphatic filariasis is often considered as the second leading cause of permanent and long-term disability [3] . Though the mass drug administration was initiated as a preventive measure , it had only a limited ability [4] , [5] . In addition , the increase in drug resistance has also been observed to most of the drugs in mass drug administration [6] , [7] . Since yearly administration of these drugs is required in effective control of infection , there is a risk of raise in resistance against these drugs in parasites . Therefore , there is an immediate need for a multi-thronged approach in controlling this mosquito borne parasitic infection . Combining the structural characterization of the filarial proteins along with the identification of candidate antigens would be an ideal strategy in controlling this infection , especially to achieve the targeted elimination date of 2020 , by the Global Programme for Elimination of Lymphatic Filariasis [8] . During the process of infection , all stages of the parasite are constantly exposed to various human proteases . It is interesting to understand how filarial parasites successfully evade or counteract the harmful effects produced by the various human proteases . Under this scenario , several lines of studies suggest that filarial parasites have evolved mechanism to neutralize the harmful effects produced by the human proteases . For example , filarial parasites produce three types of classical protease inhibitors viz . , serine protease inhibitors ( serpins ) , cysteine protease inhibitors ( cystatins ) and aspartic protease inhibitors ( aspins ) to overcome the harmful effects produced by the human proteases . The first evidence of protease inhibitors in parasite survival was Taeniaestatin from a non-filarial parasite Taenia taeniaformis [9] . Later on , hundreds of protease inhibitors of parasite origin have been identified which possess potential applications in medicine , agriculture and biotechnology [10] . However , the role filarial protease inhibitors was understood only when it was reported that serpins [11] and cystatins [12] were few of the most highly secreted proteins in Brugia malayi . The suggested function of serpins and cystatins was to contribute to the longevity of the parasite in the blood stream . Despite the pivotal role of Aspins in host pathogenesis , immune regulation and diagnostic marker [13]–[15] , no further characterization has been reported on it . In this context , an Aspin from Brugia malayi , termed as pepsin inhibitor homolog compared to the amino acid composition of PI-3 from Ascaris Suum [16] , has been characterized as an immunodominant and hypodermal antigen [17]–[18] . In addition to this , we have recently reported the pepsin inhibition activity and physicochemical characterization of Bm-Aspin [19]–[20] . Interestingly , this inhibitor has been reported to inhibit the important human aspartic proteases such as pepsin , renin , cathepsin-E and cathepsin-D . It is worth noting that these aspartic proteases are widely distributed in tissues and are thought to be involved in the regulation of physical activities like , lysosomal biogenesis , protein targeting , antigen processing and presentation by degradation of proteins and peptides [21]–[25] . As these aspartic proteases are believed to play a key role in various physiological activities , understanding the aspartic protease inhibitory mechanism of Bm-Aspin becomes imperative to study its role in parasite survival and its immune evasion strategies . However , lack of Bm-Aspin three dimensional structure limit the ability of understanding aspartic protease inhibition mechanism . In order to unravel the structure-based protease inhibition mechanism , Bm-Aspin's , aspartic protease inhibition kinetics and solution structure determination has been initiated and presented in this study . This work happens to be the first report describing the feasibility of solution structure determination of filarial Aspin and understand its protease inhibitory efficiency from structural biology point of view .
The aspartic proteases pepsin , renin , cathepsin-E , and cathepsin-D purchased from Sigma , USA and the lab purified Bm-Aspin were used for the assays . Equimolar concentrations of Bm-Aspin and the proteases were mixed and incubated in 100 mM sodium acetate buffer at pH 5 . 6 for 10 min at 37°C separately . Bm-Aspin initially termed as “pepsin inhibitor homologue” [16] based on the sequence homology with Ascaris Suum PI-3 and was believed to follow similar kind of pepsin inhibition . Hence pH 5 . 6 was considered . The sample conditions and the methods that were chosen to study the protease inhibition by Bm-Aspin were similar to those used by Abu-erreish and Peanasky to study the pepsin inhibition by PI-3 from Ascaris Suum [26] . To measure the residual protease activity , 10 µl of the Phe-Ala-Ala-Phe ( 4-NO2 ) -Phe-Val-Leu ( 4-pyridylmethyl ) ester , the specific substrate for aspartic proteases from a stock of 1 mg/ml , was added [27] . The rate of cleavage of proteases was measured spectrometrically at 310 nm . After incubation at 37°C for 10 min , the mixture was centrifuged for 40 min at 17 , 091 g . The peptide fragments in the supernatant show absorption at 310 nm . The absorption is proportional to the digestion of substrate by the proteases . The whole experiment was repeated six times and the mean values obtained are graphically represented . The kinetics of aspartic protease inhibition by Bm-Aspin was determined by the UV- spectroscopic method [28] . Using a fixed quantity of the human proteases ( 5 mM ) and fixed reaction time , the rate of proteolysis in the presence of inhibitor was measured . Human aspartic proteases and the proteases pre incubated with increasing concentration of Bm-Aspin ( 1 mM , 2 . 5 mM and 5 mM respectively ) were taken in 100 mM sodium acetate buffer ( pH 5 . 6 ) for 10 min at 37°C . To measure the residual protease activity , 10 µl of the same substrate from the stock for each aspartic proteases was added . After incubation as before and after the centrifugation , the reaction was monitored by spectrophotometer at 310 nm . The Km value for Bm-Aspin was determined by linear regression method from plots of 1/V vs . 1/S , utilizing substrate concentrations of 7–80 µM . Three fixed concentrations of Bm-Aspin as explained above were used to determine the inhibition constant ( Ki ) against six concentrations of substrate . Assays were carried out in triplicates and the kinetic constants for Bm-Aspin against the human aspartic proteases were determined using Graphpad Prism 2 . 0 ( San Diego , CA ) . In order to show the activity of the protease and its inhibition by Bm-Aspin under NMR conditions , the effects of SDS and pH on the protease were studied . We have performed protease activity assays using Casein Agar plate and also by the UV spectroscopic method . The inhibition by Bm-Aspin has also been looked at these conditions . Fine punch holes were made on Casein agar plate as described by Cheseseman [29] . Five microgram of pepsin and pepsin preincubated with 100 mM SDS for10 min at 37°C was added separately in two different wells . Approximately equimolar Bm-Aspin was added to pepsin and to pepsin-SDS . After the incubation for 10 min at 37°C , these samples were loaded separately into other two different wells . Reaction buffer at pH 5 . 6 , water , 5 mM pepstatin treated pepsin and 5 mM pepstatin treated pepsin-SDS were placed respectively into three different wells as controls . Plates were incubated overnight at room temperature to examine the pepsin activity . To measure the pepsin activity at pH 7 . 0 , similar experimental setup described above was used except that pepsin was dissolved in phosphate buffer at pH 7 . 0 . In UV spectroscopy method , equimolar mixtures of Bm-Aspin with pepsin and pepsin-SDS were incubated separately for 10 min in sodium acetate buffer pH 5 . 6 and phosphate buffer pH 7 . 0 at 37°C . To measure the protease activity , the method described above explaining the human aspartic protease inhibition assay was followed . The whole experiment was repeated three times and the mean values obtained are graphically represented The plasmid containing Bm-Aspin was expressed in 15N minimal medium using 15NH4Cl as the sole nitrogen source . The purification and the refolding protocols are the same that have been described earlier [19] . Each sample for NMR measurement was concentrated to 0 . 3 mM using an Amicon ( MWCO = 10 kDa ) ultrafiltration cartridge . The final NMR sample was in the solvent containing 25 mM phosphate buffer ( pH 7 . 0 ) , 100 mM NaCl , 1 mM DTT , 100 mM Sodium Dodecyl Sulfate ( SDS ) and 10% D2O ( v/v ) . This NMR study was initially performed without any detergents and the resultant spectrum suggests the possibility of aggregation . Then the following chemicals/detergents were used to figure out the best one that helps to avoid aggregation: 0 . 5 M urea and 1% glycerol , 100 mM n-Dodecyl β-D-Maltopyranoside ( DDM ) , 1% n-octyl-β-D-glucoside ( OG ) , 1% triton-×100 and 100 mM SDS . The final goal is to obtain the most suitable condition for Bm-Aspin that could provide good quality NMR spectra . There after the protein sample for the NMR experiment was refolded and concentrated in the presence of the above mentioned detergents each time , respectively . 15N HSQC spectrum was acquired for Bm-Aspin at 298 K . Data was collected on the Bruker Avance III 700 MHz spectrometer available at Claflin University . The NMR experiments were recorded with the carrier position of 4 . 678 ppm for 1H and 117 ppm for 15N . All chemical shifts were referenced to internal D2O . The triple resonance NMR experiments like HNCO , HN ( CO ) CA , HNCA , CBCA ( CO ) NH and HNCACB on a uniformly 13C , 15N labeled Bm-Aspin were carried out as mentioned above . The data acquired were processed using NMRPipe [30] and analyzed using Sparky [31] . For understanding the interactions between Bm-Aspin and human aspartic proteases , a series of 15N-HSQC spectra were collected by titrating with progressive additions of human aspartic proteases ( pepsin , renin , cathepsin-E and cathepsin-D ) to 15N-labeled Bm-Aspin to attain molar ratios of 0∶1 , 0 . 1∶1 , 0 . 5∶1 and 1∶1 respectively .
Inhibition effect of Bm-Aspin on the human aspartic proteases was examined ( Figure 1A ) . When assayed for the inhibition of proteases by Bm-Aspin using the specific aspartic protease substrate , Phe-Ala-Ala-Phe ( 4-NO2 ) -Phe-Val-Leu ( 4-pyridylmethyl ) ester , successful inhibitions of all four proteases were observed . Protease digestion was observed in Casein agar plate by 5 µg of pepsin and 5 µg of pepsin preincubated ( for 10 min at 37°C ) with 100 mM SDS at both pH 5 . 6 and 7 . 0 ( Figure 1 B ) . Encouragingly , no zone of digestion was observed when Bm-Aspin was added to pepsin and pepsin-SDS . In addition to this , pepstatin seems to inhibit both pepsin and pepsin-SDS at both pH 5 . 6 and 7 . 0 respectively . The results obtained from the Casein Agar plate method clearly indicate that the protease activity is affected by 9% by the presence of 100 mM SDS and 19% by the raise in the pH to 7 . 0 . UV spectroscopy based analysis also revealed protease activity of pepsin at both the pH 5 . 6 and 7 . 0 ( Figure 1 C ) in the presence of SDS . The results of the UV spectroscopic method indicated that no effect on the protease activity by the presence of SDS and that the pH to 7 . 0 produced a 10% reduction in the activity . Bm-Aspin was found to inhibit the protease under both the conditions that were used . As expected , pepsin activity was completely inhibited by Bm-Aspin in the presence of SDS both at pH 5 . 6 and 7 . 0 . From these results , it is clearly evident that pepsin is active at pH 7 . 0 and in 100 mM SDS . The behavior of the other aspartic proteases used in the study under the above mentioned conditions was believed to be similar . Hence , we felt that it's worth proceeding with the NMR titrations under these conditions to investigate the binding of Bm-Aspin with human aspartic proteases . The double inverse Lineweaver-Burk plots depicted the inhibition of pepsin , renin , cathepsin-E and cathepsin-D by Bm-Aspin ( Figure 2A–D respectively ) . The rate of the cleavage of the specific substrate was found to be decreased with increasing Bm-Aspin concentration . Inhibition constant ( Ki ) for pepsin , renin , cathepsin-E and cathepsin-D inhibition by Bm-Aspin was found to be: 2 . 1 ( ±0 . 7 ) nM , 2 . 9 ( ±0 . 9 ) nM , 4 . 3 ( ±0 . 2 ) nM , and 6 . 5 ( ±0 . 6 ) nM respectively . The data clearly indicate the strength of inhibition among the four different proteases with Bm-Aspin . The NMR measurements carried out on Bm-Aspin devoid of any detergents resulted in poorly resolved spectrum indicating an aggregated state of the protein ( Figure 3 ( i ) ) . In order to determine the sample conditions for NMR suitability , Bm-Aspin was refolded and screened in the presence of the following five detergents , such as 0 . 5 M urea , 1% OG , 100 mM DDM , 1% triton X-100 and 100 mM SDS respectively ( Figure 3 ( ii–vi ) ) . The HSQC spectrum is an important qualitative and quantitative tool to validate the uniform folding and structural homogeneity of a purified protein sample . The quality and the number of peaks present in the HSQC spectrum reveal the monomeric or oligomeric nature of the protein . This information is vital to assess the feasibility of further solution NMR based structural characterization . Among the five conditions screened , SDS produced a well resolved spectrum with the dispersion and narrow line widths indicating a well folded non aggregated protein ( Figure 3 ( vi ) ) . The optimum concentration of SDS in the buffer was determined by investigating the effect of SDS on Bm-Aspin by NMR ( Figure 4 ) . Our data indicated that the HSQC spectra closely resembled one another when SDS concentration was in the range of 50–100 mM ( Figure 4 ( i–ii ) ) . However , the spectra started to lose their quality at a concentration above 100 mM with missing peaks , indicating that the protein is either getting aggregated and/or partially denatured ( Figure 4 ( iii–iv ) . Taken together , 100 mM SDS was chosen as the working condition for further characterization ( Figure 4 ( ii ) . Though the presence of SDS yielded a quality spectrum , observation of lesser number of peaks than expected , that too , with overlap among them was the disappointing factors . The proper folding of the protein was evident when the HSQC peaks corresponding to all the 12 glycines that are evenly distributed throughout the sequence were visible and found well separated ( Inset of Fig . 4 ( ii ) ) . The overlap seen in the middle region of the HSQC spectrum is well resolved in the carbon dimension of the triple resonance spectra . Hence , we have continued to view the carbon dimension , by acquiring triple resonance NMR experiments . These experiments resolve the peak overlaps by separating them on its carbon dimension . The triple resonance experiments for the backbone assignment , like HNCO , HN ( CO ) CA , HNCA , CACB ( CO ) NH and HNCACB , were carried out at 298 K . The analysis of 3D HNCO spectra revealed the presence of nearly 95% of the peaks with relatively uniform intensity . The yields of the other triple resonance spectra are in the range of 80 to 90% . Further , well resolved resonances of the complete set of Asn and Gln 21 residue doublets were completely picked up from the triple resonance HNCO spectrum . This indicated that the NMR sample of Bm-Aspin is in a well folded state with a good structural stability and that it is feasible to carry out solution structural studies using NMR . By combining the pairs of 3D NMR spectra for the assignment of Cα and Cα/Cβ , respectively , the backbone assignment of Bm-Aspin is in progress and the complete assignment would soon be published elsewhere . To mention the progress of the backbone assignment process , a strip plot indicating the sequential Cα connectivity for the residue stretch T189 to V194 , using the two pairs of triple resonance NMR spectra , namely , HN ( CO ) CA , HNCA , CBCACONH and HNCACB have been shown in Figure 5 . To determine and analyze the interactions between the Bm-Aspin and the human aspartic proteases , the unlabeled proteases were titrated against the 15N labeled Bm-Aspin at four different molar ratios with the last being the saturated one at 1∶1 . For each of the titrated sample , the [1H 15N] HSQC spectrum was acquired . Aspartic protease interactions were followed by monitoring the changes in chemical shift positions in the fingerprint region of Bm-Aspin of the HSQC spectra . The spectra of the set of four titrated samples with a specific protease were overlaid on each other . The residues of Bm-Aspin that have been affected by the protease titrations are seen to be perturbed , shifted away gradually from the reference spectrum that are clearly visible from the superposed spectra . There are nearly 15 common residues that have been affected by the protease interactions , out of which the following residues ( G16 , G22 , G82 , G169 , G190 , A192 , A204 , A213 , I214 and Y215 ) possess appreciable peak movement indicating their involvement in the interaction phenomena . All the spectra are having the same trend in their protease interaction shifts with pepsin producing more appreciable shifts . The general inhibition characteristics that have been observed from the kinetic and inhibition assay methods are in perfect agreement with the NMR titration method , duly confirming the results . A visual look at the overlaid spectra clearly confirms the strong inhibitory characteristics of pepsin compared to the rest . There is strong evidence about the involvement of the C-terminal residues in the interaction process of all the proteases . Additionally , for pepsin , some glycines from the N-terminal domain are found to be affected as compared to the rest of the proteases , which could attribute to the more inhibitory nature of pepsin . From the binding studies of Bm-Aspin with the aspartic proteases , we have observed that there are five residues ( A192 , A204 , A213 , I214 and Y215 ) of the C-terminal domain that are commonly affected in all the four aspartic proteases interactions . This provides an information that the same kind of interaction mode is followed in all the four protease interactions . Three of the N-terminal domain residues ( G16 , G22 and G 82 ) are appreciably affected in interacting with Pepsin and Renin and they are smaller for CatD and CatE . These sites could be the aiding factor for the two former proteases to have higher activities compared to the latter two . These residue specificities for the proteases clearly indicate that they are the genuine interacting sites . As the HSQC spectrum has more peak overlaps , for simplicity , only the residues A213 , I214 , and Y215 that are having appreciable shifts when interacting with pepsin are shown here as an example . ( Figure 6 A–C ) . On comparing the aspartic protease interactions with Bm-Aspin at their saturated conditions , the saturated sample spectra for each of the four proteases were overlaid on each other . Again , the same three residues considered in the previous example have been shown here ( Figure 7A ( i–iii ) ) . The peak movements strongly support the earlier observations that Bm-Aspin inhibits pepsin more , followed by renin , cathepsin-E and cathepsin-D in the order of inhibition , respectively . In analyzing and comparing the chemical shift perturbations observed due to the Bm-Aspin interactions with all the four aspartic proteases , the radial shift displacement has been calculated for each of the affected residues , by combining both the chemical shifts of 1H and 15N , using the following equation:A scaling factor of 6 was used to normalize the differences in the 1H and 15N spectral widths . Hf , Hb , Nf , and Nb are the chemical shifts of each residue's amide 1H and 15N in the free ( Bm-Aspin alone ) and bound ( Bm-Aspin+protease complex ) states , respectively . The bar diagram has been drawn using the radial shifts data obtained for the earlier mentioned 10 residues , when each of the four proteases used and is as shown in Figure 7B . A thorough look of the bar diagram clearly indicates Bm-Aspins' strong binding affinity with pepsin . The C-terminal residues show stronger affinity compared to the N-terminal residues . The molecular modeled structure of Bm-Aspin ( not shown ) , predicted from SWISS-MODEL ( swissmodel . expasy . org ) , has two separate domains , the N-terminal and C-terminal . The present analysis supports the strong interaction of Bm-Aspin C-terminal domain with the proteases . It is this C-terminal domain that has an additional helical secondary structure compared with the crystal structure of Ascaris Suum inhibitor , PI-3 . Hence , it is quite obvious to expect the role of Bm-Aspin C-terminal domain in the inhibitory actions against the aspartic proteases .
Although proteinaceous aspins were proved to be essential and critical in inhibiting the aspartic proteases , very little structural information is known to explain the mode of action . The primary reason may be the difficulty of producing milligram quantities of aspins for structural characterization . Recombinant expression of aspins in E . coli , the primary machine for large-scale protein production for structural studies , has had very limited success . As a result , there are only two examples of recombinant expression of an Aspin from prokaryotic sources as of now , for which structural characterization has been reported [36] , [38] . We report here the isotopic labeled production of Bm-Aspin from the prokaryotic source which is feasible for structural characterization by solution NMR . This happens to be the first report on attempting solution NMR studies from a protein involved in human lymphatic filariasis . Many attempts to produce Bm-Aspin suitable for NMR studies without the use of any detergent became unsuccessful . The observation of poorly resolved spectra from each experiment suggested the aggregated state of the protein . From these results , we were convinced that a membrane mimetic environment is necessary during Bm-Aspin refolding and reconstitution . The above assumption can be confirmed based on the evidences from the earlier work suggesting that , Bm-Aspin is a surface protein secreted continuously from Brugia malayi at all stages of its life cycle [17] . From this evidence it was hypothesized that Bm-Aspin may contain at least one helix or several surface embedded residues . As explained above , to carry out the structural determination of Bm-Aspin by solution NMR , detergent screening to find the suitable membrane mimetic environment is an essential prerequisite . The choice of the detergent to be used will be determined by considering the protein solubility , stability and the quality of the 15N HSQC NMR spectrum . The HSQC spectrum correlates the amide proton and the corresponding nitrogen pair of each residue within a protein and provides a map of the finger print region . It also serves as a building block for multidimensional NMR experiments upon which the resonance assignments and the determination of the 3D structure of a protein rely . Thus , obtaining a well resolved HSQC spectrum is important for structural characterization by solution NMR . Five conditions were screened to obtain the suitable sample of Bm-Aspin to carry out solution NMR studies . These include the combination of urea and glycerol [39] , which has been successfully proved in some cases to prevent aggregation after refolding and OG , the common detergents for membrane protein crystallization [40] , SDS and triton X-100 , which are commonly used for solution NMR [41] , [42] . For most of the detergents that were used , the protein appeared to be in aggregated state leading to poorly resolved spectra with broader line widths . In contrast , SDS yielded a good quality HSQC spectrum with more number of well resolved peaks and limited number of peak overlaps in comparison to the rest of the detergents that were screened . In fact , SDS has served as one of the most popular membrane mimetic that has been widely used for integral membrane protein structure and function studies [43] , [44] . Glycines have a specific nitrogen chemical shift range and there are 12 Glycine residues that are distributed evenly throughout the sequence of Bm-Aspin . The presence of peaks around 106–110 ppm of the nitrogen chemical shift region in the HSQC spectrum have been assigned to all the glycines . This gives a clear indication that the protein is well folded and that it is suitable for the NMR solution structure determination and other structural studies . Though SDS yielded a good HSQC spectrum of Bm-Aspin , a severe overlap of the peaks in the proton chemical shift region around 8 ppm , was observed , raising questions on the difficulty to carry out solution NMR studies on Bm-Aspin . Often , this kind of a situation specifies to the helical secondary structure of the protein under study and assuming that Bm-Aspin falls into that category , triple resonance NMR experiments were performed on the doubly labeled Bm-Aspin . Thus in order to overcome the above difficulty , the following triple resonance NMR experiments , HNCO , HN ( CO ) CA , HNCA , CBCA ( CO ) NH and HNCACB were carried out at 298 K on a uniformly double 13C , 15N –labeled Bm-Aspin [45] . Protein sample was found to be stable throughout the data collection . From the analysis of the HNCO spectrum , 95% of the expected peaks with relatively uniform intensity were found . The severe overlap in the central region of the HSQC suggests a high helical content in Bm-Aspin . These findings are in agreement with the CD results that we published earlier suggesting the helical nature of Bm-Aspin [19] . Thus the three dimensional NMR experiments suggests the feasibility of conducting solution NMR-based structural studies on Bm-Aspin . The peak overlaps in the HSQC spectrum of Bm-Aspin with a large number of amide peaks make its resonance assignment extremely challenging . The backbone assignment of Bm-Aspin was initiated by establishing the neighboring residue connectivity using pairs of triple resonance spectra , HNCA and HNCOCA; HNCACB and CBCA ( CO ) NH . The sequential NMR spin system connectivities have been achieved by both intra- and inter-residue cross-peaks of Cβ and Cα respectively . Any ambiguities were resolved by going through the 15N edited NOESY spectra . The usage of CBCA ( CO ) NH/HNCACB was found to be extremely useful not only for the improved resolution in the carbon dimension , but more importantly , it provided the phase information which will be used in the assignment process . The initial assignment was simple with the easier identification of Glycine residues and its connectivity with the other easily identifiable Ala , Ser and Thr residues . Nearly 15 residue-stretches with sequential residue connectivity have been identified and assigned . The complete backbone and side-chain assignments for the other residues of Bm-Aspin is in progress and will soon be communicated elsewhere . Before performing the NMR titration analysis between Bm-Aspin and human aspartic proteases , activity of pepsin in the above mentioned conditions ( 100 mM SDS and pH 7 . 0 ) was carried out . From these studies , it was clearly demonstrated that 100 mM SDS has no effect on pepsin activity and on the inhibitory activity of Bm-Aspin . The observation of protease digestion by pepsin at both pH 5 . 6 and 7 . 0 indicate a higher activity for pH 5 . 6 , which is in agreement with earlier studies carried out on pH stability of pepsin [46] . In addition to this , 100 mM SDS seems to have negligible effect on pepsin activity . In fact , according to the literature , SDS was found to have least effect on the activity of many proteases . [47]–[51] . From these results , we felt that it's worth proceeding to study the aspartic protease binding property of Bm-Aspin using HSQC titrations . The preliminary residue assignment of Bm-Aspin has been useful in the analysis of the Bm-Aspin interactions with the proteases . Our results clearly demonstrate that Bm-Aspin interacts with human aspartic proteases supporting our earlier results . The chemical shift perturbations were observed in the HSQC experiments upon the addition of aspartic proteases to Bm-Aspin . These observations provide the direct experimental proof that the Bm-Aspin contains the binding sites for human aspartic proteases . As expected , based on the displacement observed from the chemical shift perturbations in the HSQC of Bm-Aspin upon the addition of aspartic proteases , the inhibition slows down in the order of pepsin>renin>cathepsin-E>cathepsin-D . Similar findings were observed previously , when Bm-Aspin inhibition kinetic studies were carried out using UV-spectroscopy , suggesting the authentication of the experiments performed and the results obtained . However the detailed mechanism of protease inhibition by Bm-Aspin and its role in parasite survival can only be addressed further by structure-function studies with the determination of the solution structure of Bm-Aspin which would be initiated on completion of the assignment process . We report here the aspartic protease inhibition kinetic studies and feasibility to conduct the solution NMR based structural studies on Bm-Aspin . Aspartic protease inhibition assay carried out using the specific substrate suggested that Bm-Aspin inhibits the important human aspartic proteases supporting our earlier published data . Inhibition kinetic studies suggest that , Bm-Aspin follows competitive mode of inhibition for pepsin and cathepsin-E , non-competitive for renin , and mixed for cathepsin-D . The triple resonance NMR experiments conducted on Bm-Aspin with SDS detergent suggested the feasibility to carry out the NMR based structural studies . The activities of pepsin at pH 7 . 0 and in the presence of 100 mM SDS suggests that all the titrations carried out are in the biologically relevant form . The chemical shift perturbations were observed in the 15N HSQC spectra of Bm-Aspin upon the addition of aspartic proteases , suggesting the possibilities of identifying the binding sites of human aspartic proteases on Bm-Aspin . This work happens to be the first attempt on the solution NMR studies of the protein involved in human lymphatic filariasis . Considering the physiological role played by these human aspartic proteases , understanding the protease inhibition mechanism of Bm-Aspin will be interesting to speculate Bm-Aspin , as an important drug target for human lymphatic filariasis . For which , the high resolution 3D structure determinations of Bm-Aspin in apo-form by solution NMR and the complex with human pepsin by X-ray crystallography methods are well underway in our laboratories . These high resolution structures will be instrumental in our understanding of the structure/function and mechanism of aspartic protease inhibition .
|
Filariasis is a parasitic infectious tropical disease caused by thread like filarial nematodes . These worms occupy the lymph nodes and in chronic cases they lead to the disease “elephantiasis . ” Over 120 million people have already been affected by it , and 40 million are seriously disfigured by this disease . These parasites in human , adopt numerous strategies to hamper the host immune system which can facilitate its survival . The ability of the parasite to modulate the host immune system is a concept which explains the versatility of human filarial parasites . One such interesting concept to understand is the secretion of protease inhibitors by filarial parasites . Recently , an Aspin from parasite Brugia malayi was identified and the recombinant protein was biochemically characterized . Aspins get fast gaining importance in the fields like medicine , agriculture and biotechnology . Hence , in this study , the inhibition ability of filarial Aspin against human aspartic proteases is attempted from structural biology point of view . This new knowledge may contribute to a better overall understanding of the mechanism that explains the versatility of human filarial parasite . Since filariasis is more often considered as the disease of poor countries , fight against filariasis is also a fight against poverty .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"biomacromolecule-ligand",
"interactions",
"biochemistry",
"protein",
"interactions",
"proteins",
"protein",
"structure",
"biology",
"chemical",
"biology",
"drug",
"discovery"
] |
2014
|
A Structural Biology Approach to Understand Human Lymphatic Filarial Infection
|
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